Wen Wei
email: wenwei@inria.fr

Emilie Poirion

Benedetta Bodini

Stanley Durrleman

Olivier Colliot

Bruno Stankoff

Nicholas Ayache

Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Keywords: MR Images, FLAIR Synthesis, 3D Fully Convolutional Networks, Multiple Sclerosis, Deep Learning

Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Introduction

Multiple sclerosis (MS) is a demyelinating and inflammatory disease of the central nervous system and a major cause of disability in young adults. [START_REF] Compston | Multiple sclerosis[END_REF] MS has been characterized as a white matter (WM) disease with the formation of WM lesions, which can be visualized by magnetic resonance imaging (MRI). [START_REF] Paty | MRI in the diagnosis of MS: a prospective study with comparison of clinical evaluation, evoked potentials, oligoclonal banding, and CT[END_REF][START_REF] Barkhof | Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis[END_REF] The fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is commonly used clinically and in research for the detection of WM lesions which appear hyperintense compared to the normal appearing WM tissue (NAWM). Moreover, the suppression of the ventricular signal, characteristic of the FLAIR images, allows an improved visualization of the periventricular MS lesions, [START_REF] Woo | Detection of simulated multiple sclerosis lesions on t2-weighted and flair images of the brain: Observer performance[END_REF] and can also suppress any artifacts created by CSF. In addition, the decrease of the dynamic range of the image can make the subtle changes easier to see. Typical MRI pulse T1-w provides an anatomical reference and T2-w is used for WM lesions visualization. However, on the T2-w, periventricular lesions are often indistinguishable from the adjacent cerebrospinal fluid (CSF) which is also of high signal. WM lesions (red rectangles) characteristic of MS are best seen on FLAIR pulse sequence because of the suppression of the ventricular signal. Double inversion recovery (DIR) has direct application in MS for evaluating cortical pathology. Proton density (PD) and T1 spin-echo (T1SE) are also used clinically.

sequences used in a clinical setting are shown in Fig. 1. WM lesions (red rectangles) characteristic of MS are clearly best seen on FLAIR pulse sequences. However, in a clinical setting, some MRI pulse sequences can be missing because of limited scanning time or patients' interruptions in case of anxiety, confusion or severe pain. Hence, there is a need for predicting the missing FLAIR when it has not been acquired during patients' visits. FLAIR may also be absent in some legacy research datasets, that are still of major interest due to their number of subjects and long follow-up periods, such as ADNI. [START_REF] Mueller | The alzheimer's disease neuroimaging initiative[END_REF] Furthermore, the automatically synthesized MR images may also improve brain tissue classification and segmentation results as suggested in Refs. 6 and 7, which is an additional motivation for this work.

In Ref. 8, the authors proposed an atlas-based patch matching method to predict FLAIR from T1-w and T2-w. In this approach, given a set of atlas images (I T1 , I T2 , I FLAIR ) and a subject S with (S T1 , S T2 ), the corresponding FLAIR ŜFLAIR is formed patch by patch. A pair of patches in (S T1 , S T2 ) is extracted and used to find the most similar one in the set of patches extracted from the atlas (I T1 , I T2 ). Then the corresponding patch in I FLAIR is picked and used to form ŜFLAIR . In Ref. 9, random forests (RF) are used to predict FLAIR given T1-w, T2-w, and PD. In this approach, a patch at position i is extracted from each of these three input pulse sequences. All these three patches are then rearranged and concatenated to form a column vector X i . The vector X i and the corresponding intensity y i in FLAIR at position of i are used to train the RF. There are also some other close research fields doing subject-specific image synthesis of a target modality from another modality. For example, in Refs. 10 and 11, computed tomography (CT) imaging is predicted from MRI pulse sequences.

Recently, deep learning has achieved state-of-the-art results in several computer vision domains, such as image classification, [START_REF] He | Deep residual learning for image recognition[END_REF] object detection, [START_REF] Chen | Learning efficient object detection models with knowledge distillation[END_REF] segmentation [START_REF] Shelhamer | Fully convolutional networks for semantic segmentation[END_REF] and also in the fields of medical image analysis. [START_REF] Zhou | Deep Learning for Medical Image Analysis[END_REF] Various methods of image enhancement and reconstruction using a deep architecture have been proposed, for instance, reconstruction of 7T-like images from 3T MRI [START_REF] Bahrami | Convolutional neural network for reconstruction of 7t-like images from 3t mri using appearance and anatomical features[END_REF] and of CT images from MRI, [START_REF] Nie | Estimating ct image from mri data using 3d fully convolutional networks[END_REF] and prediction of positron emission tomography (PET) images with MRI. [START_REF] Li | Deep learning based imaging data completion for improved brain disease diagnosis[END_REF] The research work most similar to ours is Ref. 19. In this method, FLAIR is generated from T1-w MRI by a five-layer 2D deep neural network (DNN) which treats the input image slice-by-slice.

However, these FLAIR synthesis methods have their own shortcomings. The method in Ref. 8 breaks the input images into patches. During inference process, the extracted patch is then used to find the most similar patch in the atlas. But this process is often computationally expensive.

Moreover, the result heavily depends on the similarity between the source image and the images in the atlas. This makes the method fail in the presence of abnormal tissue anatomy since the images in the atlas do not have the same pathology. The learning based methods in Refs. 9 and 19 are less computationally intensive, because they store only the mapping function. However, they do not take into account the spatial nature of 3D images and can cause discontinuous predictions between adjacent slices. Moreover, many works used multiple MRI pulse sequences as the inputs, [START_REF] Roy | MR contrast synthesis for lesion segmentation[END_REF][START_REF] Jog | Random Forest FLAIR Reconstruction from T1, T2, and PD -Weighted MRI[END_REF] but none of them evaluated how each pulse sequence influences the prediction results.

In order to overcome the disadvantages mentioned above, we propose 3D Fully Convolutional Neural Networks (3D FCNs) to predict FLAIR. The proposed method can learn an end-to-end and voxel-to-voxel mapping between other MRI pulse sequences and the corresponding FLAIR. Our networks have three convolutional layers and the performance is evaluated qualitatively and quantitatively. Moreover, we propose a pulse-sequence-specific saliency map (P3S map) to visually measure the impact of each input pulse sequence on the prediction result.

Method

Standard convolutional neural networks are defined for instance in Refs. 20 and 21. Their architectures basically contain three components: convolutional layers, pooling layers, and fully-connected layers. A convolutional layer is used for feature learning. A feature at some locations in the image can be calculated by convolving the learned feature detector and the patches at those locations. A pooling layer is used to progressively reduce the spatial size of feature maps in order to reduce the computational cost and the number of parameters. However, the use of a pooling layer can cause the loss of spatial information, which is important for image prediction, especially the lesion regions. Moreover, a fully-connected layer has all the hidden units connected to all the previous units, so it contains majority of the total parameters and an additional fully-connected layer makes it easy to reach the hardware limits both in memory and computation power. Therefore, we propose fully convolutional neural networks composed of only three convolutional layers.

3D Fully Convolutional Neural Networks

Our goal is to predict FLAIR pulse sequences by finding a non-linear function s, which maps multipulse-sequence source images I source = (I T1 , I T2 , I PD , I T1SE , I DIR ), to the corresponding target pulse sequence I target . Given a set of source images I source , and the corresponding target pulse sequence I target , our method finds the non-linear function by solving the following optimization problem:

ŝ = arg min s∈S N i=1 (I i target , s(I i source )) 2 N ( 1
)
where S denotes a group of potential mapping functions, N is the number of subjects and meansquare-error (MSE) is used as our loss function which calculates a discrepancy between the predicted images and the ground truth.

In order to learn the non-linear function, we propose the architecture of our 3D fully convolutional neural networks shown in Fig. 2. The input layer is composed of the multi-pulse-sequence source images I source which are arranged as channels and then sent altogether to the network. Our network architecture consists of three convolutional layers (L = 3) followed by rectified linear functions (relu(x) = max(x, 0)). If we denote the m th feature map at a given layer as h m , whose filters are determined by the weights k m and bias b m , then the feature map h m is obtained as follows:

h m = max(k m * x + b m , 0) (2)
where the size of input x is H × W × D × M . Here, H, W, D indicate the height, width and depth of each pulse sequence or feature map and M is the number of the pulse sequences or feature maps. To form a richer representation of the data, each layer is composed of multiple feature maps {h m : 1, ..., F }, also referred as channels. Note that the kernel k has a dimension The proposed 3D fully convolutional neural networks.

H k ×W k ×D k ×M ×F
Our network architecture consists of three convolutional layers. The input layer is composed of 5 pulse sequences arranged as channels. The first layer extracts a 64-dimensional feature from input images through convolution process with a 3 × 3 × 3 × 5 × 64 kernel. The second and third layers apply the same convolution process to find a non-linear mapping for image prediction.

