Apparent diffusion coefficient as a quantitative biomarker for prostate cancer treatment response on a 1.5 Tesla magnetic resonance-linear accelerator: Impact of image registration and acquisition type

表观扩散系数作为前列腺癌治疗反应的定量生物标志物在1.5特斯拉磁共振直线加速器上的应用:图像配准和采集类型的影响

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Abstract

BACKGROUND AND PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a quantitative biomarker for cancer detection and treatment monitoring. On magnetic resonance-linear accelerator (MR-Linac) systems, diffusion-weighted echo planar imaging (DW-EPI) suffers from geometric distortion, reducing the repeatability of apparent diffusion coefficient (ADC) measurements. This study evaluated the effect of low-distortion split acquisition of fast spin-echo signal (SPLICE) sequences, and of image registration on the repeatability coefficient (RC) of ADC. MATERIALS AND METHODS: ADC bias, repeatability, signal-to-noise ratio (SNR) and geometric fidelity were measured in a diffusion phantom using three DW-EPI and two DW-SPLICE protocols. ADC short-term and long-term RCs were measured in healthy volunteers. In patients, the registration of DW-EPI to unweighted images (b0) was tested for its effect on RC in gross tumour volume (GTV) and non-tumour prostate (NT-P), and for its ability to detect significant ADC changes. RESULTS: Phantom experiments showed strong linear correlation with ground-truth ADC (R(2) > 0.99). Among EPI protocols, DW-EPI-AP offered the best balance of high SNR and low RC, while Z-direction encoded DW-EPI was the most variable. Both DW-SPLICE variants exhibited reduced distortion compared with EPI but poorer repeatability. In volunteers, long-term RCs (8.0-33.7 %) varied more than short-term RCs (8.9-15.4 %). In patients, registration improved RCs (GTV: 28.0 → 25.1 %; NT-P: 19.6 → 12.6 %) and improved detection of significant ADC change in patients (GTV: 0/6 → 1/6; NT-P: 2/6 → 5/6). CONCLUSION: RC and accuracy of DW-EPI agrees with published literature and improves after registration. DW-SPLICE shows lower geometric distortion but would require further optimization and validation to improve repeatability.

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