ABSTRACT
BACKGROUND AND PURPOSE: Intra-cranial vessel wall imaging (IC-VWI) is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning optimized sequence using T1 weighted imaging. MATERIALS AND METHODS: Clinical and optimized Deep learning-based image reconstruction (DLBIR) T1 SPACE sequences were evaluated, comparing non-contrast sequences in ten healthy controls and post-contrast sequences in five consecutive patients. Images were reviewed on a Likert-like scale by four fellowship-trained neuroradiologists. Scores (range 1-4) were separately assigned for eleven vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness and homogenous CSF signal. Segment-wise scores were compared using paired samples t-tests. RESULTS: The scan time for the clinical and DLBIR sequences were 7:26 minutes and 5:23 minutes respectively. DLBIR images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in the majority of vessel segments on both pre and post contrast images. DLBIR images had lower background noise, higher image sharpness and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the DLBIR images. CONCLUSIONS: Our preliminary findings suggest that DLBIR optimized IC-VWI sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of ICVWI in clinical practice and should be further validated on a larger cohort. ABBREVIATIONS: DL deep learning; VWI = vessel wall imaging.
ABSTRACT
BACKGROUND: Ischemic nephropathy is a common cause of end-stage renal disease. Exploration of the mechanisms of deterioration of renal function is limited due to lack of noninvasive techniques available to study the single kidney. The Blood Oxygen Level-Dependent (BOLD) MRI method can measure deoxyhemoglobin and therefore indirectly estimates renal oxygen content, but has never been evaluated in renal artery stenosis (RAS). This study was therefore designed to test if BOLD can detect the characteristic of renal hypoxia induced by RAS. METHODS: RAS was induced in 8 pigs using an occluder placed around the right renal artery. Renal blood flow (RBF) was measured continuously with an ultrasound probe. BOLD signal was measured bilaterally in the cortex and medulla (as the slope of the logarithm of MR signal) at baseline and at the lower limit of RBF autoregulation. The measurements were then repeated during six sequential graded decreases in RBF (80 to 0% of baseline) and during recovery. RESULTS: During the control period, BOLD signals were not significantly different between the right and the left kidneys. In the occluded kidney, BOLD signal of the cortex (19.3 +/- 1.9/s) and the medulla (17.3 +/- 2.0/s) increased during occlusion gradually and significantly (P < 0.0001) to a maximum (at total occlusion) of 33.8 +/- 2.0/s (+79%) and 29.8 +/- 2.3/s (+78%), respectively, and returned to baseline values during recovery. CONCLUSION: This study shows that the BOLD technique can noninvasively detect change in intra-renal oxygenation during an acute reduction of RBF. This study provides a strong rationale for developing the BOLD method for the detection and evaluation of renal hypoxia induced by RAS, which may be potentially applicable in humans.