Pulse-sequence-specific Saliency Map (P3S Map)

Multiple MRI pulse sequences are used as inputs to predict FLAIR. Given a set of input pulse sequences and a target pulse sequence, we would like to assess the contribution of each pulse sequence on the prediction result. One method is class saliency visualization proposed in Ref. 22, which is used for image classification to see which pixels influence the most the class score. Such pixels can be used to locate the object in the image. We call the method presented in this paper pulse-sequence-specific saliency map to visually measure the impact of each pulse sequence on the prediction result. Our P3S map is the absolute partial derivative of the difference between the predicted image and the ground truth with respect to the input pulse sequence of subject i. It is calculated by standard backpropagation.

M i = ∂ I i target -Îi target 2 ∂I i source ( 3
)
where i denotes the subject, I target and Îtarget are the ground truth and the predicted image, respectively.

Materials and Implementation Details

Our dataset contains 24 subjects including 20 MS patients (8 women, mean age 35.1, sd 7.7) and 4 age-and gender-matched healthy volunteers (2 women, mean age 33, sd 5.6). Each subject underwent the following pulse sequences:

a) T1-w (1 × 1 × 1.1mm 3 )
b) T2-w and Proton Density (PD) (0.9 × 0.9 × 3mm 3 ) c) FLAIR (0.9 × 0.9

× 3mm 3 ) d) T1 spin-echo (T1SE, 1 × 1 × 3mm 3 ) e) Double Inversion Recovery (DIR, 1 × 1 × 1mm 3 )
All have signed written informed consent to participate in a clinical imaging protocol approved by the local ethics committee. The preprocessing steps include intensity inhomogeneity correction [START_REF] Tustison | N4itk: Improved n3 bias correction[END_REF] and intra-subject affine registration [START_REF] Greve | Accurate and robust brain image alignment using boundary-based registration[END_REF] onto FLAIR space. Finally, each preprocessed image has a size of 208 × 256 × 40 and a resolution of 0.9 × 0.9 × 3mm 3 .

Our networks have three convolutional layers (L = 3). The filter size is 3 × 3 × 3 and for every layer the number of the filters is 64 which is designed with empirical knowledge from the widely-used FCN architectures, such as ResNet. [START_REF] He | Deep residual learning for image recognition[END_REF] We used Theano [START_REF] Development | Theano: A Python framework for fast computation of mathematical expressions[END_REF] and Keras 26 libraries for both training and testing. The whole data is first normalized by using x = (x -mean)/std, where mean and std are calculated over all the voxels of all the images in each sequence. We do not use any data augmentation. Our networks were then trained using standard SGD optimizer with 0.0005 as the learning rate and 1 as the batch size. The stopping criteria used in our work is early stopping.

We stopped the training when the generalization error increased in p successive q-length-strips:

• ST OP p : stop after epoch t iff ST OP p-1 stops after epoch t -q and E ge (t) > E ge (t -q)

• ST OP 1 : stop after first end-of-strip epoch t and E ge (t) > E ge (t -q)

where q = 5, p = 3 and E ge (t) is the generalization error at epoch t. It takes 1.5 days for training and less than 2 seconds for predicting one image on a NVIDIA GeForce GTX TITAN X.

Our method is validated through a 5-fold cross validation in which the dataset is partitioned into 5 folds (4 folds have 5 subjects with 1 healthy subject in each fold and the last fold has 4 subjects). Subsequently 5 iterations of training and validation are performed such that within each iteration one different fold is held-out for validation and remaining four folds are used for training.

The validation error is used as an estimate of the generalization error. And then we compared it qualitatively and quantitatively with four state-of-the-art approaches : modality propagation, [START_REF] Ye | Modality propagation: Coherent synthesis of subjectspecific scans with data-driven regularization[END_REF] random forests (RF) with 60 trees, 9 U-Net, [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] and voxel-wise multilayer perceptron (MLP) which consists of 2 hidden layers and 100 hidden neurons for each layer, trained to minimize the mean squared error. The patch size used in modality propagation and RF is 3 × 3 × 3 as suggested in their works. [START_REF] Jog | Random Forest FLAIR Reconstruction from T1, T2, and PD -Weighted MRI[END_REF][START_REF] Ye | Modality propagation: Coherent synthesis of subjectspecific scans with data-driven regularization[END_REF] The U-Net architecture is separated in 3 parts: downsampling, bottleneck and upsampling. The downsampling path contains 2 blocks. Each block is composed of two 3 × 3 × 3 convolution layers and a max-pooling layer. Note that the number of feature maps doubles at each pooling, starting with 16 feature maps for the first block. The bottleneck is built from simply two 64-width convolutional layers. And the upsampling path also contains 2 blocks. Each block includes a deconvolution layer with stride 2, a skip connection from the downsampling path and two 3 × 3 × 3 convolution layers. Lastly, we use our pulse-sequence-specific saliency map to visually measure the contribution of each input pulse sequence.

3 Experiments and Results

Model Parameters and Performance Trade-offs

Number of Filters

Generally, the wider the network is, the more features can be learned so that the better performance can be obtained. Based on this, besides our default setting (F 1 = F 2 = F 3 = 64), we also did two experiments for comparison: (1) a wider architecture (F 1 = F 2 = F 3 = 96) and (2) a thinner architecture (F 1 = F 2 = F 3 = 32). The training process is the same as described in the section 2.3.

The results are shown in Table 1. We can observe that increasing the width of network from 32 to 64 leads to a clear improvement. However, increasing the filter numbers from 64 to 96 only slightly improved the performance. However, if less computational cost is needed, a thinner network which can also achieve a good performance is more suitable. 3. It can be found that when L = 2, the result is worse than our default setting (L = 3).

However, when we increased the number of layers to L = 4, it converges slower and finally to the same level as the 3-layer network. In addition, we also designed a much deeper network (L = 6)

by adding three more 64-width layers on our default setting (L = 3). It is shown in the Fig. 3 that the performance even dropped and failed to surpass the 3-layer network. The cause of this could be the complexity is increasing while the networks are going deeper. During the training process, it is thus more difficult to converge or falls into a bad minimum.

Evaluation of Predicted Images

Image quality is evaluated by mean square error (MSE) and structural similarity (SSIM). Table 2 shows the result of MSE and SSIM on 5-fold cross validation. Our method is statistically significantly better than the rest of the methods (p < 0.05) except for U-Net which got the best result on two folds for MSE and three folds for SSIM. However, the difference with our method is very small and we outperformed at the average level. Furthermore, the number of the parameters in U-Net is 375.6K which is much more than ours (213.7K). If less computational cost is needed, our As the network goes deeper, the result can be increased. However, deeper structure cannot always lead to better results, sometimes even worse.

method is preferred. To further evaluate the quality of our method, in particular on the MS lesions detection, we have chosen to evaluate the MS lesion contrast with the NAWM (Ratio 1) and the surrounding NAWM (Ratio 2), defined by a dilatation of 5 voxels around the lesions. Given the mean intensity of each region I i (R) of subject i, Ratio 1 and Ratio 2 are defined as:

Ratio 1 = 1 N N i=1 I i (Lesions) I i (N AW M ) , Ratio 1 = 1 N N i=1 I i (Lesions) I i (SN AW M ) (4)
As seen from Table 3, our method achieves statistically significantly better performances (p < 0.01) than other methods on both Ratio 1 and Ratio 2 which reflects a better contrast for MS lesions. The evaluation results can be visualized in Fig. 4 with the absolute difference maps on the 2nd and 4th rows. It can be observed that RF and U-Net can generate the good global anatomical information but the MS lesion contrast is poor. This can be truly reflected by a good MSE and SSIM ( See in Table 2), but a low Ratio 1/ 2 (See in Table 3). On the contrary, our method can well keep the anatomical information and also yield the best contrast for WM lesions. Moreover, we input the synthetic FLAIR and the ground truth to a brain segmentation pipeline [START_REF] Coupé | Lesionbrain: An online tool for white matter lesion segmentation[END_REF] to generate automatic segmentations of WM lesions. A similar segmentation should be obtained if the FLAIR synthesis is good enough and the DICE score is used to compare the overlap of the segmentations previously obtained from both the synthetic FLAIR and the ground truth. We got a very good WM lesion segmentation agreement with a mean (SD) DICE of 0.73(0.12). Some

Pulse-Sequence-Specific Saliency Map (P3S Map)

It can often happen that not all the subjects have the five complete protocols (T1-w, T2-w, T1SE, PD, and DIR). Therefore, it might be useful to measure the impact of each input pulse sequence.

Our proposed P3S map is to visually measure the contribution of each input pulse sequence. It can be observed in Fig. 6 that T1-w, DIR, and T2-w contribute more for FLAIR MRI prediction than PD or T1SE. In the P3S map, the intensity reflects the contribution of each input pulse sequence.

In particular, from the P3S map we can easily find which sequence affects more the generation of which specific ROIs. For example, as shown in the first row of Fig. 6, even though generally DIR is the most important sequence (see Table 4(a)), T1-w contributes more for the synthesis of ventricle which can be proved by the high degree of resemblance of ventricle between T1-w and FLAIR (see 2nd row of Fig. 6).

In order to test our P3S Map, five experiments have been designed. In each one, we removed one of the five pulse sequences (T1-w, T2-w, T1SE, PD, and DIR) from the input images. Table 4(a) shows the testing result on 5-fold cross validation by using MSE as the error metric. As shown in the table, these results are consistent with the observation revealed by our P3S map. The The first row is the saliency maps for T1, T1SE, T2, PD, and DIR, respectively. And the second row is the corresponding multi-sequence MR images. It can be found that T1-w, DIR, and T2-w contribute more for FLAIR MRI prediction than PD or T1SE.

DIR, T1-w and T2-w contribute more than T1SE and PD. In particular, DIR is the most relevant pulse sequences for FLAIR prediction. However, DIR is not commonly used in clinical settings.

We thus show a performance comparison between other methods in Table 4(b). It can be observed that when DIR is missing, the performance decreases for all the methods suggesting a high similarity between DIR and FLAIR. In addition, even though DIR is not such common, we still got an acceptable result for FLAIR prediction without DIR.

Besides, some legacy research datasets do not have T1SE or PD, we thus predicted FLAIR from different combinations of T1, T2, DIR and PD (see in Table 4(c) and Fig. 7). It indicates that our method can be used to get an acceptable predicted FLAIR from the datasets which only contain some sequences. From Table 4(c) we can also infer that adding a pulse sequence improves the prediction result. Shown are synthesized FLAIR with different MRI pulse sequences as inputs from T1+DIR to T1+T2+PD. A better performance can be achieved when both DIR and T1 exist.

Discussion and Conclusion

We introduced 3D fully convolutional neural networks for FLAIR prediction from multiple MRI pulse sequences, and a sequence-specific saliency map for investigating each pulse sequence contribution. Even though the architecture of our method is simple, the nonlinear relationship between the source images and FLAIR can be well captured by our network. Both the qualitative and quantitative results have shown its competitive performance for FLAIR prediction. Compared to previous methods, representative patches selection is not required so that this speeds up the training process.

Additionally, 2D Convolutional Neural Networks (2D CNNs) become popular in computer vision, however they are not suitable to directly use 2D CNNs for volumetric medical image data. Unlike

Refs. 19 and 9, our method can better keep the spatial information between slices. Moreover, the generated FLAIR has a good contrast for MS lesions. In practice, in some datasets, not all the subjects have all the pulse sequences. Our proposed P3S map can be used to reflect the impact of each input pulse sequence on the prediction result so that the pulse sequences which contribute very little can be removed. Furthermore, DIR is often used for the detection of MS cortical gray matter lesions and if we have DIR, we can use it to generate FLAIR so that the acquisition time for FLAIR can be saved. Also, our P3S map can be generated by any kinds of neural networks trained by standard backpropagation.

Our 3D FCNs have some limitations. The synthetic images appear slightly more blurred and smoother than the ground truth. This maybe because we use a more traditional loss L2 distance as our objective function. As mentioned in Ref. 30, the use of L1 distance can encourage less blurring and generate sharper image. Additionally, the proposed P3S is generated after the data normalization which may affect the gradient. However, the network is changed as the normalization strategy changes. And the saliency map is based on the network. Moreover, the dataset should be ideally partitioned into training-validation-test sets. However, our dataset only has 24 subjects which is quite small to split into training-validation-test set. Instead, we divided it into training-testing set and the testing error is used as an estimate of the generalization error.

In the future, it would be interesting to also assess the utility of the method in the context of other WM lesions (e.g. age-related WM hyperintensities). Specifically, FLAIR is the pulse sequence of choice for studying different types of white matter lesions, [START_REF] Koikkalainen | Differential diagnosis of neurodegenerative diseases using structural mri data[END_REF] including leucoaraiosis (due to small vessel disease) that is commonly found in elderly subjects, that is associated to cognitive decline and is a common co-pathology in neurodegenerative dementias.

Disclosures

Authors have no conflict of interest regarding this article.

Fig 1 :

 1 Fig 1: MRI pulse sequences usually used in a clinical setting. T1-w provides an anatomical reference and T2-w is used for WM lesions visualization. However, on the T2-w, periventricular lesions are often indistinguishable from the adjacent cerebrospinal fluid (CSF) which is also of high signal. WM lesions (red rectangles) characteristic of MS are best seen on FLAIR pulse sequence because of the suppression of the ventricular signal. Double inversion recovery (DIR) has direct application in MS for evaluating cortical pathology. Proton density (PD) and T1 spin-echo (T1SE) are also used clinically.

  where H k , W k , D k are the height, width and depth of the kernel respectively. The kernel k operates on x with M channels, generating h with F channels. The parameters k, b in our model can be efficiently learned by minimizing the function 1 using stochastic gradient descent (SGD).

Fig 2 :

 2 Fig 2: The proposed 3D fully convolutional neural networks.Our network architecture consists of three convolutional layers. The input layer is composed of 5 pulse sequences arranged as channels. The first layer extracts a 64-dimensional feature from input images through convolution process with a 3 × 3 × 3 × 5 × 64 kernel. The second and third layers apply the same convolution process to find a non-linear mapping for image prediction.

Fig 3 :

 3 Fig 3: Comparison of Different Number of Layers. Shown are learning curves for different number of layers (L = 2, 3, 4, 6).As the network goes deeper, the result can be increased. However, deeper structure cannot always lead to better results, sometimes even worse.

Fig 4 :

 4 Fig 4: Qualitative comparison of the methods to predict FLAIR sequence.Shown are synthetic FLAIR obtained by RF with 60 trees, MLP, U-Net, and our method followed by the true FLAIR. The 2nd and 4th rows show the absolute difference maps between each synthetic FLAIR and the ground truth.

Fig 5 :

 5 Fig 5: Examples of WM lesion segmentation for a high and a low DICE. The WM lesions are very small and diffuse, so even a slight difference in the overlap can cause a big decrease for the DICE score. (a)(c) True FLAIR. (e)(g) Predicted FLAIR. (b)(d) Segmentation of WM lesions (red) using true FLAIR. (f)(h) Segmentation of WM lesions using predicted FLAIR.

Fig 6 :

 6 Fig 6: Pulse-Sequence-Specific Saliency Maps for input pulse Sequences.The first row is the saliency maps for T1, T1SE, T2, PD, and DIR, respectively. And the second row is the corresponding multi-sequence MR images. It can be found that T1-w, DIR, and T2-w contribute more for FLAIR MRI prediction than PD or T1SE.

Fig 7 :

 7 Fig 7: Different Combinations of T1, T2, DIR and PD as input sequences. Shown are synthesized FLAIR with different MRI pulse sequences as inputs from T1+DIR to T1+T2+PD. A better performance can be achieved when both DIR and T1 exist.

  applied to neurological disease, in particular Multiple Sclerosis. Benedetta Bodini, MD-PhD, is an associate professor of neurology at Sorbonne-Université in Paris. She is part of the research team "Repair in Multiple Sclerosis, from basic science to clinical translation", at the Brain and Spinal Cord Institute (ICM) in Paris, where she studies the mechanisms underlying the pathogenesis of multiple sclerosis using MRI and molecular imaging. Stanley Durrleman, PhD, is the co-head of joint Inria/ICM Aramis Lab at the Brain and Spine Institute (ICM) in Paris. He is also the coordinator of the ICM Center of Neuroinformatics, and scientific director of the ICM platform of biostatistics and bioinformatics. His team focuses on the development of new statistical and computational approaches for the analysis of image data and image-derived geometric data such a surface meshes. Olivier Colliot, PhD, is a Research Director at CNRS and the head of the ARAMIS Laboratory, a joint laboratory between CNRS, Inria, Inserm and Sorbonne University within the Brain and Spine Institute (ICM) in Paris, France. He received the PhD in Signal Processing from Telecom ParisTech in 2003 and the Habilitation degree from University Paris-Sud in 2011. His research interests include machine learning, medical image analysis and their applications to neurological disorders. Bruno Stankoff, MD-PhD, is a professor of neurology in Sorbonne-Université in Paris. He leads the Multiple Sclerosis (MS) center of Saint Antoine Hospital (AP-HP), and co-lead the research team entitled "Repair in Multiple Sclerosis, from basic science to clinical translation", at the Brain and Spinal Cord Institute (ICM). He is member of the ECTRIMS executive committee, MAGNIMS

Table 1 :

 1 Comparison of Different Number of FiltersIt is indicated in Ref. 12 that neural networks could benefit from increasing the depth of the networks. We thus tested two different number of layers by adding or removing a 64-width layer

		MSE (SD)	Number of Parameters	Inference Time (sec)
	F 1 = F 2 = F 3 = 32 1094.52 (49.46)	60.6 K	0.72
	F 1 = F 2 = F 3 = 64	918.07 (41.70)	213.7 K	1.34
	F 1 = F 2 = F 3 = 96	909.84 (38.68)	513.5 K	2.58
	3.1.2 Number of Layers

based on our default setting (L = 3), i.e. (1) L = 2 and (2) L = 4. The comparison result is shown in Fig.

