Browsing by Author "Cohen-Adad, Julien"

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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging
    (Wiley, 2025) Jelescu, Ileana O.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Schilling, Kurt G.; Neurology, School of Medicine
    Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition
    (Wiley, 2025) Schilling, Kurt G.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Howard, Amy F. D.; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
    (Wiley, 2025) Schilling, Kurt G.; Howard, Amy F. D.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.
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    Rapid, automated nerve histomorphometry through open-source artificial intelligence
    (Springer, 2022-04-08) Daeschler, Simeon Christian; Bourget, Marie-Hélène; Derakhshan, Dorsa; Sharma, Vasudev; Asenov, Stoyan Ivaylov; Gordon, Tessa; Cohen-Adad, Julien; Borschel , Gregory Howard; Surgery, School of Medicine
    We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained for automated axon/myelin segmentation using a dataset of light-microscopic cross-sectional images of osmium tetroxide-stained rat nerves including various axonal regeneration stages. In a second dataset, accuracy of automated segmentation was determined against manual axon/myelin labels. Automated morphometry results, including axon diameter, myelin sheath thickness and g-ratio were compared against manual straight-line measurements and morphometrics extracted from manual labels with AxonDeepSeg as a reference standard. The neural network achieved high pixel-wise accuracy for nerve fiber segmentations with a mean (± standard deviation) ground truth overlap of 0.93 (± 0.03) for axons and 0.99 (± 0.01) for myelin sheaths, respectively. Nerve fibers were identified with a sensitivity of 0.99 and a precision of 0.97. For each nerve fiber, the myelin thickness, axon diameter, g-ratio, solidity, eccentricity, orientation, and individual x -and y-coordinates were determined automatically. Compared to manual morphometry, automated histomorphometry showed superior agreement with the reference standard while reducing the analysis time to below 2.5% of the time needed for manual morphometry. This open-source convolutional neural network provides rapid and accurate morphometry of entire peripheral nerve cross-sections. Given its easy applicability, it could contribute to significant time savings in biomedical research while extracting unprecedented amounts of objective morphologic information from large image datasets.