Antoine Mauri

Redouane Khemmar

Remi Boutteau

Benoit Decoux

Jean-Yves Ertaud

Madjid Haddad

A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods

In smart mobility based road navigation, object detection, depth estimation and tracking are very important tasks for improvement of the environment perception quality. In the recent years, a surge of deep-learning based depth estimation methods for monocular cameras has lead to significant progress in this field. In this paper, we propose an evaluation of state-of-the-art depth estimation algorithms based on single single input on both the KITTI dataset and the recently published NUScenes dataset. The models evaluated in this paper include an unsupervised method (Monodepth2) and a supervised method (BTS). Our work lies in the elaboration of novel depth evaluation protocols, object depth evaluation and distance ranges evaluation. We validated our new protocols on both KITTI and NUScenes datasets, allowing us to get a more comprehensive evaluation for depth estimation, especially for applications in scene understanding for both road and rail environment.

I. INTRODUCTION

Accurate depth estimation is necessary for the perception of the environment in front of the vehicle and can significantly increase safety by estimating the distance of pedestrians and vehicles. Technology can also improve the competitiveness of road transports, as shown in [START_REF] Mukojima | Moving camera backgroundsubtraction for obstacle detection on railway tracks[END_REF], [START_REF] Yanan | Rail surface defect detection method based on yolov3 deep learning networks[END_REF], but this field still has many important challenges before being completely operational. Distance measurement from objects can be based on many kind of sensors: ultrasonic, laser [START_REF] Palacín | Real-time tree-foliage surface estimation using a ground laser scanner[END_REF] or time-of-flight camera [START_REF] Kang | Harmonic distortion free distance estimation in tof camera[END_REF]. But those solutions are still costly. In this work, we investigate the use of camera(s) for this task. The most common method to estimate distance with cameras is to use a pair of stereoscopic cameras with matching algorithms. But recently, vision algorithms based on Convolutional Neural Networks (CNN) have shown stateof-the-art performance in depth estimation with a single camera. Such methods has the advantages to require a relatively low-cost and easy to integrate sensor. But those methods still lack in precise evaluation. In this context, several works related to environment perception have already been carried out, such as the tracking of a person [START_REF] Khemmar | Real time pedestrian and object detection and tracking-based deep learning. application to drone visual tracking[END_REF], the detection and tracking of objects for the road smart mobility [START_REF] Chen | Real time object detection, tracking, and distance and motion estimation based on deep learning: Application to smart mobility[END_REF] [START_REF] Mauri | Deep learning for realtime 3d multi-object detection, localisation, and tracking: Application to smart mobility[END_REF].

While the depth estimation algorithms tested in this paper offer comprehensive evaluation results, they only provide an evaluation of the overall performance of a method. It lacks information of how well objects have their distance predicted and what is the depth precision at longer ranges. These informations are vital for applications in road environment 1 Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.

2 SEGULA Engineering, 19 rue d'Arras, 92000 Nanterre, France.

especially for autonaumous driving. That is what motivated our contribution of an evaluation protocol better suited for road environments as well as a comparative evaluation using our new protocols of state-of-the-art methods on two large datasets on road environments: KITTI [START_REF] Geiger | Vision meets robotics: The kitti dataset[END_REF] and NuScene [START_REF] Caesar | nuscenes: A multimodal dataset for autonomous driving[END_REF]. The main contribution of our work is to offer a new evaluation protocol for single image depth estimation algorithms adapted to road and rail environment for autonomous vehicles as well as an evaluation of state-of-the-art methods.

The remainder of this paper is organized as follows: In section II we review the state-of-the-art algorithms which are evaluated in this paper, the datasets which are used, and present the new proposed protocols. In section III, we describe in more details the new class-specific metric which is proposed, the methodology of evaluation and some specificities of learning on the NuScene dataset. Experimental results are presented in section IV. Finally, conclusion and future directions are drawn in section V.

II. RELATED WORK A. Monocular Depth Estimation Methods

For learning purposes, if the datasets used contains the distance from object as ground-truth information, this information can be used to supervise learning of a neural network with a regression output layer. Most of the CNN models for depth estimation have an encoder-decoder structure, similar to the one that is used for the application of semantic segmentation of images [10] [11], in which the output of the network has the same size as the inputs. One of the main problems encountered by those models is to get full resolution at the output of the network, due to the bottleneck which exists at the junction between the encoder and decoder parts.

In Multi-Scale Local Planar Guidance model (called BTS for Big-To-Small in the following, the name given by the authors in their paper) [START_REF] Lee | From big to small: Multi-scale local planar guidance for monocular depth estimation[END_REF], layers located at multiple stages of the decoding phase are used and their outputs are combined to predict depth at full resolution. Another CNN in this category is DenseDepth [START_REF] Alhashim | High quality monocular depth estimation via transfer learning[END_REF], in which the encoder part is based on a pre-trained DenseNet [START_REF] Huang | Densely connected convolutional networks[END_REF]. The whole network is then trained with NYU Depth v2 [START_REF] Nathan Silberman | Indoor segmentation and support inference from rgbd images[END_REF] and KITTY datasets. A specific loss function penalizing high-frequency distorsion allows for more faithfull depth reconstruction at object boundaries.

Another way to get supervision information is to use aligned pairs of stereo images during the learning phase, and then infer depth maps on monocular images. This approach is called self-supervised, as those models don't need dataset with ground-truth (like LiDAR measurement or disparity maps) for learning. In Monodepth [START_REF] Godard | Unsupervised monocular depth estimation with left-right consistency[END_REF], the problem of depth estimation is casted into an image reconstruction one. Specifically, the CNN is given as input one of the images of the stereo pair, and gives as output an estimate of the disparities. The second image of the stereo pair is then synthesized from this estimate. The difference between the synthesized image and the real one is then used in the loss function [START_REF] Luo | Single view stereo matching[END_REF] [START_REF] Tosi | Learning monocular depth estimation infusing traditional stereo knowledge[END_REF]. In MonoResMatch model [START_REF] Tosi | Learning monocular depth estimation infusing traditional stereo knowledge[END_REF], authors also use stereo images with an end-to-end learning to get the depth information. They do not use ground truth present in the datasets, but use a stereo-matching algorithm (Semi-Global Matching) to internally generate this information. In Monodepth2 [START_REF] Godard | Digging into self-supervised monocular depth estimation[END_REF], authors present a set of improvements over the first version of their algorithm [START_REF] Godard | Unsupervised monocular depth estimation with left-right consistency[END_REF]. The model is based on three processes which collaborate together in order to improve depth estimation: the model can be trained with monocular data, stereo data, or both. Specifically, the 3 processes are made of: (1) a minimum reprojection loss calculated for each pixel, (2) an auto-masking loss to ignore confusing and (3) a full resolution multi-sampling method for reducing visual artifacts. The effectiveness of the model is demonstrated quantitatively and qualitatively on the KITTI benchmark.

Other methods use monocular images for both learning and inference. Specifically, sequences of images are used for learning, and at test time depth is estimated on single images. In [START_REF] Zhou | Unsupervised learning of depth and ego-motion from video[END_REF], a CNN with two modules sharing the first few convolutional layers, is used to jointly give estimates for depth and pose. In [START_REF] Casser | Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos[END_REF], geometric structure of objects and of the scene are introduced in the learning process, by using separate object motion estimators, ego-motion estimator and depth estimator, making the model well adapted to highly dynamic environments.

By using sequences of stereo images, it is also possible to learn depth and odometry at the same time, as both spatial and temporal photometric errors are available [START_REF] Zhan | Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction[END_REF].

B. Datasets for Depth Prediction in Outdoor Environment

In smart mobility based road and railway navigation, object detection, depth estimation and tracking are very important tasks for improvement of the environment perception quality. A deep learning method for object detection and depth estimation requires more and more training and evaluation datasets containing heterogeneous data like images, videos, ranges, etc. This is why it is very important to identify a good and high accuracy dataset for our real time object detection and depth estimation for road and rail applications.

In KITTI [START_REF] Geiger | Vision meets robotics: The kitti dataset[END_REF], the authors present one of the highly used dataset in road environment for mobile robotics and autonomous driving research. KITTI is a calibrated, synchoronized, and timestamped autonomous driving dataset which was captured with a wide range of scenarios. The KITTI dataset was collected by using a VW Station vehicle instrumented by different kinds of sensors such as: color and grayscale stereo cameras, a velodyne 3D laser scanner and a high precision GPS/IMU navigation system. The platform contains real-world traffic situations with both static and dynamic objects (object labels are presented in the form of 3D tracklets). The dataset provides online benchmarks for different tasks such as: stereo, optical flow, and object detection.

NuTonomy scenes (NUScenes [START_REF] Caesar | nuscenes: A multimodal dataset for autonomous driving[END_REF]) is a multimodal dataset for autonomous driving. NUScenes contains different types of sensor surch as: 6 cameras, 5 radars and 1 LiDAR. It is fully annotated and comprises 1000 scenes (20s long for each), 3D bounding boxes for 23 classes and 8 attributes. It has 100x as many images than the pioneering KITTI dataset. It also contains careful dataset analysis as well as baselines for LiDAR and image based detection and tracking [START_REF] Caesar | nuscenes: A multimodal dataset for autonomous driving[END_REF].

III. EVALUATION OF DEEP LEARNING-BASED MONOCULAR DEPTH ESTIMATION METHODS

A. Error Metrics used in Depth Evaluation

Before presenting our contributions for depth evaluation, we define below the depth error metrics that are used in the literature and in our work. Let p be the depth prediction of a pixel in the image, g its ground truth and N the total number of depth pixels in the image.