Table 2 :

 2 Quantitative comparison between our method and other methods

			(a) Mean Square Error (Standard Deviation)
		Random Forest 60	Modality Propagation	Multilayer Perceptron	U-Net	Our Method
	Fold 1	993.68 (67.21)	2194.79 (118.73)	1532.89 (135.82)	921.69 (38.51)	905.05 (26.06)
	Fold 2	1056.76 (125.51)	2037.69 (151.23)	1236.53 (100.95)	912.03 (38.58)	913.34 (39.95)
	Fold 3	945.38 (59.42)	1987.32 (156.11)	1169.78 (142.43)	916.16 (38.97)	898.76 (46.90)
	Fold 4	932.67 (74.48)	2273.58 (217.85)	1023.35 (97.93)	938.34 (52.54)	945.33 (63.80)
	Fold 5	987.63 (78.34)	1934.25 (140.06)	1403.57 (146.35)	908.11 (36.13)	927.88 (31.80)
	Average	983.22 (80.99)	2085.53 (156.80)	1273.22 (124.70)	919.26 (40.95)	918.07 (41.70)
			(b) Structural Similarity (Standard Deviation)
		Random Forest 60	Modality Propagation	Multilayer Perceptron	U-Net	Our Method
	Fold 1 0.814 (0.044) 0.727 (0.044)	0.770 (0.052) 0.847 (0.038) 0.868 (0.036)
	Fold 2 0.822 (0.038) 0.718 (0.045)	0.773 (0.045) 0.856 (0.025) 0.854 (0.028)
	Fold 3 0.832 (0.040) 0.713 (0.047)	0.790 (0.044) 0.854 (0.036) 0.880 (0.031)
	Fold 4 0.850 (0.032) 0.708 (0.049)	0.786 (0.044) 0.853 (0.031) 0.846 (0.035)
	Fold 5 0.830 (0.041) 0.723 (0.039)	0.781 (0.047) 0.861 (0.034) 0.850 (0.027)
	Average 0.830 (0.039) 0.718 (0.045)	0.780 (0.046) 0.854 (0.033) 0.860 (0.031)

Table 3 :

 3 Evaluation of MS lesion contrast (Standard Deviation)

	Random Forest 60	Modality Propagation	Multilayer Perceptron	U-Net	Our Method	Ground Truth
	Ratio 1 1.33 (0.07)	1.31 (0.06)	1.39 (0.11) 1.34 (0.09) 1.47 (0.13) 1.66 (0.12)
	Ratio 2 1.15 (0.04)	1.13 (0.04)	1.20 (0.05) 1.17 (0.04) 1.22 (0.07) 1.33 (0.09)

Table 4 :

 4 FLAIR prediction results by using different input pulse sequences (a) Mean Square Error (Standard Deviation)

				Removed Pulse Sequence
			T1	T1SE	T2	PD	DIR
		Fold 1	959.75 (60.58)	926.89 (73.25)	981.15 (83.45)	945.79 (67.23)	1097.99 (93.27)
		Fold 2	987.13 (91.47)	940.00 (86.34)	994.47 (78.47)	919.09 (69.82)	1097.00 (98.57)
		Fold 3	942.76 (59.22)	938.98 (64.27)	940.92 (69.44)	924.59 (61.39)	1065.08 (101.95)
		Fold 4	999.64 (100.57)	940.56 (72.98)	939.60 (76.22)	932.46 (59.49)	1151.93 (113.21)
		Fold 5	986.55 (71.25)	936.89 (63.23)	953.35 (70.12)	933.12 (65.23)	1068.72 (98.56)
		Average	975.16 (76.62)	936.67 (72.00)	961.90 (75.54)	931.01 (64.63)	1096.14 (101.11)
		(b) Performance Comparison by removing DIR (Standard Deviation)
		Random Forest 60	Multilayer Perceptron	U-Net	Our Method
	Fold 1	1035.17 (102.37)	1589.62 (131.32) 1068.59 (100.28) 1097.99 (93.27)
	Fold 2	1167.52 (127.67)	1375.28 (121.12) 998.66 (106.79)	1097.00 (98.57)
	Fold 3	1170.36 (105.37)	1316.53 (128.46) 1135.24 (128.15) 1065.08 (101.95)
	Fold 4	1218.38 (129.01)	1235.26 (117.26) 1175.68 (107.33) 1151.93 (113.21)
	Fold 5	1189.64 (108.28)	1537.61 (135.78) 1003.54 (95.18)	1068.72 (98.56)
	Average 1156.21 (114.54)	1410.86 (126.79) 1076.34 (107.55) 1096.14 (101.11)
			(c) Mean Square Error (Standard Deviation)
				Input Pulse Sequences
			T1+DIR T2+DIR T1+T2 T1+T2+DIR T1+T2+PD
		Fold 1	966.67 (70.12)	993.25 (99.35)	1375.83 (123.68)	926.88 (83.68)	1281.06 (112.57)
		Fold 2	953.87 (68.57)	974.88 (86.32)	1562.46 (132.68)	944.39 (79.23)	1324.17 (121.37)
		Fold 3	998.71 (84.90)	1007.69 (103.87)	1158.65 (112.29)	961.19 (71.68)	1261.68 (128.91)
		Fold 4	973.24 (77.79)	998.56 (98.23)	1078.67 (103.89)	931.47 (69.31)	1143.58 (98.95)
		Fold 5	968.55 (71.59)	986.57 (91.33)	1212.59 (126.79)	958.28 (73.45)	1156.79 (102.67)
		Average	972.21 (74.60)	992.19 (95.82)	1277.64 (119.87)	944.44 (75.47)	1233.46 (112.89)

Acknowledgments

The first author is funded by an Inria fellowship. The research leading to these results has received funding from the program "Investissements d'avenir" ANR-10-IAIHU-06 (Agence Nationale de la

List of Figures

Pulse-Sequence-Specific Saliency Maps for input pulse Sequences.

The first row is the saliency maps for T1, T1SE, T2, PD, and DIR, respectively.

And the second row is the corresponding multi-sequence MR images. It can be found that T1-w, DIR, and T2-w contribute more for FLAIR MRI prediction than PD or T1SE.

7

Different Combinations of T1, T2, DIR and PD as input sequences.

Shown are synthesized FLAIR with different MRI pulse sequences as inputs from T1+DIR to T1+T2+PD. A better performance can be achieved when both DIR and T1 exist.

List of Tables


REMERCIEMENTS

Au jury

Mon président du jury, Monsieur le Professeur Mehdi Mejdoubi Vous me faites l'honneur d'assurer la présidence de mon jury de thèse. Merci pour votre enseignement, votre présence et vos conseils au cours de ces années.

Monsieur le Professeur Emmanuel Houdart

Je vous remercie d'accepter de juger mon travail. Je vous remercie également pour ces 6 mois passés dans votre service à l'Hôpital Lariboisière à la fois pour l'accueil, l'ambiance, votre patience et vos enseignements. Je vous en suis très reconnaissante.

Madame le Professeur Annie Lannuzel

Vous me faites aujourd'hui l'honneur de juger cette thèse. Je vous en suis grandement reconnaissante.

Monsieur le Professeur Michel Debandt

Je vous remercie pour votre accueil lors de mon passage en stage

Mon directeur de thèse, Monsieur le Docteur Marc-Antoine Labeyrie

Je te remercie de m'avoir proposé ce sujet, de m'avoir encadrée, de ta gentillesse et de t'être montré si disponible malgré tout ce que tu avais à faire et ce même pendant tes vacances. Merci d'avoir accepté d'être mon directeur de thèse.

Madame le Docteur Pascale Poullain

Je te remercie pour ton enseignement, tes staffs neurologues/ radiologues, ta gentillesse et ta grande disponibilité même en dehors des heures de travail. Sois assurée de mon A Fanny VO DIEP, ma bonne amie de la Fac qui m'aura suivie jusqu'en Guadeloupe.

Merci pour toutes ces années, ta constante bonne humeur, ton rire ravageur, nos discussions interminables, ta grande gentillesse et ton amitié.

A mes grandes copines d'internat, Vanessa Cingala, Adeline Piquot, Liza Valls et

Pauline Simon, qui ont été ma famille aux Antilles. Je vous remercie pour votre bonne humeur, disponibilité à toute épreuve, votre sentiment fraternel et pour toutes nos aventures.

A Marie-Anne Berthelin, alias MAB, ma Cristina. Mon internat aurait été bien vide sans toi. Je suis bien triste de te quitter dans quelques mois mais je sais que nous resterons toujours aussi proches. Merci de ton amitié.

A mes co-internes de radiologie, alias la famille radio alias la "golden generation", CT angiography (CTA) is commonly used in aneurysmal subarachnoid hemorrhage (aSAH) to monitor the risk of delayed cerebral ischemia (DCI). However, its accuracy is still debated in clinical practice. We aim to assess sensitivity, specificity and radiation dose of CTA and digital subtraction angiography (DSA) to compute CVS.

Mathieu (Monsieur

Methods-Consecutive patients with aSAH who underwent both CTA and DSA within less than 6 hours at admission / during CVS period (within the 3 rd / 4 th -21 st days after aSAH onset) were included. CVS was retrospectively computed on CTA and DSA for each anterior intracranial artery segment until the end of their 2 nd segments by 2 readers (reader 1 / reader 2; 5-year / 10-year experienced radiologist / interventional neuroradiologist) in blinded conditions. Radiation dose to the eye and scalp were also compared prospectively in 1 phantom study and in 5 patients.

Results-CVS was computed on a total of 210 arterial segments in 20 patients. The correlation between CTA and DSA was better for the reader 2, CTA performed under our local protocol, CTA with great visualization of distal arteries and segments without metallic artefacts. The best sensitivity and specificity of CTA to predict CVS ≥ 50% on DSA was obtained for a threshold ≥ 30% for the reader 2 (86% and 86% respectively).

Under this value, no patient had CVS ≥ 50% on DSA. Radiation dose to the scalp and eye were respectively lower and tends to be higher for CTA compared to DSA.

INTRODUCTION

Cerebral vasospasm (CVS) is a major provider of morbidity and mortality in patients hospitalized with aneurysmal subarachnoid hemorrhage (aSAH) [START_REF] Rao | Cerebral vasospasm: current understanding[END_REF] . The involvement of large vessel CVS in the occurrence of delayed cerebral ischemia (DCI) is debated [START_REF] Dankbaar | Relationship between vasospasm, cerebral perfusion, and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage[END_REF] . Some authors propose the analysis of microcirculation by computed tomography perfusion (CTP) rather than the severity of CVS to determine the risk of DCI. However, no study concludes to the superiority of one or the other approaches [START_REF] Greenberg | Diagnostic Accuracy of CT Angiography and CT Perfusion for Cerebral Vasospasm: A Meta-Analysis[END_REF] .The analysis of CVS therefore remains a recognized major marker of the DCI risk. In addition, the quantification of CVS is a key element for angioplasty decision making with a threshold usually of 50%.

The gold standard to confirm CVS remains digital subtraction angiography (DSA). But, it is costly, invasive, time-consuming and requires the displacement of fragile patients in the operating room. Neurologic complications are not uncommon (0.33 to 1.3% of cases including 0.5% of permanent neurological deficits) [START_REF] Leffers | Neurologic complications of cerebral angiography. A retrospective study of complication rate and patient risk factors[END_REF][START_REF] Heiserman | Neurologic complications of cerebral angiography[END_REF][START_REF] Earnest | Complications of cerebral angiography: prospective assessment of risk[END_REF] . Transcranial Doppler (TCD) ultrasound is a non-invasive, non-ionising and inexpensive technique that can be performed at the bedside. Nevertheless, because of its limited acoustic window and several factors influencing blood velocity, 7,8 many false negative results need to be considered, especially in the anterior brain territory or for CVS affecting distal segments of cerebral arteries [START_REF] Lysakowski | Transcranial Doppler Versus Angiography in Patients With Vasospasm due to a Ruptured Cerebral Aneurysm: A Systematic Review[END_REF][START_REF] Mascia | The accuracy of transcranial Doppler to detect vasospasm in patients with aneurysmal subarachnoid hemorrhage[END_REF] . As for it, CT angiography (CTA) has been studied in many articles [START_REF] Hébert | 320-Row Multidetector CT Angiography in the Detection of Critical Cerebrovascular Anomalies[END_REF][START_REF] Hébert | 320-Row Multidetector Computed Tomographic Angiogram in the Evaluation of Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study[END_REF][START_REF] Otawara | Evaluation of vasospasm after subarachnoid hemorrhage by use of multislice computed tomographic angiography[END_REF][START_REF] Anderson | CT angiography for the detection of cerebral vasospasm in patients with acute subarachnoid hemorrhage[END_REF][START_REF] Shankar | CT angiography for evaluation of cerebral vasospasm following acute subarachnoid haemorrhage[END_REF][START_REF] Yoon | Multidetector-row CT angiography of cerebral vasospasm after aneurysmal subarachnoid hemorrhage: comparison of volume-rendered images and digital subtraction angiography[END_REF][START_REF] Goldsher | Role of Multisection CT Angiography in the Evaluation of Vertebrobasilar Vasospasm in Patients with Subarachnoid Hemorrhage[END_REF][START_REF] Chaudhary | Prospective Evaluation of Multidetector-Row CT Angiography for the Diagnosis of Vasospasm following Subarachnoid Hemorrhage: A Comparison with Digital Subtraction Angiography[END_REF][START_REF] Binaghi | CT Angiography and Perfusion CT in Cerebral Vasospasm after Subarachnoid Hemorrhage[END_REF] . A recent meta-analysis found out excellent sensitivity and specificity, comparable to CTP 3 . However, imaging protocols used in these studies were different.

Analysis was based on qualitative or semi-quantitative criteria rather than quantitative ones, on examinations too widely spaced in time whereas CVS is a rapidly evolving phenomenon, or on too proximal arterial segments while more distal involvement seems to also play a role in the occurrence of DCI. Therefore, the clinical interest of CTA seemed uncertain to us, mostly for angioplasty decision.

The aim of our study was to show a correlation between CTA and DSA in the determination of CVS and thus the angioplasty decision by a quantitative analysis of CVS till the 2 nd segments of cerebral arteries in quasi-synchronous examinations. We also aimed to evaluate radiation implications in the angioplasty decision.

METHODS

Population

From a prospective data base of consecutive patients hospitalized in our center between January 2015 and December 2016 at acute phase of aneurysmal subarachnoid hemorrhage (aSAH), with a proven Fischer score ≥ 2, we included patients who underwent both CTA and DSA twice. The first set was performed prior aneurysm treatment and within the 3 rd day of aSAH onset and the second one between the 4 th and the 21th day from aSAH onset.

Each CTA had to precede DSA within a delay of less than 6 hours and no curative medical or endovascular treatment was initiated or stopped during this period.

Non-invasive bedside monitoring of DCI was done using an every 2-hours neurological examination and measurement of middle cerebral artery velocity by TCD twice -a -day.

CTA or directly DSA were performed when DCI was suspected on bedside monitoring. CTP was performed in rare cases when CTA was not interpretable. Intensive medical care and endovascular preventive treatment of DCI were systematically considered in patients with CVS on DSA (CVSDSA) ≥ 50% and if DCI was suspected. Patients or their relatives were informed and provided their consent for all the care concerning aSAH. The study was approved by our local Ethics Committee.

Imaging protocol

We used a 64-section multidetector CT-scan, Philips Brillance (Philips Medical System, Amsterdam, Netherlands) and Siemens Definition Edge CT-scan (Siemens Healthineers, Germany) since September 2016. Acquisition parameters were as follows: 300mA; 120 kV; matrix, 512 x 512; field of view, 28-30 cm; section thickness, 1.5 mm on average; reconstruction interval, 1 mm; pitch, 0.55 mm. A biphasic intravenous brachial injection of 60 cc of contrast material (IOMERON 400, Iomeprol, Bracco Imaging, Milan, Italy) followed by 50 cc of saline was administrated with an injection speed of 4 mL/s. Helix acquisition was launched as soon as contrast enhancement was detected within skull base arteries, with a bolus-tracking method. Reconstructions of initial raw images were produced in 1 to 5 mm thin 3D and 3D MIP and co-registered with per-CVS CTA on processing console (PACS Carestream Health, France).

All patients had a DSA (biplane neuroangiography unit Artis Zee, Siemens Healthineers, France) under local or general anesthesia with standard frontal view angiograms of both internal carotid artery (ICA) and dominant vertebral artery (VA) territories using OMNIPAQUE 320 (Iohexol, GE Healthcare, Chicago, USA) as contrast material.

Imaging analysis

CVS was computed on 14 segments of bilateral anterior intracranial circulation including: cavernous internal carotid artery (CICA), terminal internal carotid artery (TICA), middle cerebral artery (MCA) with is first segment (M1) and second segment (M2) anterior and posterior, anterior cerebral artery (ACA) with its first (A1) and second segment (A2).

between the second and the first vascular imaging.

CVS on CTA (CVSCTA) was assessed independently by two observers: a 5-year of experience in diagnostic radiology with 1 year of neuroradiology training (reader 1) and a 10-year experienced in interventional neuroradiology (reader 2). Interpretation was blinded between readers and so were DSA data. A training period on analysis process was performed in consensus by the two readers with 10 patients. The different steps of analysis are shown in Figure 1. To limit partial volume artefacts on curved segments, each segment was analyzed in an oblique plane to unroll this segment. To limit crossvessel sections in MIP reconstruction, thickness on MIP reconstruction may have been decreased from 5 to 2 mm. When "beads on a string" artefact was seen on a vessel segment, diameter was considered as mean value between the minimal and maximal diameter. In case of arterial trifurcation, the dominant artery was chosen. In case of metalinduced susceptibility artifacts, we aim to determine the diameter of the segment on its visible part. The presence of such artefacts was analyzed for each segment. CTA quality criterion was defined qualitatively such as the presence or absence of visibility of 3 rd segments of cerebral arteries.

CVS on DSA (CVSDSA) was computed by 2 readers in consensus blinded to CTA data on frontal (M1, M2, A1) and lateral (TICA, A2) view angiograms (Figure 2).

Radiation dose

Radiation effective dose was assessed prospectively on 1 acrylic head phantom (with RaySafe Xi CT detector) and 5 patients using 3 optically stimulated luminescence dosimeters (OSL) (Nanodot, Landauer, Glenwood, USA) put on the left eye, on the left temporal region and on the occiput bone. CTA data was compared to DSA results (3 lateral and frontal series).

Statistical analysis

The interrater correlation between reader 1 and reader 2, the inter test correlation between CTA and DSA, the influence of CTA protocol, CTA quality and the location of arterial segments to assess CVS were analyzed for each arterial segment using linear regression, comparison of mean absolute differences of CVS values using a paired sample t-test, Pearson correlation, kappa correlation for dichotomized CVS with a threshold ≥ 50% and ROC analysis for a dichotomized CVSDSA with a threshold ≥ 50%. This final analysis was also done for each patient considering arterial segment with the most severe CVS.

Radiation dose was described as mean value (SD) and compared using a paired sample ttest.

We used SPSS 19 for statistical analysis. A p-value ≤0.05 was considered significant.

RESULTS

Population

A total of 156 consecutive patients with aSAH were screened during this study period.

CVS was computed in 20/156 patients (13%) following inclusion criteria. The main characteristics of these patients are presented in Table 1.

The median age was 47 years old. Patients were mostly women (65%). 85% of patients underwent aneurysmal treatment. They were in serious clinical condition, hence their hospitalization in intensive care with a WFNS scale > 4 in 40% of cases and a Fischer score = 4 in 80% of cases (Annex 1).

Exams

Initial CTA was performed on admission. CTA performed at the time of CVS was performed on the 6 th day from the aSAH onset (median; IC 95% [5-8 days]). 95% of CTA during CVS were performed according to our local protocol versus 50% among initials CTAs.

Median delay between CTA and DSA were of 2 hours for initial exams and of 3 hours for those occuring during CVS.

Arterial analysis

210 arterial segments were finally analyzed. 30 segments were excluded due to absence of DSA.

Correlations

Correlation of CVS values for each arterial segment between CTA_reader 1, and CTA_reader 2 are presented in Figure 3 and Table 2. The correlation between CTA and DSA was better for reader 2 (Pearson; 0,805 vs 0,525 for reader 1), for CTA performed under our local protocol (Pearson; 0,847 vs 0,751), for CTA with great visualization of distal arteries and segments without metallic artefacts.

ROC curve analysis

With ROC curve analysis, the best sensitivity and specificity of CTA to predict a CVS≥50% on DSA was obtain for a threshold ≥ 30% (respectively 86% and 86% for reader 2 vs 73% and 71% for reader 1). In per-patient analysis, this threshold was CVS≥40% for reader 2 (respectively 87% and 100%) and ≥ 35% for reader 1 (respectively 78% and 50%). Maximal sensitivity was obtained for a threshold of CVS ≥ 30% for reader 2 (respectively 100% and 50%) and CVS≥20% for reader 1 (respectively 100% and 25%).

Radiation dose

Radiation dose is presented in Table 3. CTA was associated to a lower radiation dose for skin (11,5 and 17,2 versus respectively 98 and 76 x 10 -3 Sv; p = 0,02 and 0,04) but tends to be associated to a higher radiation dose for eyes (13,9 versus 7 x 10 -3 Sv; p = 0,13) compared to DSA.

We also simulated 3 possible prevalence of CVS on DSA (CVSDSA) ≥ 50%: 25%, 50% and 75% on Table 4.

As prevalence of CVSDSA ≥ 50% increases, indication to perform DSA increases too but so does the radiation dose.

DISCUSSION

Our study is one of the first to provide an assessment of the correlation between DSA and CTA in clinical practice (namely comparing before and after CVS), to include an analysis till the 2 nd segments of cerebral arteries and to evaluate CVS quantitatively.

First, it suggests that CTA interpretation is difficult and operator dependent. There is indeed a great variability in the correlation between CTA and DSA according to the experience of the observer. The correlation was better for the most experienced reader (0.805 versus 0.525). Interpretation can therefore improve with practice same as the average analysis time which was quite long for CTA in our study.

Then, our study also highlights the importance of image analysis strategies. A systematic analysis of each segment is essential because DCI can occur electively on distal CVS [START_REF] Brami | Delayed cerebral infarction is systematically associated to a severe cerebral vasospasm of large intracranial vessels[END_REF] .

Therefore, it is important to compare the initial exam with the one during CVS to fully understand arterial anatomy. Morphology of arteries is usually easier to evaluate on initial examination blank of any treatment. Maximum image magnification is also needed to optimize measurements. If needed, oblique plane reconstructions of the analyzed artery helps to unroll the segment in MIP images, particularly for short stenosis. Window setting adaptation that does not dull the edge of vessels is essential in order not to underestimate the actual diameter of the vessel. Indeed, windowing (width, level) has a major effect.

Hence, a change in the visualization of a vessel whose edges will be inadequately defined [START_REF] Bash | Intracranial Vascular Stenosis and Occlusive Disease: Evaluation with CT Angiography, MR Angiography, and Digital Subtraction Angiography[END_REF][START_REF] Hoe | Assessment of accuracy of renal artery stenosis grading in helical CT angiography using maximum intensity projections[END_REF] . Adequate settings become even more relevant for low-contrast CTA or CTA with coils-related artifacts. Therefore, imaging should be interpreted by a skilled neuroradiologist with experience in this type of pathology.

Our study also suggests that reliability of CTA to determine CVS is not perfect even for an experienced observer. By privileging sensitivity (because it is a screening test), specificity in our study is relatively mediocre (in the order of 50%). There are several causes to explain certain limitations of CTA.

First, CTA quality is critical for its interpretation. This quality may depends on acquisition protocol, which is relatively standardized in the literature [START_REF] Shankar | CT angiography for evaluation of cerebral vasospasm following acute subarachnoid haemorrhage[END_REF] but poorly respected in non-specialized centers. Indeed, CTA / DSA correlation was better when CTA was performed according to our standard protocol (0.847 vs 0.778). CTA therefore is more accurate if performed in a specialized center.

Secondly, artifacts related to aneurysm treatment (coils or clips) can also disturb imaging. In addition, a "cone beam artifact" has been described in some cases in multidetector CTA [START_REF] Barrett | Artifacts in CT: Recognition and Avoidance[END_REF] . Various solutions can overcome these artifacts such as metal artifact reduction (IMAR protocol not evaluated in our series due to a lack of patients) or coils subtraction in dual energy CT [START_REF] Fitsiori | Iterative Algorithms Applied to Treated Intracranial Aneurysms[END_REF] .

Lastly, spatial resolution in CTA (0.7-0.8mm) remains lower than in DSA (0.4 mm) [START_REF] Villablanca | MDCT Angiography for Detection and Quantification of Small Intracranial Arteries: Comparison with Conventional Catheter Angiography[END_REF][START_REF] Skutta | Intracranial Stenoocclusive Disease: Double-Detector Helical CT Angiography versus Digital Subtraction Angiography[END_REF] . But standard measurements of normal intracranial arteries are well above these values [START_REF] Kamath | Observations on the length and diameter of vessels forming the circle of Willis[END_REF] . CVS analysis will therefore be limited for arteries whose caliber approaches CTA resolution. Studies have also shown that measurement errors increase with decreasing arterial size, decreasing intraluminal contrast density and field of view size [START_REF] Suzuki | Measurement of Vascular Diameter In Vitro by Automated Software for CT Angiography: Effects of Inner Diameter, Density of Contrast Medium, and Convolution Kernel[END_REF][START_REF] Suzuki | Accuracy of Automated CT Angiography Measurement of Vascular Diameter in Phantoms: Effect of Size of Display Field of View, Density of Contrast Medium, and Wall Thickness[END_REF] .

