Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity.

PURPOSE: To develop an automated deep learning model for MRI-based segmentation and detection of intracranial arterial calcification. METHODS: A novel deep learning model under the variational autoencoder framework was developed. A theoretically grounded dissimilarity loss was proposed to refine network features extracted from MRI and restrict their complexity, enabling the model to learn more generalizable MR features that enhance segmentation accuracy and robustness for detecting calcification on MRI. RESULTS: The proposed method was compared with nine baseline methods on a dataset of 113 subjects and showed superior performance (for segmentation, Dice similarity coefficient: 0.620, area under precision-recall curve [PR-AUC]: 0.660, 95% Hausdorff Distance: 0.848 mm, Average Symmetric Surface Distance: 0.692 mm; for slice-wise detection, F1 score: 0.823, recall: 0.764, precision: 0.892, PR-AUC: 0.853). For clinical needs, statistical tests confirmed agreement between the true calcification volumes and predicted values using the proposed approach. Various MR sequences, namely T1, time-of-flight, and SNAP, were assessed as inputs to the model, and SNAP provided unique and essential information pertaining to calcification structures. CONCLUSION: The proposed deep learning model with a dissimilarity loss to reduce feature complexity effectively improves MRI-based identification of intracranial arterial calcification. It could help establish a more comprehensive and powerful pipeline for vascular image analysis on MRI.

T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects.

T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.

Pathophysiology of carotid atherosclerosis: Calcification, intraplaque haemorrhage and pulse pressure as key players.

PURPOSE: Intraplaque haemorrhage (IPH) is a well-known risk factor for faster plaque progression (volume increase); however, its etiology is unclear. We aimed at determining what other local plaque- and systemic factors contribute to plaque progression and to the development and progression of IPH. METHODS: We examined 98 asymptomatic participants with carotid plaque using serial multi-contrast magnetic resonance imaging. We measured the percent of wall volume (%WV=100 x [wall volume] / [total vessel volume]) and measured IPH and calcification volumes. We used generalized estimating equations-based regression to analyze predictors of %WV change and new IPH while accounting for covariates (sex, age and statin use), and multiple non-independent observations per participant. RESULTS: Total follow-up was 1.8 ± 0.8 years on average. The presence of IPH (ß: 0.6 %/y, p = 0.033) and calcification (ß: 1.2 %/y, p = 0.028) were each associated with faster plaque progression. New IPH, detected on a subsequent scan in 4 % of arteries that did not initially have IPH, was associated with larger calcification (odds ratio [OR]: 2.6 per 1-SD increase, p = 0.038) and higher pulse pressure (OR: 2.3 per 1-SD increase, p = 0.016). Larger calcification was associated with greater increases in pulse pressure (ß: 1.4 mm Hg/y per 1-SD increase, p = 0.040). CONCLUSIONS: IPH and calcification are each independently associated with faster plaque progression. The association of carotid calcification to increased pulse pressure and new IPH development suggests a possible mechanism by which calcification drives IPH development and plaque progression.