Relative Error: The equation for the Relative Error (RE) is detailed in Equation ( 1).

RE = 1 N i j |g i,j -p i,j | g i,j (1)
Squared Relative Error: The equation for the Squared Relative Error (SRE) is detailed in Equation (2).

SRE = 1 N i j |g i,j -p i,j | 2 g i,j (2)
Root Mean Squared Error: The Root Mean Squared Error (RMSE) details can be found in Equation [START_REF] Palacín | Real-time tree-foliage surface estimation using a ground laser scanner[END_REF].

RM SE = 1 N i j (g i,j -p i,j ) 2 (3)
Logarithmic Root Mean Squared Error: The equation for the Logarithmic Root Mean Squared Error (logRMSE) is detailed in Equation [START_REF] Kang | Harmonic distortion free distance estimation in tof camera[END_REF].

logRM SE = 1 N i j (log(g i,j ) -log(p i,j )) 2 (4)
Percentage of Bad Matching Pixels: The Percentage of Bad Matching Pixels (BMP) is detailed in Equation ( 5) where C is a threshold used for setting an error tolerance.

[a] k=[1..3] = 1 N i j max( g i,j p i,j , p i,j g i,j ) < C k (5)
These metrics give a comprehensive statistical assessment of the performance of a method but we believe it can be further developed. One of our contributions lies in novel depth evaluation protocols that can be found below.

B. Object Depth Evaluation

While the current evaluation of depth estimations gives a comprehensive assessment of the overall performance of a given method, it is done on the global image and does not evaluate the object distance prediction. Object distance is a vital aspect for applications in autonomous driving and scene perception, this is why we designed a new depth evaluation protocol that allows us to compute the depth prediction error for relevant objects that are regularly encountered in road environments (person, car, truck, etc). Our evaluation protocol consists in 4 steps: [START_REF] Mukojima | Moving camera backgroundsubtraction for obstacle detection on railway tracks[END_REF] The predicted depth map is scaled using median scaling; (2) Object masks are generated using Mask-RCNN [START_REF] He | Mask r-cnn[END_REF] (see Figure 1 for an example of network output); (3) The generated object masks are then used to segment the depth maps and the depth errors are computed for each mask in the image; (4) Finally the mean of the errors are computed for each class. This new evaluation protocol will allow a better understanding of how well a given method estimates the distance of objects present in road environment. It is especially useful for autonomous driving applications.

C. Depth Evaluation over Distance Ranges

The classic depth evaluation protocol also doesn't allow to evaluate a method's performance over longer ranges since the evaluation is done on the whole image. The performance of a method over longer ranges is an important parameter that needs to be taken into account for scene understanding purposes. Here we propose to follow the work of [START_REF] Koch | Evaluation of cnn-based single-image depth estimation methods[END_REF] where they described an evaluation protocol over distance ranges, but while they used this protocol for indoor scenes, we used it in a road environment where the distance ranges are more important. Our protocol is as followed: We scale the predicted depth map using median scaling. We then create the distance ranges of 10m up to a distance of 80m (i.e [0-10m], [10-20m], ..., [70-80m]). Each pixel is then assigned to a distance range according to the value of the depth ground truth. For each distance range, we compute the depth errors. This new protocol gives an assessment of how the depth estimation degrades over longer ranges.

IV. RESULTS ANALYSIS

In this section, we will present the results of our evaluation on both KITTI and NuScenes datasets of state-of-the-art supervised and self-supervised monocular depth estimation methods BTS and Monodepth2.

A. KITTI Dataset

For the evaluation on the KITTI dataset, we used the pretrained models provided by the authors both BTS and Monodepth2. The BTS model was trained with images from the Eigen training split [START_REF] Eigen | Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture[END_REF] at a resolution of 704x352 and with a dense groundtruth. The pretrained weights of Monodeth2 were trained using the monocular training on Zhou's training split [START_REF] Zhou | Unsupervised learning of depth and ego-motion from video[END_REF] at a resolution of 1024x320. The evaluation was performed on the eigen test split. The results of our evaluation of BTS and Monodepth2 can be found in Table I and II. The value of C for the Threshold error defined in Equation ( 5) has been set to 1.25.

B. NuScenes Dataset

For the evaluation on NuScenes, we had to train both methods on the training split of NuScenes. For BTS, we trained for 50 epoch with a batch size of 20 and a resolution of 192x192, we used the sparse data from the LiDAR supervision. Given that Monodetph2 is an unsupervised method, it relies on reprojection loss for the monocular training. If the training images have poor visibility, the training might not converge. That is why we selected the scenes from the training split where the visibility is good enough for the training to converge. We also used all the images from each scene and not just the images that were synced with the LiDAR in order to get a framerate high enough for the monocular training to work. We trained Monodepth2 for 20 epochs with a batch size of 12 and a training resolution of 446x224. The results of our evaluation of BTS and Monodepth2 can be found in Table III and IV. The value of C for the Threshold error defined in Equation ( 5) has been set to 1.25.

C. Experimental Results Analysis

Our results over the two datasets show that overall BTS yields better results than Monodepth2. Our evaluation over distance ranges also shows that both methods, as expected, tends to have a lower accuracy when the distance increases. Our object depth evaluation also shows that the depth estimation predictions errors for objects is significantly higher than the errors on the overall picture (see Figures 4 and5), this can be explained by the large variety for each object class which makes learning the depth harder for the CNNs whereas the surrounding environment is less variable making it easier for the methods to learn the depth. We can see that errors can be as high as 2 time the global error for persons and cars, and it must be in mind if these methods are used to predict the distance of an object. Finally we can see that our results over the NuScenes dataset have higher errors than on KITTI, this can be explained by the differences in the training between the two datasets and NuScenes having more challenging scenes for depth estimation. For example, some scenes have been acquired in a raining weather which have reflections due to the wet road. Scenes have also been captured during nighttime with poor visibility. These scenes have a much higher error than those with good visibility and it contributes to increase the mean errors used for calculating the global error. By combining our two evaluation protocols, we also computed the evolution of the error for objects like cars over distance ranges in Figures 2 and3. Such comparative results can be used to evaluate the fitness of a depth estimation method to a particular scenario in road environments. For example for driving in regular road where we assume that the vehicle is travelling at 90km/h the method have to be accurate up to 60m (safety distance between two vehicles at that speed).

V. CONCLUSIONS

We presented in this paper a novel depth evaluation protocol better suited for autonomous driving applications in road scenes as well as an evaluation of BTS and Monodepth2, two state-of-the-art monocular depth estimation methods, using our new protocols. For depth evaluation protocols, we proposed a protocol over distance ranges allowing to evaluate the evolution of the depth accuracy over the distance and a protocol for evaluating the depth predictions of objects by using the object masks generated using Mask-RCNN, one of the best object detector. We then performed an evaluation of an unsupervised method and an supervised one with Monodepth2 and BTS on two large scale datasets for road scenes, KITTI and NuScenes. The comparatives results of the evaluation shows that BTS have better performance on all aspects than Monodepth2. We also showed that object depth estimation errors were significantly higher than the errors on the whole image for both methods. However while we offer a comprehensive evaluation of depth estimation over road environments, more methods including stereobased algorithms could also be evaluated for a complete comparative study. We also aim to evaluate depth estimation algorithms on railway environments but due to the lack of a public dataset with camera images and depth ground truths from a LiDAR, we will need to acquire our own dataset for this task. Thus, we propose to develop an acquisition system including a stereoscopic camera and a LiDAR so that we can collect our own dataset in the railway environment.

Fig. 1 .

 1 Fig. 1. Input data for our object distance evaluation protocol. (1) the input image fed to the depth prediction algorithm, (2) the disparity map, (3) the normalized depth map after median scaling and (4) the object masks from Mask-RCNN.

Fig. 3 .

 3 Fig. 3. Our quantitative RMSE results for the car object class over distance ranges on the NuScenes dataset. RMSE is expressed in meters.

Fig. 4 .

 4 Fig. 4. Our RE results of BTS and Monodepth2 for different object classes compared to the global RE (hashed) on KITTI.

Fig. 5 .

 5 Fig. 5. Our RE results of BTS and Monodepth2 for different object classes compared to the global RE (hashed) on NuScenes.

TABLE I DEPTH

 I EVALUATION OVER DISTANCE RANGES ON KITTI. THE ALGORITHMS EVALUATED ARE STATE-OF-THE-ART MONOCULAR DEPTH ESTIMATION METHODS: MONODEPTH2 (MD2) AND BTS. GLOBAL DEPTH ERRORS ARE SHOWN AS WELL AS DEPTH ERRORS FOR DISTANCE RANGES OF 10M AND UP TO 80M. BOTH SRE AND RMSE ARE EXPRESSED IN METERS.

