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Combined Whole Eye and Face Transplant: Microsurgical Strategy and 1-Year Clinical Course
Ceradini, Daniel J; Tran, David L; Dedania, Vaidehi S; Gelb, Bruce E; Cohen, Oriana D; Flores, Roberto L; Levine, Jamie P; Saadeh, Pierre B; Staffenberg, David A; Ben Youss, Zakia; Filipiak, Patryk; Baete, Steven H; Rodriguez, Eduardo D
IMPORTANCE/UNASSIGNED:Catastrophic facial injury with globe loss remains a formidable clinical problem with no previous reports of reconstruction by whole eye or combined whole eye and facial transplant. OBJECTIVE/UNASSIGNED:To develop a microsurgical strategy for combined whole eye and facial transplant and describe the clinical findings during the first year following transplant. DESIGN, SETTING, AND PARTICIPANT/UNASSIGNED:A 46-year-old man who sustained a high-voltage electrical injury with catastrophic tissue loss to his face and left globe underwent combined whole eye and face transplant using personalized surgical devices and a novel microsurgical strategy at a specialized center for vascularized composite allotransplantation. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Reperfusion and viability of the whole eye and facial allografts, retinal function, and incidence of acute rejection. RESULTS/UNASSIGNED:The patient underwent a combined whole eye and face transplant from an immunologically compatible donor with primary optic nerve coaptation and conventional postoperative immunosuppression. Globe and retinal perfusion were maintained throughout the immediate postoperative period, evidenced by fluorescein angiography. Optical coherence tomography demonstrated atrophy of inner retinal layers and attenuation and disruption of the ellipsoid zone. Serial electroretinography confirmed retinal responses to light in the transplanted eye. Using structural and functional magnetic resonance imaging, the integrity of the transplanted visual pathways and potential occipital cortical response to light stimulation of the transplanted eye was demonstrated. At 1 year post transplant (postoperative day 366), there was no perception of light in the transplanted eye. CONCLUSIONS AND RELEVANCE/UNASSIGNED:This is the first report of whole eye transplant combined with facial transplant, demonstrating allograft survival including rejection-free graft survival and electroretinographic measurements indicating retinal response to light stimuli. These data highlight the potential for clinical allotransplantation for globe loss.
PMID: 39250113
ISSN: 1538-3598
CID: 5690002
FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging
Tibrewala, Radhika; Dutt, Tarun; Tong, Angela; Ginocchio, Luke; Lattanzi, Riccardo; Keerthivasan, Mahesh B; Baete, Steven H; Chopra, Sumit; Lui, Yvonne W; Sodickson, Daniel K; Chandarana, Hersh; Johnson, Patricia M
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
PMID: 38643291
ISSN: 2052-4463
CID: 5726322
Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting
Filipiak, Patryk; Sajitha, Thajunnisa A; Shepherd, Timothy M; Clarke, Kamri; Goldman, Hannah; Placantonakis, Dimitris G; Zhang, Jiangyang; Chan, Kevin C; Boada, Fernando E; Baete, Steven H
PURPOSE/OBJECTIVE:The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS: RESULTS:ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION/CONCLUSIONS:In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.
PMID: 37927121
ISSN: 1522-2594
CID: 5612792
Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge
Girard, Gabriel; Rafael-Patiño, Jonathan; Truffet, Raphaël; Aydogan, Dogu Baran; Adluru, Nagesh; Nair, Veena A; Prabhakaran, Vivek; Bendlin, Barbara B; Alexander, Andrew L; Bosticardo, Sara; Gabusi, Ilaria; Ocampo-Pineda, Mario; Battocchio, Matteo; Piskorova, Zuzana; Bontempi, Pietro; Schiavi, Simona; Daducci, Alessandro; Stafiej, Aleksandra; Ciupek, Dominika; Bogusz, Fabian; Pieciak, Tomasz; Frigo, Matteo; Sedlar, Sara; Deslauriers-Gauthier, Samuel; KojÄić, Ivana; Zucchelli, Mauro; Laghrissi, Hiba; Ji, Yang; Deriche, Rachid; Schilling, Kurt G; Landman, Bennett A; Cacciola, Alberto; Basile, Gianpaolo Antonio; Bertino, Salvatore; Newlin, Nancy; Kanakaraj, Praitayini; Rheault, Francois; Filipiak, Patryk; Shepherd, Timothy M; Lin, Ying-Chia; Placantonakis, Dimitris G; Boada, Fernando E; Baete, Steven H; Hernández-Gutiérrez, Erick; RamÃrez-Manzanares, Alonso; Coronado-Leija, Ricardo; Stack-Sánchez, Pablo; Concha, Luis; Descoteaux, Maxime; Mansour L, Sina; Seguin, Caio; Zalesky, Andrew; Marshall, Kenji; Canales-RodrÃguez, Erick J; Wu, Ye; Ahmad, Sahar; Yap, Pew-Thian; Théberge, Antoine; Gagnon, Florence; Massi, Frédéric; Fischi-Gomez, Elda; Gardier, Rémy; Haro, Juan Luis Villarreal; Pizzolato, Marco; Caruyer, Emmanuel; Thiran, Jean-Philippe
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
PMID: 37330025
ISSN: 1095-9572
CID: 5609102
Time-dependent diffusivity and kurtosis in phantoms and patients with head and neck cancer
Solomon, Eddy; Lemberskiy, Gregory; Baete, Steven; Hu, Kenneth; Malyarenko, Dariya; Swanson, Scott; Shukla-Dave, Amita; Russek, Stephen E; Zan, Elcin; Kim, Sungheon Gene
PURPOSE:To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular-interstitial water exchange time with long diffusion times (100-800 ms) using stimulated-echo DWI. METHODS: RESULTS: CONCLUSIONS:Based on two well-established diffusion phantoms, we found that time-dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular-interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer.
