A tissue‐fraction estimation‐based segmentation method for quantitative dopamine transporter SPECT. Issue 8 (29th June 2022)

• Record Type: Journal Article
• Authors: Liu, Ziping; Moon, Hae Sol; Li, Zekun; Laforest, Richard; Perlmutter, Joel S.; Norris, Scott A.; Jha, Abhinav K.
• Is Part Of: Medical physics. Volume 49:Issue 8(2022)
• Page Start: 5121
• Page End: 5137
• Publication Date: 2022-06-29
• Subjects: objective task‐based evaluation -- parkinson's disease -- partial‐volume effects -- quantification -- segmentation -- single‐photon emission computed tomography -- tissue‐fraction effects; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15778
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter–single‐photon emission computed tomography (DaT‐SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT‐SPECT images is challenging, a major reason being partial‐volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite‐sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT‐SPECT images. Purpose: Design and objectively evaluate a fully automated tissue‐fraction estimation‐based segmentation method that segments the caudate, putamen, and GP from DaT‐SPECT images. Methods: The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three‐dimensional DaT‐SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross‐entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep‐learning‐based approach. Results: Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed ( p < 0.01 $p < 0.01$ ) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen. Conclusions: The proposed tissue‐fraction estimation‐based segmentation method for DaT‐SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical‐phantom and patient studies.