		RE		SRE		RMSE		logRMSE	a 1		a 2		a 3
	Distance ranges	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS
	0 -80m	0.115	0.060	0.882	0.249	4.701	2.798	0.190	0.096	0.879	0.955	0.961	0.993	0.982	0.998
	0 -10m	0.102	0.071	0.503	0.188	1.489	0.991	0.141	0.106	0.929	0.959	0.979	0.988	0.99	0.994
	10 -20m	0.116	0.088	0.845	0.395	3.035	2.198	0.18	0.149	0.891	0.924	0.96	0.971	0.979	0.985
	20 -30m	0.168	0.13	1.866	1.055	6.208	4.745	0.261	0.229	0.773	0.836	0.916	0.934	0.957	0.964
	30 -40m	0.196	0.16	2.788	1.945	9.11	7.476	0.307	0.279	0.694	0.764	0.886	0.906	0.942	0.947
	40 -50m	0.209	0.174	3.504	2.64	11.682	10.008	0.318	0.298	0.641	0.725	0.865	0.889	0.943	0.941
	50 -60m	0.221	0.19	4.394	3.739	14.252	12.852	0.332	0.326	0.583	0.675	0.857	0.868	0.927	0.922
	60 -70m	0.212	0.201	4.657	4.584	15.855	15.585	0.325	0.334	0.609	0.619	0.854	0.856	0.93	0.923
	70 -80m	0.181	0.214	4.34	5.454	15.8	18.219	0.284	0.333	0.652	0.548	0.873	0.843	0.945	0.925
							TABLE II

OBJECT

  DISTANCE EVALUATION ON KITTI. THE ALGORITHMS EVALUATED ARE STATE-OF-THE-ART MONOCULAR DEPTH ESTIMATION METHODS: MONODEPTH2 (MD2) AND BTS. DEPTH ERRORS WERE COMPUTED FOR THE OBJECT CLASSES WITH ENOUGH INSTANCE IN THE TEST SPLIT. BOTH SRE AND RMSE ARE EXPRESSED IN METERS.

		RE		SRE		RMSE		logRMSE	a 1		a 2		a 3
	Object class	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS
	Person	0.314	0.166	5.721 1.786	8.43	5.892	0.326	0.253	0.601	0.772	0.829	0.894	0.92	0.947
	Bicycle	0.131	0.116	0.517 0.467	2.81	2.669	0.172	0.163	0.829	0.839	0.964	0.962	0.993 0.994
	Car	0.206	0.137	3.132 1.491	7.924	6.052	0.271	0.223	0.773	0.838	0.883	0.922	0.938 0.955
	Truck	0.215	0.122	2.769 0.826	6.978	4.523	0.259	0.177	0.694	0.854	0.903	0.969	0.964 0.985

TABLE III DEPTH

 III EVALUATION OVER DISTANCE RANGES ON NUSCENES. THE ALGORITHMS EVALUATED ARE STATE-OF-THE-ART MONOCULAR DEPTH ESTIMATION METHODS: MONODEPTH2 (MD2) AND BTS. GLOBAL DEPTH ERRORS ARE SHOWN AS WELL AS DEPTH ERRORS FOR DISTANCE RANGES OF 10M AND UP TO 80M. BOTH SRE AND RMSE ARE EXPRESSED IN METERS.

		RE		SRE		RMSE		logRMSE	a 1		a 2		a 3
	Distance range	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS
	0 -80m	0.176	0.147	2.521	1.184	7.746	5.849	0.271	0.214	0.787	0.817	0.911	0.94	0.955	0.977
	0 -10m	0.115	0.116	0.982	0.43	1.561	1.347	0.139	0.151	0.919	0.915	0.972	0.974	0.986	0.987
	10 -20m	0.187	0.153	2.69	0.966	4.854	3.452	0.243	0.197	0.794	0.81	0.916	0.945	0.958	0.984
	20 -30m	0.242	0.162	3.488	1.386	8.316	5.427	0.32	0.217	0.643	0.768	0.859	0.933	0.932	0.978
	30 -40m	0.256	0.183	4.296	2.01	11.327	7.922	0.368	0.255	0.569	0.677	0.807	0.909	0.904	0.969
	40 -50m	0.264	0.206	5.14	3.047	14.167	10.952	0.404	0.3	0.518	0.598	0.76	0.845	0.888	0.946
	50 -60m	0.27	0.225	6.298	4.441	17.267	14.398	0.44	0.346	0.483	0.556	0.746	0.789	0.854	0.904
	60 -70m	0.28	0.248	8.024	6.275	20.725	18.243	0.475	0.385	0.48	0.495	0.692	0.736	0.817	0.868
	70 -80m	0.289	0.284	10.299	8.802	24.621	23.16	0.505	0.434	0.463	0.394	0.645	0.677	0.776	0.834

TABLE IV OBJECT

 IV DISTANCE EVALUATION ON NUSCENES. THE ALGORITHMS EVALUATED ARE STATE-OF-THE-ART MONOCULAR DEPTH ESTIMATION METHODS: MONODEPTH2 (MD2) AND BTS. DEPTH ERRORS WERE COMPUTED FOR THE OBJECT CLASSES WITH ENOUGH INSTANCE IN THE TEST SPLIT. BOTH SRE AND RMSE ARE EXPRESSED IN METERS.

		RE		SRE		RMSE		logRMSE	a 1		a 2		a 3
	Object class	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS	MD2	BTS
	Car	0.346	0.218	6.853	2.144	10.42	6.862	0.448	0.278	0.546	0.708	0.736	0.88	0.92	0.949
	Person	0.501	0.384	8.312	3.91	9.291	7.858	0.531	0.449	0.438	0.492	0.679	0.717	0.803	0.839
	Bus	0.448	0.228	11.837	2.226	13.929	7.811	0.448	0.274	0.465	0.644	0.729	0.891	0.848	0.958
	Truck	0.324	0.218	6.803	2.091	11.425	7.263	0.378	0.26	0.574	0.674	0.793	0.902	0.887	0.964
	Motorcycle	0.284	0.245	1.671	1.43	4.509	3.917	0.32	0.288	0.512	0.658	0.868	0.869	0.935	0.946

Fig. 2. Our quantitative RMSE results for the car object class over distance ranges on the KITTI dataset. RMSE is expressed in meters.

ACKNOWLEDGMENT

This research is supported by SEGULA Technologies and M2SINUM project (This project is co-financed by the European Union with the European regional development fund (ERDF, 18P03390/18E01750/18P02733) and by the Haute-Normandie Regional Council via the M2SINUM project). We would like to thank SEGULA Technologies for their collaboration and the engineers of Autonomous Navigation Laboratory of IRSEEM for their support. This work was performed in part on computing resources provided by CRIANN (Centre Régional Informatique et d'Applications Numériques de Normandie, Normandy, France).


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Introduction

In acute ischemic stroke (AIS) due to large vessel occlusion (LVO), mechanical thrombectomy preceded or not by intravenous thrombolysis affords >80% recanalization rates and significantly improves outcome [START_REF] Goyal | Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials[END_REF] . However, >50% of treated patients do not recover to an independent life [START_REF] Goyal | Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials[END_REF] . Understanding the mechanisms underlying such 'futile recanalizations' 2 may lead to novel therapies as adjuncts to thrombectomy [START_REF] Savitz | Reconsidering Neuroprotection in the Reperfusion Era[END_REF][START_REF] Baron | Protecting the ischaemic penumbra as an adjunct to thrombectomy for acute stroke[END_REF] . Tissue reperfusion is a stronger predictor of clinical outcome than recanalization [START_REF] Silva | Assessing reperfusion and recanalization as markers of clinical outcomes after intravenous thrombolysis in the echoplanar imaging thrombolytic evaluation trial (EPITHET)[END_REF][START_REF] Eilaghi | Reperfusion is a stronger predictor of good clinical outcome than recanalization in ischemic stroke[END_REF][START_REF] Cho | Reperfusion within 6 hours outperforms recanalization in predicting penumbra salvage, lesion growth, final infarct, and clinical outcome[END_REF] , but numerous adverse events may compromise reperfusion following thrombectomy, including per-procedural distal embolization, early re-occlusion, parenchymal hemorrhage (PH) and the 'no-reflow' phenomenon [START_REF] El Amki | Improving Cerebral Blood Flow after Arterial Recanalization: A Novel Therapeutic Strategy in Stroke[END_REF] .

No-reflow refers to the absence of tissue reperfusion despite complete recanalization, due to impaired microvascular perfusion. First described in rabbits following global cerebral ischemia [START_REF] Ames | Cerebral ischemia. II. The no-reflow phenomenon[END_REF] , it was subsequently confirmed in monkeys subjected to transient occlusion of the proximal middle cerebral artery (MCA) [START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Little | Microcirculatory obstruction in focal cerebral ischemia. Relationship to neuronal alterations[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF] in the form of extensive areas with complete lack of capillary filling following carbon black perfusion associated with tissue infarction, as well as more recently in rodents [START_REF] Yemisci | Pericyte contraction induced by oxidative-nitrative stress impairs capillary reflow despite successful opening of an occluded cerebral artery[END_REF][START_REF] Burrows | Delayed reperfusion deficits after experimental stroke account for increased pathophysiology[END_REF][START_REF] Desilles | Downstream Microvascular Thrombosis in Cortical Venules Is an Early Response to Proximal Cerebral Arterial Occlusion[END_REF] . No-reflow is currently believed to reflect capillary constriction secondary to pericyte disruption in previously ischemic areas [START_REF] Yemisci | Pericyte contraction induced by oxidative-nitrative stress impairs capillary reflow despite successful opening of an occluded cerebral artery[END_REF][START_REF] Hall | Capillary pericytes regulate cerebral blood flow in health and disease[END_REF] , possibly associated with precapillary small muscle cell constriction [START_REF] Hill | Regional Blood Flow in the Normal and Ischemic Brain Is Controlled by Arteriolar Smooth Muscle Cell Contractility and Not by Capillary Pericytes[END_REF] . Disruption of capillary flow facilitates microvascular obstructs containing trapped erythrocytes and leukocytes, fibrin deposits and aggregated platelets [START_REF] Yemisci | Pericyte contraction induced by oxidative-nitrative stress impairs capillary reflow despite successful opening of an occluded cerebral artery[END_REF][START_REF] Hall | Capillary pericytes regulate cerebral blood flow in health and disease[END_REF][START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF][START_REF] Dalkara | Pericytes: A Novel Target to Improve Success of Recanalization Therapies[END_REF] . Importantly, no-reflow may be prevented or counter-acted, for instance by acting on the NO pathway [START_REF] Yemisci | Pericyte contraction induced by oxidative-nitrative stress impairs capillary reflow despite successful opening of an occluded cerebral artery[END_REF] . Despite these experimental data, no-reflow remains poorly characterized in man, and while an incidence around 25% is frequently cited [START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] , its very existence is still debated. Three main factors, which all directly derive from its definition, may explain this situation. First, documentation of complete arterial recanalization, which is afforded only by digital subtraction angiography (DSA), is in principle required in order to reliably investigate no-reflow. However, DSA has only recently become part of routine acute stroke care, after thrombectomy became standard-of-care in early 2015 [START_REF] Goyal | Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials[END_REF] . Second, complete recanalization on DSA has been variably defined over time, and a strict definition as Modified Thrombolysis in Cerebral Infarction (mTICI) score 2c or 3 [START_REF] Goyal | 2C or not 2C: defining an improved revascularization grading scale and the need for standardization of angiography outcomes in stroke trials[END_REF] has only lately been widely accepted [START_REF] Lecouffe | 2B, 2C, or 3: What Should Be the Angiographic Target for Endovascular Treatment in Ischemic Stroke?[END_REF] . Third, and finally, using an imaging technique that directly assesses tissue perfusion is required to reliably evaluate reperfusion. To this day, however, no published study on no-reflow fulfills these three key criteria (see Discussion).

In the present proof-of-principle study, we therefore aimed to assess, in a strictly selected sample of thrombectomized LVO stroke patients with DSA-proven TICI2c-3 recanalization, the incidence of no-reflow. Based on available pre-clinical, particularly nonhuman primate, literature, no-reflow was operationally defined as severe focal hypoperfusion causing tissue infarction. Importantly, post-recanalization perfusion was evaluated by means of arterial spin labelling (ASL), a non-invasive MR-based technique that provides truly quantitative perfusion maps without the limitations of contrast agents [START_REF] Zaharchuk | Arterial spin-labeled perfusion imaging in acute ischemic stroke[END_REF][START_REF] Harston | Quantification of Serial Cerebral Blood Flow in Acute Stroke Using Arterial Spin Labeling[END_REF] and that has been validated against conventional methods in both healthy controls and AIS patients [START_REF] Bivard | Arterial spin labeling identifies tissue salvage and good clinical recovery after acute ischemic stroke[END_REF][START_REF] Bivard | Arterial spin labeling versus bolus-tracking perfusion in hyperacute stroke[END_REF][START_REF] Fan | Comparison of cerebral blood flow measurement with [15O]-water positron emission tomography and arterial spin labeling magnetic resonance imaging: A systematic review[END_REF][START_REF] Huang | Comparison of arterial spin labeling and dynamic susceptibility contrast perfusion MRI in patients with acute stroke[END_REF][START_REF] Nael | Quantitative analysis of hypoperfusion in acute stroke: arterial spin labeling versus dynamic susceptibility contrast[END_REF][START_REF] Puig | Comparison of simultaneous arterial spin labeling MRI and (15)O-H2O PET measurements of regional cerebral blood flow in rest and altered perfusion states[END_REF] .

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Operational definition of no-reflow

No-reflow by definition entails severe tissue hypoperfusion despite full recanalization of the previously occluded territory [START_REF] Ames | Cerebral ischemia. II. The no-reflow phenomenon[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF][START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] . For the present study, we operationally defined no-reflow as ≥40% reductions in cerebral blood flow (CBF) affecting anatomical regions of the affected hemisphere on 24-hr ASL maps. This cut-off was based on a previous ASL study that demonstrated that ≥40% CBF reduction relative to contralateral regions best identifies critical hypoperfusion, with high correlation with standard first-pass gadolinium perfusion-weighted sequence (PWI) [START_REF] Bivard | Arterial spin labeling versus bolus-tracking perfusion in hyperacute stroke[END_REF] . Anatomical regions were based on ASPECTS (Alberta Stroke Program Early CT Score) [START_REF] Barber | Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score[END_REF] , which was recently used by Yu et al to validate a score of ASL-based reperfusion after AIS [START_REF] Yu | ASPECTS-based reperfusion status on arterial spin labeling is associated with clinical outcome in acute ischemic stroke patients[END_REF] .

The other key operational component to identify no-reflow was tissue outcome. As noreflow is consistently associated with tissue infarction [START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF][START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] , we considered no-reflow to be present only if the severely hypoperfused ASPECTS area as defined above also exhibited infarction on 24hr follow-up MRI.

Study population

For the present single-center study, we retrospectively searched the Sainte-Anne Hospital prospective database of all consecutive patients treated by MT, which includes patients either directly admitted ("mothership") or referred for MT to Sainte-Anne from other centers ("drip and ship"). As per current French regulations, prospective inclusion of patients in this database has received formal regulatory agreement. Because the present study only implied retrospective analysis of anonymized data collected as part of routine care, formal approval by an Ethics Committee was not required, but patients were offered the possibility to withdraw.

Inclusion criteria for the present study were as follows: 1/ anterior circulation AIS treated between 1/2014 and 5/2019; 2/ intracranial internal carotid artery (ICA) or first segment MCA occlusion (i.e., proximal LVO, the pre-clinical situation where no-reflow has been best documented [START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF][START_REF] Desilles | Downstream Microvascular Thrombosis in Cortical Venules Is an Early Response to Proximal Cerebral Arterial Occlusion[END_REF] ); 3/ successful recanalization by means of MT, defined as a modified TICI 2c or 3 [START_REF] Goyal | 2C or not 2C: defining an improved revascularization grading scale and the need for standardization of angiography outcomes in stroke trials[END_REF] according to an experienced neuroradiologist (WBH) who reviewed the DSA dataset blinded to findings on 24hr ASL; and 4/ availability of MRI both before MT ('MRI-1') and ~24hr after MT ('MRI-2'; including an ASL-sequence). Thrombolysis performed prior to MT according to international guidelines was not a cause of exclusion.

Exclusion criteria were 1/ presence on MRI-2 of parenchymal hematoma (ECASS classification [START_REF] Hacke | Intravenous thrombolysis with recombinant tissue plasminogen activator for acute hemispheric stroke. The European Cooperative Acute Stroke Study (ECASS)[END_REF] ), which may hinder assessment of ASL maps [START_REF] Tamm | Cerebral Perfusion Pressure is Maintained in Acute Intracerebral Hemorrhage: A CT Perfusion Study[END_REF] ; 2/ re-occlusion, new intracranial occlusion or ipsi-or contralateral ICA or MCA ≥ 50% stenosis on MRA-2, which may also affect brain perfusion; and 3/ un-interpretable ASL images due to movement artefacts, technical problems or inordinately low whole brain CBF (<20mL/100g/min).

Because no-reflow is expected to expand tissue necrosis, a secondary objective of the study was to assess MRI-1 to MRI-2 infarct growth. To limit the chance of marked prerecanalization infarct growth, an additional exclusion criterion was MRI-1-to-groin puncture delay ≥3hrs.

MRI protocol

In France, MRI is recommended as first line diagnostic imaging work-up in candidates to recanalization, and is implemented 24/7 at the Sainte-Anne comprehensive stroke center. At Sainte-Anne hospital, a follow-up MRI (MRI-2) is carried out around 24hrs after treatment whenever feasible. All MRI data were acquired using a 1.5T MRI scanner (General Electric Healthcare). As per the above inclusion criteria, patients were included in the present study only if they had undergone both the acute and follow-up MRI sessions, and if the latter included ASL.

The following standard sequences were acquired: i) Diffusion-Weighted Imaging (DWI) with b=0s/mm 2 and b=1000s/mm 2 , 6mm thick axial slices; ii) Fluid-Attenuated Inversion Recovery (FLAIR); iii) T2*, to determine the presence of haemorrhages; and iv) 3D Time-of-Flight (3D TOF) MR angiography (MRA), to determine the presence and localization of intracranial occlusion on MRI-1 and the absence of reocclusion on MRI-2. In addition, ASL perfusion maps were obtained at MRI-2 using a 3D pseudo-continuous sequence acquired using the following parameters: 4.5 minutes duration, 3.5×3.5×4mm resolution, TR/TE = 4600/10.5ms, pairs of control/label 3; post-labeling delay 2000ms, 40 slices with a 4.0mm thickness with whole brain coverage and background suppression, in order to suppress signal from static tissue and hence improve signal-to-noise ratio while reducing sensitivity to motion artifacts. ASL-CBF parametric perfusion maps were automatically calculated from the labelled and unlabelled ASL images (M0) with the READY VIEW® software (GE Healthcare).

Whenever feasible, MRI-1 also included a PWI sequence as well as, whenever needed, a contrast-enhanced cervical MRA to guide decision-making regarding MT.

Identification of no-reflow on ASL Maps

1. Visual analysis Visual analysis was performed independently by two investigators with ASPECTS training (ATS, CP), blinded to clinical information except stroke side. Because anatomical identification may be difficult on ASL maps, the latter were rigidly co-registered to the 6mm-thick 24-hr DWI and FLAIR images, using General Electric Volume Viewer® software. Then, replicating the previously validated approach [START_REF] Yu | ASPECTS-based reperfusion status on arterial spin labeling is associated with clinical outcome in acute ischemic stroke patients[END_REF] , the investigators were instructed to select the two axial slices best matching the standard ASPECTS slices [START_REF] Barber | Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score[END_REF] , and to assess the affected hemisphere across the 10 ASPECTS regions (which have no formal boundaries [START_REF] Barber | Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score[END_REF] ) for the presence of severe hypoperfusion as compared to contralateral mirror ASPECTS region. Any ASPECTS region with old infarction on MRI-1 FLAIR or hemorrhagic infarction type-2 on MRI-2 T2* was excluded. To confirm their judgment, the investigator positioned on the co-registered DWI image a circular or elliptic two-dimensional region of interest (ROI) encompassing the hypoperfused region, and mirrored the ROI in the contralateral hemisphere. As per the operational criteria above, severe hypoperfusion was retained if mean CBF in the affected ROI was reduced ≥40% relative to mirror ROI. Discrepancies between readers were to be resolved by consensus. As per the operational definition, any eligible ASPECTS region was then assessed for the presence of infarction on MRI-2. In addition, they were also assessed for the presence on MRI-1 of DWI hyperintense signal as well as hypoperfusion (whenever PWI was available; no-reflow being expected to develop in severely hypoperfused areas [START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] ).

Automatic analysis

To underpin the results of the visual analysis, we implemented an independent, fully quantitative and automated analysis replicating Yu et al's method 32 . To this end, an ASPECTS ROI template was created using the MANGO software, version 4.0.1 (Research Imaging Institute, University of Texas Health Science Center), in the T2 Montreal Neurological Institute MNI 152 space, which contains 152 axial sections each 2mm thick. In order to match the visual analysis, three contiguous MNI sections (thickness: 2mm) were selected by the first author for each of the two standard ASPECTS cuts, and the template was created by drawing the ASPECTS ROIs on each of the 6 resulting sections [START_REF] Yu | ASPECTS-based reperfusion status on arterial spin labeling is associated with clinical outcome in acute ischemic stroke patients[END_REF] , based on the AAL-2 atlas [START_REF] Rolls | Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas[END_REF] and the study by Tatu et al [START_REF] Tatu | Arterial territories of the human brain: cerebral hemispheres[END_REF] .

To apply the MNI-space ASPECTS template onto each patient's ASL images, we implemented the post-processing pipeline illustrated in the Supplemental Figure, using Matlab R2018b (The MathWorks, Inc., Natick, MA, USA) via SPM12 (Wellcome Center for Human Imaging, UCL, UK) and the Clinical Toolbox (CRNL, University of South Carolina, USA). As initial normalization step we elected to normalize the b=0 maps into the Montreal Neurological Institute (MNI) space rather than directly the ASL into this space, because ASL maps are functional, as opposed to b=0s/mm 2 being anatomical. This approach is recommended for efficient normalization [START_REF] Ashburner | Nonlinear spatial normalization using basis functions[END_REF] . To coregister the ASL maps into MNI space, we then registered the b=0s/mm 2 image with the M0 ASL sequence rather than the ASL derived CBF maps, as the former contains greater anatomical information. Once the ASL dataset was co-registered to the MNI 152 template, the ASPECTS ROI template was then simply projected on the six ASL slices. The CBF value for each ASPECTS region was then obtained by computing the weighted average of the CBF values of the three original ROIs belonging to this region. As with the visual analysis, the presence of no-reflow was retained whenever CBF in an ipsilesional ASPECTS ROI was reduced ≥40% relative to contralateral mirror ROI.

Clinical and imaging variables

The following data were collected: iii) Thrombolysis and time of thrombolysis start, groin puncture time, mTICI score; iv) Cause of stroke (TOAST criteria) [START_REF] Tomsick | Revascularization results in the Interventional Management of Stroke II trial[END_REF] ;

v) NIHSS at 24hrs; early neurological improvement or deterioration (≥8-point decrease in 24hr NIHSS relative to baseline or 24hr NIHSS ≤4, and ≥4-point increase on 24hr NIHSS, respectively [START_REF] Seners | Incidence, causes and predictors of neurological deterioration occurring within 24 h following acute ischaemic stroke: a systematic review with pathophysiological implications[END_REF][START_REF] Wityk | Serial assessment of acute stroke using the NIH Stroke Scale[END_REF] ).

vi) 3-month follow-up data: mRS; favourable outcome was defined as mRS≤2.

Statistical analysis

Data were analysed using SPSS Statistics Version 25 (IBM, Armonk, NY). Kappa coefficients were calculated to characterize inter-rater agreement for the visual analysis. To analyse differences between included and non-included patients, continuous variables were compared using the Wilcoxon rank-sum test, and categorical data were analysed by means of Fisher exact test. Tests were bilateral and acceptable type I error was set a priori at α=0.05.

Means are presented as mean±SD, medians as median (IQR).

Results

Study population

Thirty-three patients fulfilled the inclusion criteria. Figure 1 presents the study flowchart including the causes for exclusion. Because in our center most drip-and-ship patients return to their referring stroke center immediately following the procedure, only three drip-andship patients had MRI-2 including ASL in the registry; however, they were ineligible to the present study because the delay from pre-MT MRI and groin puncture was >3hrs.

Consequently, only mothership patients were included in the analysis.

The baseline characteristics, clinical and imaging data, treatment received and outcome data are summarized in Tables 1 and2. Unfavorable outcome (mRS>2) was present in 12

patients. There was a significant association between unfavorable outcome and larger admission DWI lesion volume (p = 0.001; Mann-Whitney).

Twenty-six mothership patients with TICI 2c-3 recanalization for ICA or M1 occlusion did not undergo follow-up ASL imaging (Figure 1), of which 14 fulfilled the other inclusion/exclusion criteria for this study. None of the baseline and follow-up characteristics of these 14 patients significantly differed from those of the 33 included patients (Supplemental Table 1).

No-reflow analysis

Across the 33 included patients, eight ASPECTS regions were excluded a priori because of ipsilateral old infarct or hemorrhagic transformation, or contralateral old infarct (n=3, 3 and 2, respectively). Accordingly, 322 ipsilateral ASPECTS regions were available for final analysis.

Visual analysis

No instance of severe focal hypoperfusion was identified in 32/33 patients. In the remaining patient, both investigators independently identified severe hypoperfusion in the same two ASPECTS regions, namely the caudate and lentiform (Figure 2). Inter-observer reproducibilty was therefore perfect (kappa = 1). Both regions were infarcted on MRI-2, and therefore fulfilled the operational criteria for no-reflow. Importantly, neither exhibited DWI hyperintensity on MRI-1, indicating they underwent infarction subsequently. Likewise, they did not exhibit perfusion abnormalities on baseline PWI. This patient had early neurological recovery (24-hr NIHSS=1) and excellent outcome (3-month mRS=0). Further clinical and imaging details are provided in the figure legend.

Automatic analysis

Consistent with the visual analysis, no ASPECTS ROI exhibited severe hypoperfusion in any patient save for the patient already identified with the visual analysis, involving the caudate nucleus (CBF = -73%) but not reaching the prescribed CBF threshold for the lentiform ROI (-22%).

Mild hypoperfusions

Given the rather unexpected finding that in our cohort no-reflow, as operationally defined here, was present in one patient only as compared to the widely cited prevalence of ~25% [START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] , we carried out a post hoc analysis assessing milder hypoperfusions. Using the same visual analysis procedure as described above, each ASPECTS region in the affected hemispheres (saved for the two severely hypoperfsued regions just described) was classified by the two independent observers into four categories, according to the presence or absence of i)

hypoperfusion not fulfilling the criteria for severe hypoperfusion; and ii) infarction on concomitant DWI. The inter-observer reproducibility was again very good. Of 33 patients, 11 had at least one mildly hypoperfused ASPECTS region (Supplemental Table 2). Across the whole sample, 43/320 ASPECTS regions displayed mild hypoperfusion, of which two only were infarcted on MRI-2, both involving the basal ganglia in a single patient (Figure 3). Of note, however, both regions already showed diffusion abnormality at baseline. The remaining 41 mildly hypoperfused but non-infarcted ASPECTS regions were all cortical, except one involving the caudate.

Discussion

Consistently with both the visual and automatic analyses, there was evidence of noreflow, as operationally defined in the present study, in one out of 33 patients only. This somewhat unexpected finding suggests that no-reflow may be a rare phenomenon, unlikely to substantially contribute to 'futile' recanalizations.

Supporting the above interpretation, the only patient who fulfilled the no-reflow criteria in our cohort had excellent outcome (mRS=0), while none of the 11 patients with poor outcome (mRS >2) did. Conversely, there was a strong association between unfavorable outcome and larger baseline DWI lesion volume, consistent with extensive previous literature [START_REF] Bivard | Tissue Is More Important than Time in Stroke Patients Being Assessed for Thrombolysis[END_REF][START_REF] Marchal | Voxel-based mapping of irreversible ischaemic damage with PET in acute stroke[END_REF][START_REF] Xie | Pretreatment lesional volume impacts clinical outcome and thrombectomy efficacy[END_REF] . Thus, large baseline core, rather than no-reflow, may be the main factor accounting for futile recanalizations.

