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Chen X, Liu C. Deep-learning-based methods of attenuation correction for SPECT and PET. J Nucl Cardiol 2023; 30:1859-1878. [PMID: 35680755 DOI: 10.1007/s12350-022-03007-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/02/2022] [Indexed: 10/18/2022]
Abstract
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT, 06520, USA.
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Apostolopoulos ID, Papandrianos NI, Feleki A, Moustakidis S, Papageorgiou EI. Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies. EJNMMI Phys 2023; 10:6. [PMID: 36705775 PMCID: PMC9883373 DOI: 10.1186/s40658-022-00522-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/19/2022] [Indexed: 01/28/2023] Open
Abstract
Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.
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Affiliation(s)
- Ioannis D. Apostolopoulos
- grid.11047.330000 0004 0576 5395Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece ,grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Nikolaos I. Papandrianos
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Anna Feleki
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Serafeim Moustakidis
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece ,AIDEAS OÜ, 10117 Tallinn, Estonia
| | - Elpiniki I. Papageorgiou
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
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Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. Eur J Nucl Med Mol Imaging 2022; 49:3046-3060. [PMID: 35169887 PMCID: PMC9253078 DOI: 10.1007/s00259-022-05718-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/06/2022] [Indexed: 12/22/2022]
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.
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Affiliation(s)
- Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Huidong Xie
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Hui Liu
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China
| | | | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Visage Imaging, Inc, San Diego, CA, USA
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
- Department of Biomedical Imaging and Radiological Sciences, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Edward J Miller
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale University, CT, New Haven, USA.
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