Biology subjects

Radman, G.

Publications and source records attributed to Radman, G..

4 recordsLinked to original sources

bioRxiv2026

Characterization and Validation of Compressed Sensing for Time-of-Flight MRI Angiography of the Human Brain at 3T and 7T

BackgroundCerebral vasculature is a key biomarker of brain health, and time-of-flight (TOF) magnetic resonance angiography (MRA) provides noninvasive assessment of vascular anatomy. However, conventional TOF-MRA requires long scan times, increasing patient burden and susceptibility to motion artifacts. Compressed sensing (CS) offers a feasible acceleration strategy. PurposeTo quantitatively evaluate CS acceleration in TOF-MRA at 3T and 7T using automated whole-FOV vascular segmentation and semi-automatic segmentation of representative vessels. Study typeProspective Population23 healthy human participants (3T) and 8 healthy human participants (7T). Field Strength/SequenceCS TOF-MRA (CS factors 4 and 8 at 3T; 8 at 7T) was compared against non-accelerated (CS0) TOF-MRA. AssessmentVisual comparison and vascular segmentation were performed using automated whole-FOV methods and semi-automatic segmentation of the posterior cerebral artery and anterior choroidal artery. Statistical TestsContrast-to-noise ratio (CNR), voxel count, and vessel diameter were assessed using two-tailed paired t-tests. ResultsWhole-FOV CNR differed significantly across CS factors at 3T (CS0 > CS4: p < 0.001, d = 0.77; CS0 < CS8: p = 0.008, d = 0.36; CS4 < CS8: p < 0.001, d = 1.11) and 7T (CS0 < CS8: p = 0.002, d = 0.54), with semi-automatic segmentation yielding consistent findings (p < 0.01 for all comparisons). The diameter measurements for segmented vessels are also higher with high CS-factors (PCA 7T: left: p = 0.006, d = 0.93, right: p = 0.045, d = 0.43; AChA 7T: left: p < 0.001, d = 0.66, right: p = 0.009, d = 1.06; PCA 3T: p < 0.001 for all comparison dLeft = 0.52 (CS0 vs. CS4), 0.56 (CS4 vs. CS8), 1.11 (CS0 vs. CS8) and dRight = 0.78 (CS0 vs. CS4), 0.57 (CS4 vs. CS8), 1.17 (CS0 vs. CS8)). Data ConclusionCS shows promise for enhancing clinical applicability of TOF-MRA, with advantages most pronounced at 7T.

bioengineering↗

bioRxiv2025

Harmonized Protocol for Segmentation of the Hippocampal Tail on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

The hippocampus is a heterogeneous structure with cytoarchitectonically distinct subfields that exhibit heterogeneous lifespan trajectories and are differentially susceptible to diseases. Advances in high-resolution imaging have accelerated research on these structures, yet variability in segmentation protocols limits cross-study comparability. The Hippocampal Subfields Group (HSG) is an international consortium addressing this challenge by developing a reliable, accessible, and freely available segmentation protocol for high-resolution T2-weighted 3 tesla MRI scans (http://www.hippocampalsubfields.com). Here, we present the harmonized protocol for the posterior portion of the hippocampus (the "tail"), complementing the previously established "body" protocol, and with an anterior "head" protocol under development. The tail protocol provides standardized definitions of the external boundaries for the posterior-most extent of the hippocampus, facilitating consistent segmentation from surrounding tissues. The research community was extensively involved through an online survey that incorporated comprehensive protocol details, feasibility assessments, tutorial videos, and illustrative segmentations. Through this collaborative process, consensus emerged to exclude subfield labeling in the hippocampal tail due to limited visibility of internal landmarks and substantial anatomical variability in this region. All proposed boundary guidelines were deemed clear and agreed upon via a Delphi procedure. The harmonized tail protocol has high intra-(Averaged ICC(2,1) > 0.98; Averaged Dice Similarity Coefficient = 0.92) and inter-rater reliability (Averaged ICC(2,k) > 0.98; Averaged Dice Similarity Coefficient = 0.86) and offers a practical framework for replicable segmentation. By establishing standardized guidelines, this protocol enhances comparability of findings across developmental, aging, and clinical research and is compatible with ongoing technological advances.

neuroscience↗

bioRxiv2025

Harmonized Protocol for Subfield Segmentation in the Hippocampal Body on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

Hippocampal subfields differentially develop and age, and they vary in vulnerability to neurodegenerative diseases. Innovation in high-resolution imaging has accelerated clinical research on human hippocampal subfields, but substantial differences in segmentation protocols impede comparisons of results across laboratories. The Hippocampal Subfields Group (HSG) is an international organization seeking to address this issue by developing a histologically-valid, reliable, and freely available segmentation protocol for high-resolution T2-weighted 3 tesla MRI (http://www.hippocampalsubfields.com). Here, we report the first portion of the protocol focused on subfields in the hippocampal body; protocols for the head and tail are in development. The body protocol includes definitions of the internal boundaries between subiculum, Cornu Ammonis (CA) 1-3 subfields, and dentate gyrus, in addition to the external boundaries of the hippocampus apart from surrounding white matter and cerebrospinal fluid. The segmentation protocol is based on a novel histological reference data set labeled by multiple expert neuroanatomists. With broad participation of the research community, we voted on the segmentation protocol via online survey, which included detailed protocol information, feasibility testing, demonstration videos, example segmentations, and labeled histology. All boundary definitions were rated as having high clarity and reached consensus agreement by Delphi procedure. The harmonized body protocol yielded high inter- and intra-rater reliability. In the present paper we report the procedures to develop and test the protocol, as well as the detailed procedures for manual segmentation using the harmonized protocol. The harmonized protocol will significantly facilitate cross-study comparisons and provide increased insight into the structure and function of hippocampal subfields across the lifespan and in neurodegenerative diseases.

neuroscience↗

bioRxiv2023

A (Sub)field Guide to Quality Control in Hippocampal Subfield Segmentation on High-resolution T2-weighted MRI

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.

neuroscience↗