Faculty Bibliography

This study investigated the potential of combining multiple MR parameters to enhance the characterization of myelin in the mouse brain. We collected ex vivo multiparametric MR data at 7 T from control and Gli1^-/- mice; the latter exhibit enhanced myelination at Postnatal Day 10 (P10) in the corpus callosum and cortex. The MR data included relaxivity, magnetization transfer, and diffusion measurements, each targeting distinct myelin properties. This analysis was followed by and compared to myelin basic protein (MBP) staining of the same samples. Although a majority of the MR parameters included in this study showed significant differences in the corpus callosum between the control and Gli1^-/- mice, only T₂, T₁/T₂, and radial diffusivity (RD) demonstrated a significant correlation with MBP values. Based on data from the corpus callosum, partial least square regression suggested that combining T₂, T₁/T₂, and inhomogeneous magnetization transfer ratio could explain approximately 80% of the variance in the MBP values. Myelin predictions based on these three parameters yielded stronger correlations with the MBP values in the P10 mouse brain corpus callosum than any single MR parameter. In the motor cortex, combining T₂, T₁/T₂, and radial kurtosis could explain over 90% of the variance in the MBP values at P10. This study demonstrates the utility of multiparametric MRI in improving the detection of myelin changes in the mouse brain.

PURPOSE/OBJECTIVE:To develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue. METHODS:A physics-based unsupervised deep learning network for estimation of fatty acid composition-network (FAC-Net) is proposed to estimate the number of double bonds and number of methylene-interrupted double bonds from multi-echo bipolar gradient-echo data, which are subsequently converted to saturated, mono-unsaturated, and poly-unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post-menopausal women scanned using a whole-body 3T MRI system between February 2022 and January 2024. The post-menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy-proven breast cancer. RESULTS: > 0.9 except chain length). The FAC values measured from scan and rescan data of the control group showed no significant difference between the two scans. The FAC measurements of the cancer group conducted before contrast and after contrast showed a significant difference in saturated fatty acid and mono-unsaturated fatty acid. The cancer group has higher saturated fatty acid than the control group, although not statistically significant. CONCLUSION/CONCLUSIONS:The results in this study suggest that the proposed FAC-Net can be used to measure the FAC of mammary adipose tissue from gradient-echo MRI data of the breast.

BACKGROUND:Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events. METHODS:A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both non-contrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization). RESULTS:Our cohort included 3599 patients (median age 60 years, 62% male, 74% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r=0.56, P<0.001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683 to 0.718]; AUC for PREVENT plus AAC 0.782 [0.767 to 0.797]; P<0.001). There were 324 MACE, of which 246 were MCE. Following adjustment for the 10-year cardiovascular disease PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P<0.001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P<0.001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE. CONCLUSIONS:Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.

BACKGROUND:MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization. PURPOSE/OBJECTIVE:To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial. STUDY TYPE/METHODS:Retrospective and prospective. POPULATION/METHODS:The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI). ASSESSMENT/RESULTS:The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner. STATISTICAL TESTS/METHODS:AUROC with 95% confidence intervals (CI) was used to evaluate model performance. RESULTS:In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations. DATA CONCLUSION/CONCLUSIONS:The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows. EVIDENCE LEVEL/METHODS:1. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm³ resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm (Braitenberg and Schüz, 2013), leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2,102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.

OBJECTIVES/OBJECTIVE:Magnetization-prepared rapid gradient-echo (MP-RAGE) sequences are routinely acquired for brain exams, providing high conspicuity for enhancing lesions. Vessels, however, also appear bright, which can complicate the detection of small lesions. T1RESS (T1 relaxation-enhanced steady-state) sequences have been proposed as an alternative to MP-RAGE, offering improved lesion conspicuity and suppression of blood vessels. This work aims to evaluate the performance of radial T1RESS variants for motion-robust contrast-enhanced brain MRI. MATERIALS AND METHODS/METHODS:Radial stack-of-stars sampling was implemented for steady-state free-precession-based rapid T1RESS acquisition with saturation recovery preparation. Three variants were developed using a balanced steady-state free-precession readout (bT1RESS), an unbalanced fast imaging steady precession (FISP) readout (uT1RESS-FISP), and an unbalanced reversed FISP readout (uT1RESS-PSIF). Image contrast was evaluated in numerical simulations and phantom experiments. The motion robustness of radial T1RESS was demonstrated with a motion phantom. Four patients and six healthy volunteers were scanned at 3 T and 0.55 T. Extensions were developed combining T1RESS with GRASP for dynamic imaging, with GRAPPA for accelerated scans, and with Dixon for fat/water separation. RESULTS:In simulations and phantom scans, uT1RESS-FISP provided higher signal intensity for regions with lower T1 values (<500 ms) compared with MP-RAGE. In motion experiments, radial uT1RESS-FISP showed fewer artifacts than MP-RAGE and Cartesian uT1RESS-FISP. In patients, both unbalanced uT1RESS variants provided higher lesion conspicuity than MP-RAGE. Blood vessels appeared bright with MP-RAGE, gray with uT1RESS-FISP, and dark with uT1RESS-PSIF. At 0.55 T, bT1RESS provided high signal-to-noise ratio T1-weighted images without banding artifacts. Lastly, dynamic T1RESS images with a temporal resolution of 10.14 seconds/frame were generated using the GRASP algorithm. CONCLUSIONS:Radial T1RESS sequences offer improved lesion conspicuity and motion robustness and enable dynamic imaging for contrast-enhanced brain MRI. Both uT1RESS variants showed higher tumor-to-brain contrast than MP-RAGE and may find application as alternative techniques for imaging uncooperative patients with small brain lesions.

Subjective cognitive decline (SCD) is associated with preclinical Alzheimer's disease (AD). Suboptimal sleep is also a risk factor for cognitive decline, but with unclear relationship to SCD. We conducted a retrospective cross-sectional study in a biracial research cohort of 148 cognitively normal older adults who underwent quantification of SCD (Cognitive Change Index; CCI), sleepiness (Epworth Sleepiness Scale; ESS), depression (Geriatric Depression Scale; GDS), and amyloid/tau PET. ESS score was associated with total, amnestic, and non-amnestic CCI scores, after adjustment for GDS, amyloid/tau burden, and race. This supports future longitudinal work on how sleepiness impacts SCD outcomes.

PURPOSE/OBJECTIVE:To implement and evaluate the feasibility of brain spin-lattice relaxation in the rotating frame (T1ρ) mapping using a novel optimized pulse sequence that incorporates weighted spin-lock acquisitions, enabling high-resolution three-dimensional (3D) mapping. METHODS:The optimized variable flip-angle framework, previously proposed for knee T1ρ mapping, was enhanced by integrating weighted spin-lock acquisitions. This strategic combination significantly boosts signal-to-noise ratio (SNR) while reducing data acquisition time, facilitating high-resolution 3D-T1ρ mapping of the brain. The proposed sequence was compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo sequence snapshots (MAPSS). RESULTS:) MAPSS in SNR. The weighted spin-lock acquisition combined with optimized variable flip angle improved the SNR over optimized variable flip angle alone by about 28%. CONCLUSION/CONCLUSIONS:Compared with the 20-min MAPSS sequence for brain T1ρ mapping, the proposed learned high-resolution 3D pulse sequence simultaneously achieved a 2.3-fold improvement in effective (3.2-fold nominal) spatial resolution, a 1.1-fold improvement in SNR, and a 2.5-fold reduction in scan time.

OBJECTIVES/OBJECTIVE:This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. METHODS:A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. RESULTS:Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. CONCLUSION/CONCLUSIONS:The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. CLINICAL RELEVANCE STATEMENT/CONCLUSIONS:Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. KEY POINTS/CONCLUSIONS:Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.