Our results are hardly comparable to other series that use very different methodologies 11- 19,30,31 . Unlike most published studies [START_REF] Anderson | CT angiography for the detection of cerebral vasospasm in patients with acute subarachnoid hemorrhage[END_REF][START_REF] Shankar | CT angiography for evaluation of cerebral vasospasm following acute subarachnoid haemorrhage[END_REF][START_REF] Yoon | Multidetector-row CT angiography of cerebral vasospasm after aneurysmal subarachnoid hemorrhage: comparison of volume-rendered images and digital subtraction angiography[END_REF][START_REF] Goldsher | Role of Multisection CT Angiography in the Evaluation of Vertebrobasilar Vasospasm in Patients with Subarachnoid Hemorrhage[END_REF][START_REF] Otawara | Evaluation of vasospasm after subarachnoid hemorrhage by use of multislice computed tomographic angiography[END_REF][START_REF] Chaudhary | Prospective evaluation of multidetector-row CT angiography for the diagnosis of vasospasm following subarachnoid hemorrhage: a comparison with digital subtraction angiography[END_REF] , we evaluated CVS quantitatively and not semi-quantitatively (various classifications used in literature) or even qualitatively.

Although our methodology is limited by some degree of uncertainty in our measurements, it is more likely to be reproducible than with a subjective approach. Furthermore, we calculated vasospasm up to the second segments of cerebral arteries while the only other study using quantitative approach like us studied it proximally [START_REF] Hébert | 320-Row Multidetector CT Angiography in the Detection of Critical Cerebrovascular Anomalies[END_REF] . Our methodology could be therefore a major contribution. Indeed, CVS seems important in the occurrence of DCI even if it's distal. Santillan et al [START_REF] Santillan | Transluminal Balloon Angioplasty for Symptomatic Distal Vasospasm Refractory to Medical Therapy in Patients With Aneurysmal Subarachnoid Hemorrhage[END_REF] showed that new techniques make these segments accessible to angioplasty without increasing risk compared to proximal treatments.

In addition, CVS is a dynamic phenomenon. It is therefore subject to variations. For this reason, we only included patients with a delay between CTA and DSA less than 6 hours (median 3h IC95% [2-4 h]) to be closest to reality. This is the shortest inclusion period, to our knowledge, in the literature [START_REF] Shankar | CT angiography for evaluation of cerebral vasospasm following acute subarachnoid haemorrhage[END_REF][START_REF] Yoon | Multidetector-row CT angiography of cerebral vasospasm after aneurysmal subarachnoid hemorrhage: comparison of volume-rendered images and digital subtraction angiography[END_REF][START_REF] Chaudhary | Prospective evaluation of multidetector-row CT angiography for the diagnosis of vasospasm following subarachnoid hemorrhage: a comparison with digital subtraction angiography[END_REF] .

Our study suggests that CTA has an excellent NPV for CVS ≥ 50% on DSA for a threshold less than 30% and an excellent PPV above a threshold of 40%. For CVS below 30%, CTA eliminate any risk of CVS ≥ 50% on DSA and therefore any intracranial angioplasty in our center. CTA is superior compared to TCD which has many false negatives [START_REF] Lysakowski | Transcranial Doppler Versus Angiography in Patients With Vasospasm due to a Ruptured Cerebral Aneurysm: A Systematic Review[END_REF] .It helps to avoid DSA to evaluate CVS and thus contributes to reduce neurological risk inherent to DSA but also to diminish the mobilization of fragile patients out of the intensive care unit. With CVS above 40%, CTA is a potent risk marker for CVS ≥50% in DSA and therefore of DCI. It helps to decide to intensify a preventive medical treatment of DCI and to discuss indication for angioplasty. However, the poor specificity of CTA between 30% and 40% invites us to consider other arguments as well to roll out CVS in order to avoid an abusive therapeutic intensification or unnecessary DSA before angioplasty decision.

Finally, our study emphasizes that CTA as a screening test has an impact on radiation on lens compared to DSA, superior compared to a CVS screening directly by DSA. Our results are obtained with recent dose reduction protocols. For radiation dose to the skin, the benefit of CTA disappears when the prevalence of CVS ≥ 50% in DSA becomes too high. In fact, even if in many cases CTA limits the need of DSA, it increases radiation dose received when it leads to perform DSA for angioplasty. Gain in terms of "DSA avoided" increases when prevalence of CVS ≥ 50% decreases. This test loses its interest for too high prevalence of CVS ≥ 50% both for "DSA avoided" but also radiation dose received on skin. Repetition of DSA (especially if associated with CTA or CTP) exposes to a large cumulative dose. Thus, 2 CTA and 2 DSA performed in the same patient exhibit a cumulative dose of about 74 mSv for the crystalline and 250 mSy for the skin which is far from the dose causing deterministic effects (2 and 3 Sy for the lens and the skin respectively) but already higher to recommended annual dose for lens for personnel exposed to ionizing radiation by EURATOM (20mSv) 35 . Note that in our structure, on average 4 CTA were performed for patients when CVS was suspected.

Our study has many limitations. CTA were assessed retrospectively. Our results need to be confirmed with a prospective study for a sufficient level of proof.

Our population sample included wasn't representative of all patients who had a CTA.

Only patients with very close delay between CTA and DSA and who underwent angioplasty were included limiting our data base. Therefore, we cannot determine the exact prevalence of CVS ≥ 50% in our study or calculate predictive values of CTA. To overcome this, we proposed a simulation of 3 possible prevalence. The exact prevalence of CVS≥ 50% is very difficult to analyze in the literature as this parameter has been measured differently. It depends on pre-test risk assessed clinically and in relation to TCD (flow velocity > 120 cm/s in our structure). In Mascia et al 10 , all patients had systematically DSA at day 1, day 7 and if neurologic deficit occurred up until day 8. CVS peak was at day 6.5. There was a high prevalence of CVSDSA ≥ 50 % (severe for the authors) if the patient presented a sudden neurological deficit associated with a flow velocity on TCD > 160 cm/s. TCD could therefore help screening patients.

DSA stays a technique with certain risks therefore CTA place is still to be defined particularly between day 5 and day 7 when assessment of DCI is difficult (sedated patients, lack of acoustic window in TCD) or when there is poorly insufficiently specified neurological degradation.

Finally, our study does not directly link CTA findings to DCI risk. This risk would be improperly appreciated in our study because it depends on therapeutics administrated after CTA. We know that not all severe CVS lead to DCI and conversely even if this is rare, some DCIs occur without CVS [START_REF] Vergouwen | Definition of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage as an Outcome Event in Clinical Trials and Observational Studies: Proposal of a Multidisciplinary Research Group[END_REF] .Therefore, it is likely that sensitivity and specificity of CTA to predict occurrence of DCI are lower than those to predict a significant CVS.

Some authors argue that analysis of microcirculation by CTP would be better to predict DCI [START_REF] Greenberg | Diagnostic Accuracy of CT Angiography and CT Perfusion for Cerebral Vasospasm: A Meta-Analysis[END_REF] . However, a recent metanalysis didn't show any superiority of CTP compared to CTA on this criterion. They also found similar specificity and sensitivity with an overall sensitivity and specificity of 79.6% and 93.1% respectively for CTA and 74.1% and 93.0% respectively for CTP [START_REF] Greenberg | Diagnostic Accuracy of CT Angiography and CT Perfusion for Cerebral Vasospasm: A Meta-Analysis[END_REF] . In our practice, CTP is more irradiant than CTA, and only indicated as second-line imaging when CTA remains inconclusive.

CONCLUSION

CTA is an interesting test in clinical practice to detect CVS and may replace DSA. Its challenging accurate interpretation must be reserved to specialized centers and expert neuroradiologists. Adequate selection of patients most likely to benefit from CTA before a possible DSA, is required due to radiation exposure, based on clinical criteria and TCD data.

BIBLIOGRAPHY

  profond respect et de mon admiration. Merci d'avoir accepté de faire partie de mon jury de thèse. *********************************** A ma famille et amis A mon quatuor, ma mère, ma grand-mère Mme Albertine Baclet, ma soeur Anne-Louisa et ma tante Maryla. Bien trop à remercier car sans chacune de vous, sans votre soutien et persévérance, je ne serais pas où j'en suis aujourd'hui. Merci de votre amour. A mon oncle Alex et ma tante Lucette, je vous remercie de votre soutien, gentillesse et de l'accueil chez vous en Guadeloupe pendant 6 mois. A ma tante Suzie, qui m'a si bien accueillie à mon arrivée en Martinique et s'est occupée de moi comme sa fille. A mes cousins et cousine, Marc, Justine, Jérémy et Germain pour leur présence toutes ses années. A ceux qui ne sont malheureusement plus là, mon grand-oncle Félix et Mamie Laporal qui auraient été si fiers de me voir arriver jusque-là. Merci pour leur amour. Aux proches Marie-Galantais, Clémencia, Mr et Mme Hégesippe, Mme Comuce pour leur gentillesse et grande bonté d'âme qui me rendent si fiers d'être originaire de cette petite île. A mes copines de toujours, Anaïs, Aurélie, Agathe, Olga, Stéphanie, Reenal. Merci pour votre amitié, aide et patience. Je vous remercie d'être toujours là pour moi après toutes ces années.