PMCID:9712275
PMID: 36219464
ISSN: 1522-2594
CID: 5646302
Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting
Filipiak, Patryk; Shepherd, Timothy; Basler, Lee; Zuccolotto, Anthony; Placantonakis, Dimitris G; Schneider, Walter; Boada, Fernando E; Baete, Steven H
Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.
PMCID:9870046
PMID: 36695675
CID: 5646312
Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model
Filipiak, Patryk; Shepherd, Timothy; Lin, Ying-Chia; Placantonakis, Dimitris G; Boada, Fernando E; Baete, Steven H
PURPOSE/OBJECTIVE:Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier. METHODS:A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images. RESULTS:ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature. CONCLUSION/CONCLUSIONS:The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.
PMID: 35225365
ISSN: 1522-2594
CID: 5174102
Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI
Maffei, Chiara; Girard, Gabriel; Schilling, Kurt G; Aydogan, Dogu Baran; Adluru, Nagesh; Zhylka, Andrey; Wu, Ye; Mancini, Matteo; Hamamci, Andac; Sarica, Alessia; Teillac, Achille; Baete, Steven H; Karimi, Davood; Yeh, Fang-Cheng; Yildiz, Mert E; Gholipour, Ali; Bihan-Poudec, Yann; Hiba, Bassem; Quattrone, Andrea; Quattrone, Aldo; Boshkovski, Tommy; Stikov, Nikola; Yap, Pew-Thian; de Luca, Alberto; Pluim, Josien; Leemans, Alexander; Prabhakaran, Vivek; Bendlin, Barbara B; Alexander, Andrew L; Landman, Bennett A; Canales-RodrÃguez, Erick J; Barakovic, Muhamed; Rafael-Patino, Jonathan; Yu, Thomas; Rensonnet, Gaëtan; Schiavi, Simona; Daducci, Alessandro; Pizzolato, Marco; Fischi-Gomez, Elda; Thiran, Jean-Philippe; Dai, George; Grisot, Giorgia; Lazovski, Nikola; Puch, Santi; Ramos, Marc; Rodrigues, Paulo; PrÄkovska, Vesna; Jones, Robert; Lehman, Julia; Haber, Suzanne N; Yendiki, Anastasia
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
PMID: 35636227
ISSN: 1095-9572
CID: 5235852
Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems
Coelho, Santiago; Baete, Steven H; Lemberskiy, Gregory; Ades-Aaron, Benjamin; Barrol, Genevieve; Veraart, Jelle; Novikov, Dmitry S; Fieremans, Els
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40 and 80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
PMID: 35545197
ISSN: 1095-9572
CID: 5214502
Three-Dimensional Printed Anatomic Models Derived From Magnetic Resonance Imaging Data: Current State and Image Acquisition Recommendations for Appropriate Clinical Scenarios
Talanki, Varsha R; Peng, Qi; Shamir, Stephanie B; Baete, Steven H; Duong, Timothy Q; Wake, Nicole
Three-dimensional (3D) printing technologies have been increasingly utilized in medicine over the past several years and can greatly facilitate surgical planning thereby improving patient outcomes. Although still much less utilized compared to computed tomography (CT), magnetic resonance imaging (MRI) is gaining traction in medical 3D printing. The purpose of this study was two-fold: 1) to determine the prevalence in the existing literature of using MRI to create 3D printed anatomic models for surgical planning and 2) to provide image acquisition recommendations for appropriate clinical scenarios where MRI is the most suitable imaging modality. The workflow for creating 3D printed anatomic models from medical imaging data is complex and involves image segmentation of the regions of interest and conversion of that data into 3D surface meshes, which are compatible with printing technologies. CT is most commonly used to create 3D printed anatomic models due to the high image quality and relative ease of performing image segmentation from CT data. As compared to CT datasets, 3D printing using MRI data offers advantages since it provides exquisite soft tissue contrast needed for accurate organ segmentation and it does not expose patients to unnecessary ionizing radiation. MRI, however, often requires complicated imaging techniques and time-consuming postprocessing procedures to generate high-resolution 3D anatomic models needed for 3D printing. Despite these challenges, 3D modeling and printing from MRI data holds great clinical promises thanks to emerging innovations in both advanced MRI imaging and postprocessing techniques. EVIDENCE LEVEL: 2 TECHNICAL EFFICATCY: 5.
PMID: 34046959
ISSN: 1522-2586
CID: 4888362