Development and in silico imaging trial evaluation of a deep‐learning‐based transmission‐less attenuation compensation method for DaT SPECT

作者：Zitong Yu, Md Ashequr Rahman, Zekun Li, Chunwei Ying, Hongyu An, Tammie L.S. Benzinger, Richard Laforest, Jingqin Luo, Scott A. Norris, Abhinav K. Jha · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.17976 · 被引用次数：3 · 研究领域：Medical Imaging Techniques and Applications、Parkinson's Disease Mechanisms and Treatments、Advanced X-ray and CT Imaging

BACKGROUND: Quantitative measures of dopamine transporter (DaT) uptake in the caudate, putamen, and globus pallidus (GP) derived from DaT-single-photon emission computed tomography (SPECT) images are being investigated as biomarkers to diagnose, assess disease status, and track the progression of Parkinsonism. Reliable quantification from DaT-SPECT images requires performing attenuation compensation (AC), typically with a separate x-ray CT scan. Such CT-based AC (CTAC) has multiple challenges, a key one being the non-availability of x-ray CT components on many clinical SPECT systems. Even when a CT is available, the additional CT scan leads to increased radiation dose, costs, and complexity; potential quantification errors due to SPECT-CT misalignment; and higher training and regulatory requirements. PURPOSE: To overcome the challenges with the requirement of a CT scan for AC in DaT SPECT, we develop a transmission-less AC method for DaT SPECT and validate the method in a clinically realistic setting using an in silico imaging trial. METHOD: Integrating concepts from physics and deep learning (DL), we propose a DL-based transmission-less AC method for DaT-SPECT (DaT-CTLESS). In this method, an initial attenuation map reconstructed from scatter-energy window projection is segmented into different regions using a U-net-based network trained on CT scans. Each region is assigned a predefined attenuation coefficient, yielding an attenuation map for AC. An in silico imaging trial, ...