Interestingly, in the patient fulfilling the no-reflow operational criteria, the affected basal ganglia was not initially hypoperfused and only exhibited a small faint diffusion abnormality in the lentiform's posterior-most part, consistent with the fact that the lenticulo-striate arteries were patent on admission MRA (Fig 3). In retrospect, the clinical events prior to admission suggest initially more proximal M1 occlusion followed by spontaneous distal clot migration, resulting in clinical improvement and spontaneous reperfusion of the basal ganglia preventing extensive damage [START_REF] Olivot | Relationships between cerebral perfusion and reversibility of acute diffusion lesions in DEFUSE: insights from RADAR[END_REF] . Although lack of baseline hypoperfusion is unexpected for no-reflow, which is thought to develop in ischemic tissue [START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] , the initial ischemic insult might have triggered delayed no-reflow. Two alternative mechanisms may account for basal ganglia delayed hypoperfusion and infarction in this patient, namely inadvertent occlusion of lenticulostriate arteries during, and cardio-embolic recurrence after the procedure, both implying another ischemic event rather than no-reflow.

The discrepancy between the visual and automatic analyses regarding this patient's lenticular nucleus can be explained by the fact that severe hypoperfusion on 24hr ASL spared its postero-lateral part (Figure 2), diluting the average CBF in the lentiform ASPECTS ROI used in the automatic analysis. Although the visual analysis of ASL maps has been validated against the template-based analysis [START_REF] Yu | ASPECTS-based reperfusion status on arterial spin labeling is associated with clinical outcome in acute ischemic stroke patients[END_REF] , our data suggest it is more sensitive to focal hypoperfusion not affecting entire ASPECTS ROIs.

How do our findings compare to published literature? The characteristics and findings of all 11 previously published articles that investigated impaired reperfusion despite recanalization as a surrogate for no-reflow 5-7, 25, 45-51 are presented in Table 3. The key observation is that all the studies had significant methodological shortcomings, calling their findings into question. Thus, two studies only used DSA to assess recanalization [START_REF] Baird | Reperfusion after thrombolytic therapy in ischemic stroke measured by single-photon emission computed tomography[END_REF][START_REF] Khatri | Revascularization end points in stroke interventional trials: recanalization versus reperfusion in IMS-I[END_REF] , while the rest used techniques such as transcranial Doppler, MRA or CTA, which can miss distal occlusions. Importantly, those two studies that used DSA to assess recanalization [START_REF] Baird | Reperfusion after thrombolytic therapy in ischemic stroke measured by single-photon emission computed tomography[END_REF][START_REF] Khatri | Revascularization end points in stroke interventional trials: recanalization versus reperfusion in IMS-I[END_REF] unfortunately had major limitations: one defined recanalization as either partial or complete [START_REF] Baird | Reperfusion after thrombolytic therapy in ischemic stroke measured by single-photon emission computed tomography[END_REF] , and in the other DSA was used to assess reperfusion [START_REF] Khatri | Revascularization end points in stroke interventional trials: recanalization versus reperfusion in IMS-I[END_REF] . Nine studies reported no-reflow rates of 21 to 40%, which due to these limitations may be overestimated. However, the two remaining studies, though affected by similar drawbacks, reported no instance of hypoperfusion following recanalization. In one study [START_REF] Bivard | Arterial spin labeling identifies tissue salvage and good clinical recovery after acute ischemic stroke[END_REF] , hypoperfusion on 24hr ASL was present in 41/100 patients (all receiving medical treatment only), but none of these patients had complete recanalization on 24hr MRA, suggesting absence of no-reflow -a finding not discussed by the authors. Another study [START_REF] Cho | Reperfusion within 6 hours outperforms recanalization in predicting penumbra salvage, lesion growth, final infarct, and clinical outcome[END_REF] reported no instance of no-reflow in 13 patients with full recanalization on follow-up MRA, but most had distal (M2) occlusions, where no-reflow might be difficult to detect, while reperfusion was assessed ≤3hrs of admission MRI, which might be too early to reliably assess no-reflow. Despite these caveats, these two studies are consistent with our findings, i.e., they suggest that no-reflow is infrequent. Also favoring a low prevalence of no-reflow, another study (not listed in Table 3 as its focus wasn't on tissue reperfusion) quoted in its Methods section a rate of incomplete reperfusion in mTICI3 thrombectomized patients of 7% only [START_REF] Albers | Relationships Between Imaging Assessments and Outcomes in Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment for Acute Ischemic Stroke[END_REF] . The data reported in the EXTEND-IA [START_REF] Campbell | Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection[END_REF] and DEFUSE-3 [START_REF] Albers | Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging[END_REF] thrombectomy trials also appear to be consistent with a low prevalence of no-reflow: although in both reports the actual data on reperfusion after complete recanalization are missing and the latter included TICI 2b, the reported reperfusion rates were 89% and 79%, while recanalization rates were 86% and 76%, respectively.

Another piece of evidence mitigating against a high prevalence of significant no-reflow in our population was the negligible infarct volume growth from MRI-1 to MRI-2 (median =2 mL; Table 2), which is consistent with a recent thrombectomy study. [START_REF] Gauberti | Ischemia-Reperfusion Injury After Endovascular Thrombectomy for Ischemic Stroke[END_REF] Although a portion of the acute DWI lesion may revert on 24hr follow-up MRI in some patients, it is now widely acknowledged that the latter provides a valid metric for final infarct [START_REF] Nagaraja | Reversible diffusion-weighted imaging lesions in acute ischemic stroke: A systematic review[END_REF] .

In the present study, we considered complete recanalization as a key inclusion criterion in order to reliably assess no-reflow. Accordingly, only patients with TICI 2c-3 arteriographic reperfusion grades were considered eligible. As a note of caution, TICI 2c definition is "nearcomplete perfusion except for slow flow in a few distal cortical vessels or presence of small distal cortical emboli", as opposed to TICI 3 which is normal angiogram [START_REF] Goyal | 2C or not 2C: defining an improved revascularization grading scale and the need for standardization of angiography outcomes in stroke trials[END_REF][START_REF] Lecouffe | 2B, 2C, or 3: What Should Be the Angiographic Target for Endovascular Treatment in Ischemic Stroke?[END_REF] . If one considered only TICI 3 as true complete recanalization, the incidence of no-reflow would be one in 11 patients, i.e. <10%. However, the lack of no-reflow in the 22 patients with TICI2c would be unexpected given this arteriographic grade implies poorer distal contrast clearance.

Interestingly, recent studies point to no significant difference in clinical outcome between the TICI 3 and TICI 2c grades [START_REF] Lecouffe | 2B, 2C, or 3: What Should Be the Angiographic Target for Endovascular Treatment in Ischemic Stroke?[END_REF] .

An entirely different hypothesis to account for our observation would posit that noreflow may exist mainly with longer occlusion times. In primates, no-reflow was prominent only after 3-4-hr MCA occlusion [START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Little | Microcirculatory obstruction in focal cerebral ischemia. Relationship to neuronal alterations[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF][START_REF] Morawetz | Cerebral blood flow determined by hydrogen clearance during middle cerebral artery occlusion in unanesthetized monkeys[END_REF] , and it was absent following 2-hr occlusion in a rat study [START_REF] Li | Capillary patency after transient middle cerebral artery occlusion of 2 h duration[END_REF] . Conversely, however, some pre-clinical studies have observed no-reflow as early as 1hr after occlusion [START_REF] Yemisci | Pericyte contraction induced by oxidative-nitrative stress impairs capillary reflow despite successful opening of an occluded cerebral artery[END_REF][START_REF] Hill | Regional Blood Flow in the Normal and Ischemic Brain Is Controlled by Arteriolar Smooth Muscle Cell Contractility and Not by Capillary Pericytes[END_REF] . Our patients received thrombectomy as per the mothership paradigm, and accordingly the median delay between last-time-seen-well and recanalization was relatively short, namely 208mins, although the range was wide (112 to 750min, with one outlier at 1325min). Nonetheless, no-reflow was rare in our standard mothership population and in turn would not account for the poor outcomes that affected one-third of our patients despite complete recanalization (Table 2).

As almost two-thirds of our patients received thrombolysis (Table 1), could t-PA have prevented no-reflow? To our knowledge, this issue has not been addressed experimentally, but one clinical study reported a 22% no-reflow incidence despite 68% of the sample receiving thrombolysis [START_REF] Horsch | Predictors of reperfusion in patients with acute ischemic stroke[END_REF] . Alternatively, could no-reflow be common only in situation of incomplete recanalization? In a thrombectomy study by Arsava et al [START_REF] Arsava | Angiographic Microcirculatory Obstructions Distal to Occlusion Signify Poor Outcome after Endovascular Treatment for Acute Ischemic Stroke[END_REF] , iodine contrast was injected into the distal site of occlusive thrombi prior to stent-retriever deployment, which revealed lack of capillary blush -taken to indicate microcirculatory obstructions -in 7/34 patients, all of whom subsequently had incomplete recanalization [START_REF] Arsava | Angiographic Microcirculatory Obstructions Distal to Occlusion Signify Poor Outcome after Endovascular Treatment for Acute Ischemic Stroke[END_REF] , suggesting that impaired microcirculatory flow might impact recanalization per se. However post-thrombectomy perfusion status was not assessed in this study, and the above pattern was not seen in patients who fully recanalized, making these observations of unclear relevance to the no-reflow phenomenon.

