PET image enhancement using artificial intelligence for better characterization of epilepsy lesions

Flaus, Anthime; Deddah, Tahya; Reilhac, Anthonin; Leiris, Nicolas De; Janier, Marc; Mérida, Inès; Grenier, Thomas; McGinnity, Colm J.; Hammers, Alexander; Lartizien, Carole; Costes, Nicolas

doi:10.3389/fmed.2022.1042706

Cited by 8 publications

(3 citation statements)

References 62 publications

(87 reference statements)

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“…Due to the presence of local hyperintensities in some FCD cases, some detection models [ 14 , 15 ] added FLAIR sequences to improve model performance. In addition, Flaus et al [ 26 ] proposed a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization, from 38 to 75%, in a 37-case adult cohort. Although the combination of multiple imaging techniques would benefit the subtle FCD detection [ 27 ], we aim to simplify input requirements.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

Zhang,

Zhuang,

Luo

et al. 2024

Insights Imaging

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Objectives Focal cortical dysplasia (FCD) represents one of the most common causes of refractory epilepsy in children. Deep learning demonstrates great power in tissue discrimination by analyzing MRI data. A prediction model was built and verified using 3D full-resolution nnU-Net for automatic lesion detection and segmentation of children with FCD II. Methods High-resolution brain MRI structure data from 65 patients, confirmed with FCD II by pathology, were retrospectively studied. Experienced neuroradiologists segmented and labeled the lesions as the ground truth. Also, we used 3D full-resolution nnU-Net to segment lesions automatically, generating detection maps. The algorithm was trained using fivefold cross-validation, with data partitioned into training (N = 200) and testing (N = 15). To evaluate performance, detection maps were compared to expert manual labels. The Dice-Sørensen coefficient (DSC) and sensitivity were used to assess the algorithm performance. Results The 3D nnU-Net showed a good performance for FCD lesion detection at the voxel level, with a sensitivity of 0.73. The best segmentation model achieved a mean DSC score of 0.57 on the testing dataset. Conclusion This pilot study confirmed that 3D full-resolution nnU-Net can automatically segment FCD lesions with reliable outcomes. This provides a novel approach to FCD lesion detection. Critical relevance statement Our fully automatic models could process the 3D T1-MPRAGE data and segment FCD II lesions with reliable outcomes. Key points • Simplified image processing promotes the DL model implemented in clinical practice. • The histopathological confirmed lesion masks enhance the clinical credibility of the AI model. • The voxel-level evaluation metrics benefit lesion detection and clinical decisions. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

Zhang,

Zhuang,

Luo

et al. 2024

Insights Imaging

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“…Folego et al ( 2020 ) have adapted LeNet, VGGNet, GoogLeNet, and ResNet in 3D domain to the aim of AD detection. Flaus et al ( 2022 ) has proposed a 3D sequential ResNet to enhance PET images for better visualization of brain lesions. A transparent CNN framework proposed by Eitel et al ( 2019 ) has revealed the decision process of CNN in the diagnosis of MS and pointed out more disease-relevant features in MR images.…”

Section: Introductionmentioning

confidence: 99%

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Chen,

Barile,

Durand-Dubief

et al. 2024

Front. Neurosci.

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Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

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“…They are mainly based on auto‐encoders, using latent space representation of the FDG normal distribution to detect out‐of‐distribution areas that could correspond to the EZ 16,27,28 . Other approaches are based on image synthesis: to predict enhanced [ 18 F]FDG PET image to facilitate visual analysis 29 or to predict the FDG normal distribution from a T1w image of the patient and compare it to the actual clinical PET. Proofs of concept of clinical relevance of last approach have been published in dementia 30 and epilepsy 25 .…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy

Flaus,

Jung,

Ostrowky‐Coste

et al. 2023

Epilepsia Open

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ObjectiveNormal interictal [18F]FDG‐PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co‐registered to MRI) can then be used to compare epilepsy patients’ predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug‐resistant epilepsy (DRE) with reference to visual and Statistical Parametric Mapping (SPM) analysis.MethodsPatients with complete pre‐surgical work‐up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed.ResultsTwenty patients aged 17 to 50 (32±7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI‐negative. After surgery, fourteen patients (70%) had a good outcome (Engel I‐II). RZ localisation rate was 60% with SIPCOM vs. 35% using SPM (p=0.015) and vs. 85% using visual analysis (p=0.54). Results were similar for Engel I‐II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI‐positive vs. 50% in MRI‐negative patients, and 64% in TLE vs. 43% in extra‐TLE. The average number of false‐positive clusters was 2.2±1.3 using SIPCOM vs. 2.3±3.1 using SPM. All RZs localised with SPM were correctly localised with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localised the RZ.SignificanceSIPCOM performed better than the reference computer‐assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated.

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PET image enhancement using artificial intelligence for better characterization of epilepsy lesions

Cited by 8 publications

References 62 publications

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy

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