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Nigam S, Gjelaj E, Wang R, Wei GW, Wang P. Machine Learning and Deep Learning Applications in Magnetic Particle Imaging. J Magn Reson Imaging 2024. [PMID: 38358090 PMCID: PMC11324856 DOI: 10.1002/jmri.29294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Saumya Nigam
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Elvira Gjelaj
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, USA
| | - Rui Wang
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Guo-Wei Wei
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biochemistry and Molecular Biology, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Ping Wang
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
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Sun J, Jiang H, Du Y, Li CY, Wu TH, Liu YH, Yang BH, Mok GSP. Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT. J Nucl Cardiol 2023; 30:970-985. [PMID: 35982208 DOI: 10.1007/s12350-022-03045-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 06/13/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). METHODS Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. RESULTS cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. CONCLUSIONS Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.
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Affiliation(s)
- Jingzhang Sun
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China
| | - Han Jiang
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China
| | - Yu Du
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China
| | - Chien-Ying Li
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Yi-Hwa Liu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Bang-Hung Yang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China.
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Du Y, Shang J, Sun J, Wang L, Liu YH, Xu H, Mok GSP. Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT. J Nucl Cardiol 2023; 30:1022-1037. [PMID: 36097242 DOI: 10.1007/s12350-022-03092-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/31/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch. METHODS One hundred patients with different 99mTc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients 99mTc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC. RESULTS Mismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled. CONCLUSION DL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.
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Affiliation(s)
- Yu Du
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China
| | - Jingjie Shang
- Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jingzhang Sun
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Lu Wang
- Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yi-Hwa Liu
- Department of Internal Medicine (Cardiology), Yale University School of Medicine, New Haven, CT, USA
| | - Hao Xu
- Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China.
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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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Sun J, Du Y, Li C, Wu TH, Yang B, Mok GSP. Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising. Quant Imaging Med Surg 2022; 12:3539-3555. [PMID: 35782241 PMCID: PMC9246746 DOI: 10.21037/qims-21-1042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/18/2022] [Indexed: 11/12/2023]
Abstract
BACKGROUND Myocardial perfusion (MP) SPECT is a well-established method for diagnosing cardiac disease, yet its radiation risk poses safety concern. This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images. METHODS One hundred male and female patients with different 99mTc-sestamibi activity distributions, organ and body sizes were simulated by a population of digital 4D Extended Cardiac Torso (XCAT) phantoms. Realistic noisy SPECT projections of full dose of 987 MBq injection and 16 min acquisition, and low dose ranged from 1/20 to 1/2 of the full dose, were generated by an analytical projector from the right anterior oblique (RAO) to the left posterior oblique (LPO) positions. Additionally, twenty patients underwent ~1,184 MBq 99mTc-sestamibi stress SPECT/CT scan were also retrospectively recruited for the study. For each patient, low dose SPECT images (7/10 to 1/10 of full dose) were generated from the full dose list mode data. Our Pix2Pix GAN model was trained with full dose and low dose reconstructed SPECT image pairs. Normalized mean square error (NMSE), structural similarity index (SSIM), coefficient of variation (CV), full-width-at-half-maximum (FWHM) and relative defect size differences (RSD) of Pix2Pix GAN processed images were evaluated along with a reference convolutional auto encoder (CAE) network and post-reconstruction filters. RESULTS NMSE values of 0.0233±0.004 vs. 0.0249±0.004 and 0.0313±0.007 vs. 0.0579±0.016 were obtained on 1/2 and 1/20 dose level for Pix2Pix GAN and CAE in the simulation study, while they were 0.0376±0.010 vs. 0.0433±0.010 and 0.0907±0.020 vs. 0.1186±0.025 on 7/10 and 1/10 dose level in the clinical study. Similar results were also obtained from the SSIM, CV, FWHM and RSD values. Overall, the use of Pix2Pix GAN was superior to other denoising methods in all physical indices, particular in the lower dose levels in the simulation and clinical study. CONCLUSIONS The Pix2Pix GAN method is effective to reduce the noise level of low dose MP SPECT. Further studies on clinical performance are warranted to demonstrate its full clinical effectiveness.
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Affiliation(s)
- Jingzhang Sun
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Yu Du
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - ChienYing Li
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei
| | - BangHung Yang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei
| | - Greta S. P. Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China
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