Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network. Issue 4 (18th November 2020)

• Record Type: Journal Article
• Authors: Zhou, Liangdong; Zhang, Qihao; Spincemaille, Pascal; Nguyen, Thanh D.; Morgan, John; Dai, Weiying; Li, Yi; Gupta, Ajay; Prince, Martin R.; Wang, Yi
• Is Part Of: Magnetic resonance in medicine. Volume 85:Issue 4(2021)
• Page Start: 2247
• Page End: 2262
• Publication Date: 2020-11-18
• Subjects: arterial spin labeling (ASL) -- flow quantification -- mass transport -- perfusion -- quantitative transport mapping (QTM); Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28584
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Proof‐of‐concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer‐labeled MRI data. Theory and Methods: To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time‐resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety's method that uses a global arterial input function. Multiple post‐label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility. Results: Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety's method (45.7%, 2.5‐fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety's method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region. Conclusions: QTM flow velocity mapping is feasible from multi‐delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety's method with deconvolution in time only.