Xiongchao Chen1, Bo Zhou1, Huidong Xie1, Luyao Shi1, Hui Liu2,3, Wolfgang Holler4, MingDe Lin2,5, Yi-Hwa Liu6,7, Edward J Miller2,6, Albert J Sinusas1,2,6, Chi Liu8,9. 1. Department of Biomedical Engineering, Yale University, New Haven, CT, USA. 2. Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA. 3. Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China. 4. Visage Imaging GmbH, Berlin, Germany. 5. Visage Imaging, Inc, San Diego, CA, USA. 6. Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA. 7. Department of Biomedical Imaging and Radiological Sciences, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan. 8. Department of Biomedical Engineering, Yale University, New Haven, CT, USA. chi.liu@yale.edu. 9. Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA. chi.liu@yale.edu.
Abstract
PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.
PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.
Authors: Sergey V Nesterov; Chunlei Han; Maija Mäki; Sami Kajander; Alexandru G Naum; Hans Helenius; Irina Lisinen; Heikki Ukkonen; Mikko Pietilä; Esa Joutsiniemi; Juhani Knuuti Journal: Eur J Nucl Med Mol Imaging Date: 2009-04-30 Impact factor: 9.236
Authors: Karim Armanious; Tobias Hepp; Thomas Küstner; Helmut Dittmann; Konstantin Nikolaou; Christian La Fougère; Bin Yang; Sergios Gatidis Journal: EJNMMI Res Date: 2020-05-24 Impact factor: 3.138