In the present study, presence of PH was a cause of exclusion as it would markedly hinder interpretation of 24hr perfusion maps. This is unlikely to have affected our finding, as no-reflow by definition prevents tissue reperfusion, and accordingly no-reflow was not associated with PH in primate models [START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Little | Microcirculatory obstruction in focal cerebral ischemia. Relationship to neuronal alterations[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF] , while one rodent study only reported associated micro-hemorrhages on pathology [START_REF] Desilles | Downstream Microvascular Thrombosis in Cortical Venules Is an Early Response to Proximal Cerebral Arterial Occlusion[END_REF] . In the above-mentioned Arsava study [START_REF] Arsava | Angiographic Microcirculatory Obstructions Distal to Occlusion Signify Poor Outcome after Endovascular Treatment for Acute Ischemic Stroke[END_REF] , the sole PH-2 case was a patient without evidence of microcirculatory obstruction. Likewise, we excluded a priori all ASPECTS regions with hemorrhagic infarction type-2, which might per se affect regional perfusion.

However, this concerned only 3 regions, none of which displayed significant hypoperfusion on post-hoc assessment, consistent with the established notion that hemorrhagic transformation is associated with post-recanalization hyperperfusion [START_REF] Okazaki | Cerebral hyperperfusion on arterial spin labeling MRI after reperfusion therapy is related to hemorrhagic transformation[END_REF] .

Taken together, therefore, our findings suggest that no-reflow, as operationally defined here, may be at best infrequent in human stroke. How can this be reconciled with the pre-clinical evidence? Given the animal, including primate, literature [START_REF] Ames | Cerebral ischemia. II. The no-reflow phenomenon[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in monkeys[END_REF][START_REF] Crowell | Impaired microvascular filling after focal cerebral ischemia in the monkey. Modification by treatment[END_REF][START_REF] Little | Microcirculatory Obstruction in Focal Cerebral Ischemia: An Electron Microscopic Investigation in Monkeys[END_REF][START_REF] Dalkara | Can restoring incomplete microcirculatory reperfusion improve stroke outcome after thrombolysis?[END_REF] , where no-reflow is defined as markedly impaired or absent capillary perfusion, we considered severe hypoperfusion a key operational criterion to define no-reflow, and used as cut-off the value of 40% previously validated in earlier ASL studies as a surrogate to Tmax>6s, the validated penumbra threshold [START_REF] Zaharchuk | Arterial spin-labeled perfusion imaging in acute ischemic stroke[END_REF][START_REF] Bivard | Arterial spin labeling versus bolus-tracking perfusion in hyperacute stroke[END_REF] . Using this definition, one patient only out of 33 with complete recanalization showed possible no-reflow, which prompted us to revisit post-hoc our dataset to assess milder hypoperfusions. Assessing milder hypoperfusions would also address the seminal observation made by Ames et al following complete cerebral circulation arrest that lack of capillary filling involved large confluent areas interspersed with 'mottled' areas [START_REF] Ames | Cerebral ischemia. II. The no-reflow phenomenon[END_REF] . If mottled no-reflow also existed in human stroke, and considering the limited spatial resolution of ASL, it would translate as milder hypoperfusion. The results of the post-hoc analysis revealed that mild hypoperfusions were relatively common, with 11/33 patients showing at least one mildly hypoperfused ASPECTS region, for a total of 43 regions across the cohort. However, two such regions only were infarcted on MRI-2, both involving the basal ganglia in a single patient. Although these two regions might represent a milder form of no-reflow, it is important to note that they were already infarcted on admission DWI (Figure 3). This kind of mild no-reflow developing in already infarcted brain tissue would however have little clinical relevance.

Could mottled no-reflow exist without frank tissue infarction? One might raise the possibility that the penumbra rescued from infarction might be the seat of microvascular impairment as a result of either or both pre-reperfusion ischemic endothelial and pericyte damage [START_REF] Hall | Capillary pericytes regulate cerebral blood flow in health and disease[END_REF][START_REF] Del Zoppo | Heterogeneity in the penumbra[END_REF] and reperfusion injury [START_REF] Bai | Revisiting cerebral postischemic reperfusion injury: new insights in understanding reperfusion failure, hemorrhage, and edema[END_REF] , potentially causing selective neuronal loss (SNL), which has no counterpart on conventional MRI [START_REF] Baron | Selective neuronal loss in ischemic stroke and cerebrovascular disease[END_REF] . Occurrence of SNL in the salvaged penumbra is well established histopathologically in rodent models of transient MCA occlusion [START_REF] Baron | Selective neuronal loss in ischemic stroke and cerebrovascular disease[END_REF] , and is strongly supported by [START_REF] Little | Microcirculatory obstruction in focal cerebral ischemia. Relationship to neuronal alterations[END_REF] C-flumazenil PET in stroke patients with early reperfusion [START_REF] Guadagno | Selective neuronal loss in rescued penumbra relates to initial hypoperfusion[END_REF][START_REF] Morris | Relationships between selective neuronal loss and microglial activation after ischaemic stroke in man[END_REF][START_REF] Saur | Iomazenil-single-photon emission computed tomography reveals selective neuronal loss in magnetic resonance-defined mismatch areas[END_REF] .

However, although in rodents SNL slows down sensori-motor recovery [START_REF] Ejaz | Cortical selective neuronal loss, impaired behavior, and normal magnetic resonance imaging in a new rat model of true transient ischemic attacks[END_REF][START_REF] Sicard | Long-term changes of functional MRIbased brain function, behavioral status, and histopathology after transient focal cerebral ischemia in rats[END_REF] , no clinical counterpart has been documented so far in human stroke [START_REF] Morris | Relationships between selective neuronal loss and microglial activation after ischaemic stroke in man[END_REF][START_REF] Carrera | Is neural activation within the rescued penumbra impeded by selective neuronal loss?[END_REF] . Further studies of no-reflow combining 24-hr ASL and delayed [START_REF] Little | Microcirculatory obstruction in focal cerebral ischemia. Relationship to neuronal alterations[END_REF] C-flumazenil PET would be important to address this hypothesis.

What additional mechanisms could underly the mild hyperfusions observed on 24hr ASL maps, knowing that patients with persistent significant arterial stenosis/occlusion on 24hr

MRA were a priori excluded from the present study? One likely mechanism is ipsilesional diaschisis, i.e., functional suppression in areas remote from but connected to the damaged area -a well-established, widely prevalent phenomenon after stroke, which frequently involves the cerebral cortex overlying deep MCA territory infarcts, so-called intra-hemispheric diaschisis 70- 72 . Interestingly, all 11 patients with cortical hypoperfusion in our sample had deep MCA infarcts, supporting this idea (Supplemental Table 2). To further address this hypothesis, we assessed for the presence of crossed cerebellar diaschisis (CCD), a type of diaschisis known to frequently associate with cortical diaschisis [START_REF] Pappata | Effects of capsular or thalamic stroke on metabolism in the cortex and cerebellum: a positron tomography study[END_REF][START_REF] Baron | Crossed cerebellar diaschisis" in human supratentorial brain infarction[END_REF] . CCD was present in 6/28 patients, and was significantly more frequent in those with, as compared to those without, mildly hypoperfused cortical regions (p=0.038, Fisher). These findings are illustrated in Figure 3; please note also in this patient the extension of the cortical mild hypoperfusion beyond the MCA territory, which would also favor diaschisis). Intra-hemispheric diaschisis therefore likely accounts for a substantial fraction of the mild cortical hypoperfusions observed in our cohort.

Our mechanistic study has limitations. Given the relatively small sample and retrospective design, its findings warrant confirmation in prospective cohorts. However, our cohort was extracted from a large prospective database of consecutively thrombectomized patients (Fig 1 ), using stringent operational criteria required to match our operational definition, notably both TICI 2c-3 recanalization and availability of 24hr ASL. As a result of our choice to replicate the only previously validated method to assess post-thrombectomy ASL maps [START_REF] Yu | ASPECTS-based reperfusion status on arterial spin labeling is associated with clinical outcome in acute ischemic stroke patients[END_REF] , severe hypoperfusion was evaluated in ASPECTS regions only. To address this potential limitation, a post-hoc visual analysis was carried out assessing all axial sections across the whole sample, which did not identify any additional severely hypoperfused reagion (data not shown). Finally, our results are applicable only to the population studied, namely mothership patients with proximal occlusion.

Summary

In our cohort of patients with proximal occlusion who enjoyed complete and stable postthrombectomy recanalization, severe focal hypoperfusion in the affected vascular territory on 24hr follow-up imaging associated with newly developed infarction was a rare occurrence. This unexpected finding may suggest that in human stroke no-reflow is in fact an infrequent phenomenon that may not substantially contribute to 'futile' recanalizations or stand as an important therapeutic target. Prospective studies are however warranted to confirm our findings. ASL 24 hours 33 1 (3.3%) ‡: these techniques are inadequate to identify distal occlusions, which could in turn have caused some of the observed hypoperfusions; †: Inclusion of partial recanalization could in part account for some of the reported hypoperfusions. §: in some patients of this study (number missing), follow-up perfusion imaging was not available, in which case reperfusion was assumed to be present based on TICI 3 recanalization on completion of thrombectomy; $: lack of re-occlusion was verified in each patient using the 24hr follow-up MRA.

Figure legends:

AFTER THROMBECTOMY

  i) Clinical data: age, sex, vascular risk factors, history of atrial fibrillation, pre-stroke modified Rankin scale; time since last-seen-well, time of admission, National Institutes of Health Stroke Scale (NIHSS) score; ii) Imaging data: occlusion site (ICA, proximal or distal MCA-M1, tandem), DWI lesion site (deep or cortical) and volume for both MRI sessions: using MANGO software, DWI lesions were semi-automatically segmented, and volumes were extracted.

Figure 1 :

 1 Figure 1: Study flowchart: Abbreviations: CCA = Common Carotid Artery; ICA = Internal Carotid Artery; MCA-M1 = first segment of middle cerebral artery; MCA-M2 = second segment of the middle cerebral artery.

Figure 2 :

 2 Figure 2: Coregistered imaging data for the single patient in whom no-reflow was identified. The patient had sudden-onset severe hemiparesis (three levels) and global aphasia. On admission to our hospital, the neurological deficit had partly resolved and the patient had mild 3-level hemiparesis and non-fluent dysphasia (NIHSS=4). She was treated with thrombectomy 160min after symptom onset. (A-C) MRI-1 obtained 84min after symptom onset, showing a small, faint DWI lesion involving the posterior-most lentiform nucleus (A, arrow), an MCA-M1 occlusion on time-of-flight angiogram (B, yellow arrow) distal to the lenticulo-striate arteries (blue arrow), and on PWI (Tmax; the pseudo-colour scale ranges from 0 to 10s) an extensive area of severe hypoperfusion covering a large part of the left cortical MCA territory (yellow arrow), but sparing the deep MCA territory (white arrow) (C). (D) Digital subtraction arteriography obtained immediately following thrombectomy, showing mTICI 3 recanalization. (E-H) 24-hr MRI-2 showing extension of the DWI lesion to the entire caudate and lentiform nuclei (E), without re-occlusion on time-of-flight MRA (F). These newly infarcted areas are severely hypoperfused on 24hrs ASL-CBF maps (G; the pseudo-colour scale is in mLs/100g.min), fulfilling the operational criteria for no-reflow for both regions (CBF relative to mirror region: -63% and -55%, respectively). No hemorrhage was present across the MCA territory on T2* MRI-2 (H). A,C,E,G,H: Images coregistered using GE Volume Viewer (see Methods). Stroke etiology was atrial fibrillation. This patient enjoyed early neurological recovery (24-hr NIHSS=1) and excellent outcome (3-month mRS=0).

Figure 3 :

 3 Figure 3: Illustrative image dataset of the patient with mild hypoperfusion associated with infarction. This 66 year-old patient presented with acute-onset moderate deficit (NIHSS score = 11) and was treated with IV thrombolysis followed by thrombectomy for right terminal ICA occlusion, with mTICI 2c recanalization obtained 215min after symptom onset. Perfusion MRI was carried out prior to thrombolysis in this patient but unfortunately the data were not interpretable due to marked head motion and technical errors. (A-C) Illustrative DWI b=1000 sequences obtained i) on admission (68min after symptom onset) (A), and at 24hr follow-up (B and C), showing a stable DWI lesion in the striato-capsular area (i.e., deep MCA territory); (D-F) 24hr follow-up ASL-CBF maps, showing mild hypoperfusion within the DWI lesion (15 and 17% reduction in the caudate and lenticular nuclei, respectively, relative to contralateral ASPECTS regions; white arrows), but also cortically at distance from the DWI lesion (20-35% reduction relative to contralateral ASPECTS regions; red

  Abbreviations: IA= Intra-arterial; IV=Intravenous; t-PA=tissue plasminogen activator; SK= streptokinase; MT= mechanical thrombectomy; DSA= digital subtraction angiography; SPECT= Single-Photon Emission Computed Tomography; AOL= Arterial Occlusive Lesion score; TIMI= Thrombolysis in Myocardial Infarction score; mTICI= modified Thrombolysis in Cerebral Infarction score; PWI=Perfusion-Weighted Imaging; MRA= Magnetic Resonance Angiography; CTA= Computed Tomography Angiography; CTP= CT perfusion

Figure 1 599

 1 Figure 1

  Figure 5

Table 1 :

 1 Clinical and radiological characteristics. Values shown are numbers (percentages), unless stated otherwise.

	Variable

mRS=modified Rankin Scale, IQR=Interquartile range; NIHSS=National Institutes of Health Stroke Scale, DWI=Diffusion Weighted Imaging, ASPECTS=Alberta Stroke Program Early CT score, ICA=Internal Carotid Artery, ACA=Anterior Carotid Artery, MCA=Middle Cerebral Artery, M1=First segment of MCA; DSA: digital subtraction angiography. *Onset of symptoms: for stroke with unknown time, time when the patient was found symptomatic.

Table 3 : Previously published articles (listed in chronological order) on cerebral hypoperfusion in recanalized stroke patients, and present study.

 3 Methodological limitations that affect interpretation regarding the presence and reported rate of no-reflow appear in bold and italics.

	First author,	Acute		Stroke	Recanalization	Recanaliza-	Definition	Imaging	Reperfusion	Number	Number
	year	revasculari-	onset to	assessed with	tion	of	modality used	assessment	of	of
		zation		treatment		assessment	recanaliza	to assess	time	recanali-	recanali-
		treatment	delay		time	-tion	reperfusion		zed	zed
										patients	patients
											with
											hypoper-
											fusion (%)
	Baird, 1994 45	IA SK		4-24hrs	DSA	24hrs	Partial †	SPECT	24 hours	4	1 (25%)
							or
							complete
	Yasaka, 1998 46 IV SK or	< 4hrs	Transcranial	24hrs	Partial †	SPECT	24 hours	8	4 (50%)
		placebo			Doppler ‡		or
							complete
	Khatri, 2005 47	IV + IA t-PA	< 3hrs	DSA	On	Complete	DSA (TIMI)	On DSA	32	26 (81%)
						completion	(AOL III)		completion
						of DSA
	Albers, 2006 51	IV t-PA		3-6hrs	MRA ‡	3-6hrs after	Partial †	PWI	3-6 hours	19	4 (21%)
						t-PA	or		after t-PA
							complete
	DeSilva,2009 5	IV t-PA		3-6hrs	MRA ‡	3-5 days	Partial †	PWI	3-5 days	13	4 (31%)
							or
							complete
							(TIMI 2-3)
	Soares, 2010 48	IV t-PA ± MT,	< 6hrs	CTA ‡	24hrs	Partial †	CTP	24 hours	13	5 (38%)
		or	no				or
		treatment				complete
	Bivard, 2013 25	IV t-PA or no	< 6hrs	MRA ‡	24hrs	Complete	ASL	24 hours	48	0%
		treatment
	Eilaghi, 2013 6	IV t-PA or no	< 4.5hrs	CTA ‡	≤ 24hrs	Partial †	CTP	≤ 24 hours	59	14 (24%)
		treatment				or
							complete
							(TIMI 2-3)
	Horsch, 2015 49 IV t-PA or no	< 9hrs	CTA ‡	3 days	Complete	CTP	3 days	83	33 (40%)
		treatment
	Cho, 2015 7	IV t-PA or no	< 6hrs	MRA ‡	< 6hrs after	Partial †	PWI	< 6 hours of	13	0 (0%)
		treatment			stroke onset	or		symptoms
							complete		onset
							(AOL II-III)
	Carbone,	IV t-PA ± MT,	< 8hrs	CTA ‡	24hrs	Partial †	CTP	24 hours	39	15 (38%)
	2019 50	or	no				or
		treatment				complete
							(TIMI 2-3)
	Present study	MT ± t-PA		< 24hrs	DSA $	On	Complete
						completion	(mTICI 2c-
						of DSA	3)

Acknowledgements:

The authors are indebted to Ms Stéphanie Lion for expert technical assistance.

Funding: ATS received a scholarship from the Société Française Neurovasculaire (SFNV) to carry out this project as part of his MSc.

Author Contributions

AtS: conception and design of the study; acquisition and analysis of data; drafting of the manuscript and figures SC: analysis of data; drafting a significant portion of the manuscript WbH: data analysis CP: data analysis ON: acquisition and analysis of data JB: acquisition and analysis of data PS: data acquisition GT: data acquisition JCB: conception and design of the study; drafting of the manuscript and figures CO: conception and design of the study; data acquisition; drafting a significant portion of the manuscript and figures

Disclosures: None

Supplemental information can be found on the JCBFM website arrows), spreading beyond the ICA territory (i.e., posterior circulation, yellow arrows; note that in this patient both posterior cerebral arteries originated from the basilar artery on MRA, data not shown). The left cerebellar hemisphere, i.e., contralateral to the stroke, also shows mild hypoperfusion compared to the right cerebellum (purple arrow), reflecting crossed cerebellar diaschisis. The patient made a good recovery (24hr NIHSS score = 1, 3-month mRS = 1).

Final recanalization score

-mTICI 3 11 (33) Groin puncture to recanalization delay (min), median (IQR) 30 (16-46) Admission MRI to recanalization delay (min), median (IQR)

84 (55-101) Stroke onset* to recanalization delay (min), median (IQR)

(154-230)

Last-time-seen-well to recanalization delay (min), median (IQR)

(154-337)

Follow-up data

Recanalization to follow-up MRI delay (hours), median, IQR 24 (20-27) NIHSS at 24 hours

(1-9)

ΔNIHSS24h-initial

(5-14)

Early neurological improvement †

(79)

Early neurological deterioration ‡ 0 24-hr follow-up DWI-lesion volume (mL), median (IQR)

13 (6-32) Admission to follow-up DWI lesion volume change (mL), median (IQR)