T Clusters Are More Sensitive Than Mean T Change to Detect Early and Longitudinal Changes in Anterior Cruciate Ligament Reconstructed and Healthy Knees.

Background: Post-traumatic osteoarthritis (PTOA) often follows anterior cruciate ligament reconstruction (ACLR), leading to early cartilage degradation. Change in mean T fails to capture subject-specific spatial-temporal variations, highlighting the need for robust quantitative methods for early PTOA detection and monitoring.

Purpose/hypothesis: Develop and apply 3D T cluster analysis to ACLR and healthy knees over 2.

Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction.

Purpose: BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications.

A diffusion-prepared reduced FOV sequence for prostate MRI near metallic implants.

Purpose: To enable diffusion weighted imaging in prostate patients with metallic total hip replacements in clinically feasible scan times for prostate cancer screening, and avoid distortion and dropout artifacts present in the conventionally used Echo Planar Imaging (EPI).

Methods: A reduced field of view (FOV) diffusion-prepared sequence that is robust to the B inhomogeneities produced by total hip replacements was achieved using high radiofrequency (RF) bandwidth pulses and manipulation for stimulated echo pathways. The reduced FOV along the A/P direction was obtained using slice-select gradient reversal, and the prepared magnetization was imaged with a three-dimensional RF-spoiled gradient echo readout.

From microscope to head-mounted display: integrating hand tracking into microsurgical augmented reality.

Purpose: The operating microscope plays a central role in middle and inner ear procedures that involve working within tightly confined spaces under limited exposure. Augmented reality (AR) may improve surgical guidance by combining preoperative computed tomography (CT) imaging that can provide precise anatomical information, with intraoperative microscope video feed. With current technology, the operator must manually interact with the AR interface using a computer.

Updated perspectives on the contribution of the microbiome to the pathogenesis of mucositis using the MASCC/ISOO framework.

Advances in the treatment of cancer have significantly improved mortality rates; however, this has come at a cost, with many treatments still limited by their toxic side effects. Mucositis in both the mouth and gastrointestinal tract is common following many anti-cancer agents, manifesting as ulcerative lesions and associated symptoms throughout the alimentary tract. The pathogenesis of mucositis was first defined in 2004 by Sonis, and almost 20 years on, the model continues to be updated reflecting ongoing research initiatives and more sophisticated analytical techniques.

Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction.

Purpose: Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B and B variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction.

Comprehensive assessment of nonuniform image quality: Application to imaging near metal.

Purpose: Comprehensive assessment of image quality requires accounting for spatial variations in (i) intensity artifact, (ii) geometric distortion, (iii) signal-to-noise ratio (SNR), and (iv) spatial resolution, among other factors. This work presents an ensemble of methods to meet this need, from phantom design to image analysis, and applies it to the scenario of imaging near metal.

Methods: A modular phantom design employing a gyroid lattice is developed to enable the co-registered volumetric quantitation of image quality near a metallic hip implant.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

Purpose: Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection.

Purpose: Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion.

Mitochondrial DNA levels in perfusate and bile during normothermic machine correspond with donor liver quality.

normothermic machine perfusion (NMP) preserves donor organs and permits real-time assessment of allograft health, but the most effective indicators of graft viability are uncertain. Mitochondrial DNA (mtDNA), released consequent to traumatic cell injury and death, including the ischemia-reperfusion injury inherent in transplantation, may meet the need for a biomarker in this context. We describe a real time PCR-based approach to assess cell-free mtDNA during NMP as a universal biomarker of allograft quality.

Stereoscopic calibration for augmented reality visualization in microscopic surgery.

Purpose: Middle and inner ear procedures target hearing loss, infections, and tumors of the temporal bone and lateral skull base. Despite the advances in surgical techniques, these procedures remain challenging due to limited haptic and visual feedback. Augmented reality (AR) may improve operative safety by allowing the 3D visualization of anatomical structures from preoperative computed tomography (CT) scans on real intraoperative microscope video feed.

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

Purpose: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.

Methods: We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts.

Towards Automatic Cartilage Quantification in Clinical Trials - Continuing from the 2019 IWOAI Knee Segmentation Challenge.

Objective: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage loss in longitudinal clinical trials.

Design: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volume scores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019 challenge to segment the 1130 knee MRIs.

Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios.

Multishot Diffusion-Weighted MRI of the Breasts in the Supine vs. Prone Position.

Background: Diffusion-weighted imaging (DWI) may allow for breast cancer screening MRI without a contrast injection. Multishot methods improve prone DWI of the breasts but face different challenges in the supine position.

Purpose: To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs.

A method for measuring B field inhomogeneity using quantitative double-echo in steady-state.

Purpose: To develop and validate a method for mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference ( ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method, depends only on the first echo time.

Methods: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions.