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

Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus 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 component 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. To overcome the challenges with the requirement of a CT scan for AC in DaT SPECT, we propose a deep learning (DL)-based transmission-less AC method for DaT-SPECT (DaT-CTLESS). An in silico imaging trial, titled ISIT-DaT, was designed to evaluate the performance of DaT-CTLESS on the regional uptake quantification task. We observed that DaT-CTLESS yielded a significantly higher correlation with CTAC than that between UAC and CTAC on the regional DaT uptake quantification task. Further, DaT-CLTESS had an excellent agreement with CTAC on this task, significantly outperformed UAC in distinguishing patients with normal versus reduced putamen SBR, yielded good generalizability across two scanners, was generally insensitive to intra-regional uptake heterogeneity, demonstrated good repeatability, exhibited robust performance even as the size of the training data was reduced, and generally outperformed the other considered DL methods on the task of quantifying regional uptake across different training dataset sizes. These results provide a strong motivation for further clinical evaluation of DaT-CTLESS.