  Schertz), Sarah (Dhundass), JB et Mickael (Mima). Je vous remercie d'avoir été de si bons co-internes, amis, d'avoir eu une telle émulsion pour toujours nous pousser vers le haut, d'avoir su créer une telle cohésion entre nous. Un merci tout particulier à Mathieu qui a su trouver les mots pour me conforter vers mon choix d'internat aux Antilles et le tout pendant une garde un samedi. A mes anciens co-internes et amis, Audrey Guillier et Dimitri Dinal. Je vous remercie pour ces 6 mois passés à vos côtés en stage clinique, pour votre bonne humeur et vos blagues. Merci pour votre amitié qui perdure malgré l'éloignement. A Jonathan Brami, pour sa relecture, son amitié et nos moments de folie à Larib. A mes tout premiers chefs, Mehdi Lebbadi, Isaure Biette et Fanny Rouget qui m'ont si bien accueilli lors de mon arrivée en 1er semestre en Martinique, pour m'avoir transmis les fondements de la radiologie et inculqué ce qu'est d'être radiologue. Je vous en serais éternellement reconnaissante et suis honorée de vous avoir rencontrés sur mon chemin. Un merci particulier à Isaure qui s'extasiait tant devant les scanners abdominopelviens et m'a transmis cet amour. secrétaire du service de radiologie du CHU de Martinique, Nadine Granier, pour leur si bon accueil et gentillesse. A mes anciens colocataires, Adrien Tolle et Lindou. Je vous remercie pour ces bons moments passés ensemble. A Fabienne LOUIS-SIDNEY, ma colocataire actuelle et amie. Je te remercie de ton soutien, tes bons petits plats, tes supers tantes et d'avoir eu la patience de me relire. Au Dr Damien BISSERET, ancien chef et ami. Je te remercie pour ton enseignement, notre présentation aux JFR, nos moments de rigolades et d'avoir cru en moi. Au Dr Melinda MAJLATH, ancienne chef et amie. Merci pour ta douceur, ta gentillesse, tes bons conseils et ta précieuse relecture. A mes futurs collègues de l'Hôpital St Antoine à Paris, je vous remercie pour votre si bon accueil lors de mon retour en Métropole. A tous les médecins, co-internes, infirmières et brancardiers que j'ai eu le plaisir de côtoyer durant ces 5 années d'internat. CT angiography for quantitative assessment of large vessel cerebral vasospasm in aneurysmal subarachnoid hemorrhage Mariam Soumah, MD; Jonathan Brami, MD; Antonella Jean-Pierre ; Lama Hadid, MD; Samuel Gaugain, MD ; Vittorio Civelli, MD ; Jean-Pierre Saint-Maurice, MD ; Emmanuel Houdart, MD; Marc-Antoine Labeyrie, MD ABSTRACT Background and purpose-Assessment of large vessel cerebral vasospasm (CVS) with

Figure 1 .

 1 Figure 1. Cerebral vasospasm (CVS) computed on CTA. Initial (pre-CVS) and per-

Figure 2 .

 2 Figure 2. Cerebral vasospasm (CVS) computed on DSA. Initial frontal (A) and lateral

Figure 3 .

 3 Figure 3. Graphical representation of correlation between CTA and DSA. Blot

Table 1 . Patients characteristics (n=20)

 1

	Age, yr

Time to CTA1/CTA2: time from subarachnoid hemorrhage onset to CTA1 or CTA2.

Table 2 . Interrater and intertest correlation (210 arterial segments in 20 patients)

 2

		Absolute difference	Pearson	Kappa coefficient	Area under
		Mean (SD)	coefficient	for CVS ≥50%	curve for
					CVS≥50%
					on DSA
	CTA_reader 1 / 2	10% (10)	0.694	0.437	NA
	CTA_reader 1 /	15% (12)	0.525	0.420	0.839
	DSA
	CTA_reader 2 /	10% (9)	0.805	0.606	0.938
	DSA
	Local CTA	8% (7)	0.847	0.751	0.965
	Non-local CTA	11% (10)	0.778	0.507	0.912
	Quality CTA	8% (8)	0.861	0.697	0.967
	Non-quality CTA	11% (10)	0.739	0.548	0.899
	Proximal segments	9% (9)	0.826	0.705	0.947
	Distal segments	10% (9)	0.780	0.530	0.917
	No metallic	10% (8)	0.843	0.611	0.971
	artefacts
	Metallic artefacts	9% (10)	0.738	0.593	0.911
	CVS: cerebral vasospasm; NA: non-applicable; CTA: computed tomodensitometry angiography;
	DSA: digital subtraction angiography; local CTA: local CTA protocol in our neuroradiology
	department; quality CTA: visibility of the 3 rd segments of cerebral arteries; Distal / proximal
	segments: M2 and A2 / terminal internal carotid artery, M1 and A1.

Reader 1/2: 5-year experienced radiologist/10-year experienced interventional neuroradiologist

Table 3 . Comparative radiation dose between CTA and DSA for CVS

 3

	assessment (n=5)
		CTA	DSA	P value
	Left eye	13.9	7.0	0.13
	Left temporal skin	17.2	76.0	0.04
	Left occipital skin	11.5	98.0	0.02
	Non specified data are mean values (SD): 10 -3 Sievert
	CVS: cerebral vasospasm; CTA: CT angiography; DSA : Digital Subtraction
	Angiography

Table 4 . Simulation of CTA predictive value and dose radiation for different prevalence of CVS ≥ 50% on DSA

 4

		Prevalence of CVS ≥ 50% on DSA
		25%	50%	75%	If no CTA
	Negative predictive value	100 %	100 %	100 %	NA
	Positive predictive value	40 %	66 %	86 %	NA
	Indication of DSA	62 %	75 %	87 %	100%
	Eye dose radiation (CTA+
		18	19	20	7
	DSA)*, mSy
	Skin dose radiation (CTA +
		65	75	85	80
	DSA)*, mSy

* Mean probability of radiation dose for a patient with high suspicion or risk of delayed cerebral ischemia on clinical exam or TCD for whom CTA and potentially DSA are required. The threshold value for CVS on CTA ≥30% was considered as an indication to perform DSA to confirm CVS ≥50% and potentially indicate an angioplasty.

Je ferai tout pour soulager les souffrances, sans acharnement.

Je ne provoquerai jamais la mort délibérément.

Je préserverai l'indépendance nécessaire à l'accomplissement de ma mission.

Que je sois modéré en tout, mais insatiable de mon amour de la science.

Je n'entreprendrai rien qui ne dépasse mes compétences ; je les entretiendrai et les perfectionnerai pour assurer au mieux les services qui me seront demandés.

J'apporterai mon aide à mes confrères ainsi qu'à leurs familles dans l'adversité. Background and purpose-Assessment of large vessel cerebral vasospasm (CVS) with CT angiography (CTA) is commonly used in aneurysmal subarachnoid hemorrhage (aSAH) to monitor the risk of delayed cerebral ischemia (DCI). However, its accuracy is still debated in clinical practice. We aim to assess sensitivity, specificity and radiation dose of CTA and digital subtraction angiography (DSA) to compute CVS. Methods-Consecutive patients with aSAH who underwent both CTA and DSA within less than 6 hours at admission / during CVS period (within the 3 rd / 4 th -21 st days after aSAH onset) were included. CVS was retrospectively computed on CTA and DSA for each anterior intracranial artery segment until the end of their 2 nd segments by 2 readers (reader 1/reader 2; 5-year/10-year experienced radiologist/interventional neuroradiologist) in blinded conditions. Radiation dose to the eye and scalp were also compared prospectively in 1 phantom study and in 5 patients. Results-CVS was computed on a total of 210 arterial segments in 20 patients. The correlation between CTA and DSA was better for the reader 2, CTA performed under our local protocol, CTA with great visualization of distal arteries and segments without metallic artefacts. The best sensitivity and specificity of CTA to predict CVS≥50% on DSA was obtained for a threshold ≥30% for the reader 2 (86% and 86% respectively). Under this value, no patient had CVS≥50% on DSA. Radiation dose to the scalp and eye were respectively lower and tends to be higher for CTA compared to DSA. Conclusions-CTA in clinical practice may not be as excellent as reported to predict CVS on DSA. Its accuracy highly depends on observers' experience. A threshold value of ≤ 30% may rule out CVS≥50% on DSA. Radiation dose limitation is not a valid argument to substitute DSA with CTA.

JURY : Président