Combined Whole Eye and Face Transplant: Microsurgical Strategy and 1-Year Clinical Course.

Importance: 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: 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: 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.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

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.

Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting.

Purpose: 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: Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF-FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI ( healthy C57BL/6 mice).

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients.

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

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.

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer.

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.

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.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.

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.

Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems.

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.

Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model.

Purpose: 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.

Three-Dimensional Printed Anatomic Models Derived From Magnetic Resonance Imaging Data: Current State and Image Acquisition Recommendations for Appropriate Clinical Scenarios.

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.

Cortical and subcortical signatures of conscious object recognition.

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network.

Global decrease in brain sodium concentration after mild traumatic brain injury.

The pathological cascade of tissue damage in mild traumatic brain injury is set forth by a perturbation in ionic homeostasis. However, whether this class of injury can be detected and serve as a surrogate marker of clinical outcome is unknown. We employ sodium MRI to test the hypotheses that regional and global total sodium concentrations: (i) are higher in patients than in controls and (ii) correlate with clinical presentation and neuropsychological function.

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge.

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures.

Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset.

Introduction: Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets.

Materials And Methods: In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity.

Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles.

Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP).

Low Rank plus Sparse decomposition of ODFs for improved detection of group-level differences and variable correlations in white matter.

A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis.

Accelerated radial diffusion spectrum imaging using a multi-echo stimulated echo diffusion sequence.

Purpose: Diffusion spectrum imaging (DSI) provides us non-invasively and robustly with anatomical details of brain microstructure. To achieve sufficient angular resolution, DSI requires a large number of q-space samples, leading to long acquisition times. This need is mitigated here by combining the beneficial properties of Radial q-space sampling for DSI with a Multi-Echo Stimulated Echo Sequence (MESTIM).

Validation of surface-to-volume ratio measurements derived from oscillating gradient spin echo on a clinical scanner using anisotropic fiber phantoms.

A diffusion measurement in the short-time surface-to-volume ratio (S/V) limit (Mitra et al., Phys Rev Lett. 1992;68:3555) can disentangle the free diffusion coefficient from geometric restrictions to diffusion.

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

Purpose: To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis.

Materials And Methods: This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.

Radial q-space sampling for DSI.

Purpose: Diffusion spectrum imaging (DSI) has been shown to be an effective tool for noninvasively depicting the anatomical details of brain microstructure. Existing implementations of DSI sample the diffusion encoding space using a rectangular grid. Here we present a different implementation of DSI whereby a radially symmetric q-space sampling scheme for DSI is used to improve the angular resolution and accuracy of the reconstructed orientation distribution functions.

Comparison of contrast enhancement and diffusion-weighted magnetic resonance imaging in healthy and cancerous breast tissue.

Objective: To measure background parenchymal enhancement (BPE) and compare with other contrast enhancement values and diffusion-weighted MRI parameters in healthy and cancerous breast tissue at the clinical level.

Materials And Methods: This HIPAA-compliant, IRB approved retrospective study enrolled 77 patients (38 patients with breast cancer - mean age 51.8 ± 10.

Dynamic diffusion-tensor measurements in muscle tissue using the single-line multiple-echo diffusion-tensor acquisition technique at 3T.

When diffusion biomarkers display transient changes, i.e. in muscle following exercise, traditional diffusion-tensor imaging (DTI) methods lack the temporal resolution to resolve the dynamics.

Multiple-echo diffusion tensor acquisition technique (MEDITATE) on a 3T clinical scanner.

This article describes the concepts and implementation of an MRI method, the multiple-echo diffusion tensor acquisition technique (MEDITATE), which is capable of acquiring apparent diffusion tensor maps in two scans on a 3T clinical scanner. In each MEDITATE scan, a set of RF pulses generates multiple echoes, the amplitudes of which are diffusion weighted in both magnitude and direction by a pattern of diffusion gradients. As a result, two scans acquired with different diffusion weighting strengths suffice for accurate estimation of diffusion tensor imaging (DTI) parameters.