How to Implement Clinical 7T MRI-Practical Considerations and Experience with Ultra-High-Field MRI.

The implementation of clinical 7T MRI presents both opportunities and challenges for advanced medical imaging. This tutorial provides practical considerations and experiences with 7T MRI in clinical settings. We first explore the history and evolution of MRI technology, highlighting the benefits of increased signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and susceptibility at 7T.

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

Purpose: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD).

Comparison of 3D printed anatomical model qualities in acetabular fracture representation.

Background: Acetabular fractures account for 10% of pelvis injuries, which are especially difficult to treat in developing countries with less access to resources. 3D printing has previously been shown to be a beneficial method of surgical planning, however the steep initial costs associated with purchasing a 3D printer may prevent some facilities form utilizing this technique. The purpose of this study was to develop 3D printed models for acetabular surgery using methodologies of varying cost to determine differences in model accuracy and overall quality.

Use of a three-dimensional printed anatomical model for tumor management in a pediatric patient.

The purpose of this study was to investigate the usage of an anatomical model to improve surgical planning of a complex schwannoma resection. As advancements in additive manufacturing continue to prosper, new applications of this valuable technology are being implemented in the medical field. One of the most recent applications has been in the development of patient-specific anatomical models for unique clinical education as well as for preoperative planning.

Radiologic Differentiation of Primary CNS Posttransplant Lymphoproliferative Disorder From Brain Metastasis.

Primary CNS posttransplant lymphoproliferative disorder (PTLD) may present as multiple contrast-enhancing intraaxial lesions, often with central necrosis and surrounding edema. This imaging appearance is similar to the pattern seen in brain metastases. The purpose of this study was to find differences in the radiologic features of primary CNS PTLD lesions and brain metastases.

Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.

Purpose: Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations.

Educational Material for 3D Visualization of Spine Procedures: Methods for Creation and Dissemination.

Spine anatomy can be difficult to master and is essential for performing spine procedures. We sought to utilize the rapidly expanding field of 3D technology to create freely available, interactive educational materials for spine procedures. Our secondary goal was to convey lessons learned about 3D modeling and printing.

Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state.

Objectives: Application of fMRI connectivity metrics as diagnostic biomarkers at the individual level will require reliability, sensitivity and specificity to longitudinal changes in development, aging, neurocognitive, and behavioral performance and pathologies. Such metrics have not been well characterized for recent advances in BOLD acquisition.

Experimental Design: Analysis of multiband BOLD data from the HCP 500 Subjects Release was performed with FIX ICA and with WM, CSF and motion parameter regression.