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.

Estimating Flow Direction of Circle of Willis Using Dynamic Arterial Spin Labeling Magnetic Resonance Angiography.

BACKGROUND AND PURPOSE: The Circle of Willis (COW) is a crucial mechanism for cerebral collateral circulation. This proof-ofconcept study aims to develop and assess an analysis method to characterize the hemodynamics of the arterial segments in COW using arterial spin labeling (ASL) based non-contrast enhanced dynamic magnetic resonance angiography (dMRA). MATERIALS AND METHODS: The developed analysis method uses a graph model, bootstrap strategy, and ensemble learning methodologies to determine the time-curve shift from ASL dMRA to estimate the flow direction within the COW. The performance of the method was assessed on 52 subjects, using the flow direction, either antegrade or retrograde, derived from 3D phase contrast (PC) MRI as the reference. RESULTS: A total of 340 arterial segments in COW were evaluated, among which 30 (8.8%) had retrograde flow according to 3D PC. The ASL dMRA-based flow direction estimation has an accuracy, sensitivity, and specificity of 95.47%, 80%, and 96.34%, respectively. CONCLUSIONS: Using ASL dMRA and the developed image analysis method to estimate the flow direction in COW is feasible. This study provides a new method to assess the hemodynamics of the COW, which could be useful for the diagnosis and study of cerebrovascular diseases. ABBREVIATIONS: COW = Circle of Willis; ASL = arterial spin labeling; dMRA =dynamic magnetic resonance angiography; PC = phase contrast.

Intra-examination agreement between multi-echo gradient echo and confounder-corrected chemical shift-encoded MR sequences for R2* estimation as a biomarker of liver iron content in patients with a wide range of T2*/R2* and proton density fat fraction values.

PURPOSE: To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement. METHODS: Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5 T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec-1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland-Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses. RESULTS: 49 patients were included. Mean MEGE-R2* was 94.2 s-1 (range: 31.0-737.1) and mean CSE-R2* 87.7 (29.7-748.1). Mean CSE-PDFF was 9.12% (0.1-43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235 s-1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%. CONCLUSION: MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis.