ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U‐Nets

TL;DR

This paper introduces a new deep learning model for segmenting blood vessels in 4D MRI scans, which performs better than existing methods.

Contribution

The novel 4DST U-Net architecture improves vessel segmentation in ASL-based 4D MRA by combining spatial and temporal data efficiently.

Findings

4DST achieved the highest DSC, clDice, and HD scores compared to other models.

4DST outperformed other models in sensitivity across various SNR and arterial transit time ranges.

4DST segmentations produced vessel lengths and branch counts closer to ground truths.

Abstract

To propose a spatio‐temporal U‐Net based network (4DST) that exploits both spatial and dynamic information while avoiding memory‐intensive 4D convolutional layers for ASL‐based non‐contrast enhanced 4‐dimensional MR angiography (4D MRA) vessel segmentation.

Pulsed ASL‐based 4D MRA data were collected on 35 healthy volunteers and 5 arteriovenous malformation patients. Spatial only (2D, 3D) and spatio‐temporal U‐Net variations (including the proposed 4DST) were tested. Two recently developed methods, including feature‐based isolation forest and BRAVE‐Net, were used for comparison. Dice‐Sørensen coefficient (DSC), center‐line Dice (clDice), Hausdorff distance (HD), precision, accuracy, specificity, and sensitivity were calculated. Sensitivity was analyzed relative to SNR and arterial transit time (ATT) to explore detectability. From graph analysis, total vessel length, number of branches, and number of endpoints were reported.

4DST achieved the best DSC, clDice, and HD (0.876 ± 0.03, 0.865 ± 0.02, 6.241 ± 0.95, respectively). 4DST outperformed all other models across the SNR range of 1 to 10 and arterial transit time range of 500 to 800 ms in sensitivity. Last, the 4DST segmentations yielded total lengths and the number of branch splits that more closely matched the ground truths compared to the other models.

The proposed 4DST network architecture offers an overall improvement in 4D MRA vessel segmentation performance over the compared methods and provides the framework for an end‐to‐end trainable model for spatio‐temporal datasets. Additionally, 4DST requires minimal pre/post‐processing steps, rendering it an attractive solution for pulsed ASL‐based 4D MRA vessel segmentation.

Figures