Julien Cohen-Adad

2024-01-01

AMAI@MICCAI (published)

Towards contrast-agnostic soft segmentation of the spinal cord

Sandrine Bédard

Enamundram Naga Karthik

Charidimos Tsagkas

Emanuele Pravatà

Cristina Granziera

Andrew C. Smith

Kenneth Arnold Weber

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (see more)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (

2023-10-23

ArXiv (preprint)

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord

Haykel Snoussi

Olivier Commowick

Benoit Combes

Elise Bannier

Slimane Tounekti

Anne Kerbrat

Christian Barillot

Emmanuel Caruyer

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (see more) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.

2023-09-29

Brain and Behavior (published)

Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord

Kurt G Schilling

Anna J. E. Combes

Karthik Ramadass

Francois Rheault

Grace Sweeney

Logan Prock

Subramaniam Sriram

John C Gore

Bennett A Landman

Seth A. Smith

Kristin P. O’Grady

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment acro… (see more)ss time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.

2023-09-27

bioRxiv (preprint)

Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord

Kurt G Schilling

Anna J. E. Combes

Karthik Ramadass

Francois Rheault

Grace Sweeney

Logan Prock

Subramaniam Sriram

John C Gore

Bennett A Landman

Seth A. Smith

Kristin P. O’Grady

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment acro… (see more)ss time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.

2023-09-27

bioRxiv (preprint)

Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions

Sandrine Bédard

Maxime Bouthillier

2023-08-19

Scientific Reports (published)

An 8‐channel Tx dipole and 20‐channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla

Nibardo Lopez‐Rios

Kyle M. Gilbert

Daniel Papp

Gaspard Cereza

Alexandru Foias

Deshpande Rangaprakash

Markus W. May

Bastien Guerin

Lawrence L. Wald

Boris Keil

Jason P. Stockmann

Robert L. Barry

The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency (RF) coil solutions for ultrahigh field ima… (see more)ging; however, very few commercial and research 7‐T RF coils currently exist for the spinal cord, and in particular, those with parallel transmission (pTx) capabilities. This work presents the design, testing, and validation of a pTx/Rx coil for the human neck and cervical/upper thoracic spinal cord. The pTx portion is composed of eight dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made up of twenty semiadaptable overlapping loops to produce high signal‐to‐noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while also being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency, and/or specific absorption rate efficiency. B1+ homogeneity, SNR, and g‐factor were evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4‐T5 level. We compared the proposed coil with two state‐of‐the‐art head and head/neck coils, confirming its superiority in the cervical and upper thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.

2023-07-13

NMR in Biomedicine (published)

Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T

Eva Alonso‐Ortiz

Daniel Papp

A. D'Astous

2023-03-01

Neuroimage. Reports (published)

8-channel Tx dipole and 20-channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla

Nibardo Lopez Rios

Kyle M. Gilbert

Daniel Papp

Gaspard Cereza

Alexandru Foias

D. Rangaprakash

Markus W. May

Bastien Guerin

Lawrence L. Wald

Boris Keil

Jason P. Stockmann

Robert L. Barry

2023-02-09

bioRxiv (preprint)

Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

Reza Azad

Yiwei Jia

Ehsan Khodapanah Aghdam

Dorit Merhof

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suff… (see more)er from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available at https://github.com/mindflow-institue/TransCeption.

2023-01-25

ArXiv (preprint)

Label fusion and training methods for reliable representation of inter-rater uncertainty

Andréanne Lemay

Charley Gros

Enamundram Naga Karthik

2023-01-18

Machine Learning for Biomedical Imaging (published)

HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

Moein Heidari

Amirhossein Kazerouni

Milad Soltany

Reza Azad

Ehsan Khodapanah Aghdam

Dorit Merhof

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation … (see more)in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, quantitative and qualitative results. Our code is publicly available at GitHub.

2023-01-02

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (published)