Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.

ABSTRACT:

Purpose

Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine learning has proven its value in medical image segmentation, including the kidneys. This study presents a fully automatic workflow for renal cortex perfusion quantification by including machine learning-based segmentation.

Methods

Fully automatic workflow was achieved by construction of a cascade of 3 U-nets to replace manual segmentation in ASL quantification. All 1.5T ASL-MRI data, including M₀ , T₁ , and ASL label-control images, from 10 healthy volunteers was used for training (dataset 1). Trained cascade pe

SUBMITTER: Bones IK 

PROVIDER: S-EPMC9297892 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature