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Roya M, Mostafapour S, Mohr P, Providência L, Li Z, van Snick JH, Brouwers AH, Noordzij W, Willemsen ATM, Dierckx RAJO, Lammertsma AA, Glaudemans AWJM, Tsoumpas C, Slart RHJA, van Sluis J. Current and Future Use of Long Axial Field-of-View Positron Emission Tomography/Computed Tomography Scanners in Clinical Oncology. Cancers (Basel) 2023; 15:5173. [PMID: 37958347 PMCID: PMC10648837 DOI: 10.3390/cancers15215173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
The latest technical development in the field of positron emission tomography/computed tomography (PET/CT) imaging has been the extension of the PET axial field-of-view. As a result of the increased number of detectors, the long axial field-of-view (LAFOV) PET systems are not only characterized by a larger anatomical coverage but also by a substantially improved sensitivity, compared with conventional short axial field-of-view PET systems. In clinical practice, this innovation has led to the following optimization: (1) improved overall image quality, (2) decreased duration of PET examinations, (3) decreased amount of radioactivity administered to the patient, or (4) a combination of any of the above. In this review, novel applications of LAFOV PET in oncology are highlighted and future directions are discussed.
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Affiliation(s)
- Mostafa Roya
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Samaneh Mostafapour
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Philipp Mohr
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Laura Providência
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Zekai Li
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Johannes H. van Snick
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Adrienne H. Brouwers
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Walter Noordzij
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Antoon T. M. Willemsen
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Rudi A. J. O. Dierckx
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Adriaan A. Lammertsma
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Andor W. J. M. Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Charalampos Tsoumpas
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enchede, The Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; (S.M.); (P.M.); (L.P.); (Z.L.); (J.H.v.S.); (A.H.B.); (W.N.); (A.T.M.W.); (R.A.J.O.D.); (A.A.L.); (A.W.J.M.G.); (C.T.); (J.v.S.)
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Hu J, Mougiakakou S, Xue S, Afshar-Oromieh A, Hautz W, Christe A, Sznitman R, Rominger A, Ebner L, Shi K. Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease. EUROPEAN PHYSICAL JOURNAL PLUS 2023; 138:391. [PMID: 37192839 PMCID: PMC10165296 DOI: 10.1140/epjp/s13360-023-03745-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 01/25/2023] [Indexed: 05/18/2023]
Abstract
Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT-PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health.
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Affiliation(s)
- Jiaxi Hu
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Song Xue
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Wolf Hautz
- Department of University Emergency Center of Inselspital, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
| | - Andreas Christe
- Department of Radiology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Lukas Ebner
- Department of Radiology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
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Liu J, Ren S, Wang R, Mirian N, Tsai YJ, Kulon M, Pucar D, Chen MK, Liu C. Virtual high-count PET image generation using a deep learning method. Med Phys 2022; 49:5830-5840. [PMID: 35880541 PMCID: PMC9474624 DOI: 10.1002/mp.15867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/07/2022] [Accepted: 07/18/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. METHODS The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images. RESULTS The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference. CONCLUSIONS A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.
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Affiliation(s)
- Juan Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Sijin Ren
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Rui Wang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China
| | - Niloufarsadat Mirian
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Yu-Jung Tsai
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Michal Kulon
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Darko Pucar
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 06520, USA
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Nai YH, Loi HY, O'Doherty S, Tan TH, Reilhac A. Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images. Jpn J Radiol 2022; 40:1290-1299. [PMID: 35809210 DOI: 10.1007/s11604-022-01311-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/19/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images and the minimum counts required. MATERIALS AND METHODS 33 standard dose (SD) PET images, were used to simulate LD PET images at seven-count levels of 0.25, 0.5, 1, 2, 5, 7.5 and 10 million (M) counts. Image quality transfer (IQT), a ML algorithm that uses decision tree and patch-sampling was compared to two DL networks-HighResNet (HRN) and deep-boosted regression (DBR). Supervised training was performed by training the ML and DL algorithms with matched-pair SD and LD images. Image quality evaluation and clinical lesion detection tasks were performed by three readers. Bias in 53 radiomic features, including mean SUV, was evaluated for all lesions. RESULTS ML- and DL-estimated images showed higher signal and smaller error than LD images with optimal image quality recovery achieved using LD down to 5 M counts. True positive rate and false discovery rate were fairly stable beyond 5 M counts for the detection of small and large true lesions. Readers rated average or higher ratings to images estimated from LD images of count levels above 5 M only, with higher confidence in detecting true lesions. CONCLUSION LD images with a minimum of 5 M counts (8.72 MBq for 10 min scan or 25 MBq for 3 min scan) are required for optimal clinical use of ML and DL, with slightly better but more varied performance shown by DL.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.
| | - Hoi Yin Loi
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Sophie O'Doherty
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Teng Hwee Tan
- Department of Radiation Oncology, National University Cancer Institute, Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Fielding PA, Su KH, Johnsen R, Kotasidis F, Jansen FP, McGowan DR. Image enhancement of whole-body oncology [ 18F]-FDG PET scans using deep neural networks to reduce noise. Eur J Nucl Med Mol Imaging 2022; 49:539-549. [PMID: 34318350 PMCID: PMC8803788 DOI: 10.1007/s00259-021-05478-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/20/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. METHODS List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). RESULTS OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. CONCLUSION Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.
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Affiliation(s)
| | | | | | - Kevin M Bradley
- Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK.
- Department of Oncology, University of Oxford, Oxford, UK.
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Liu J, Malekzadeh M, Mirian N, Song TA, Liu C, Dutta J. Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement. PET Clin 2021; 16:553-576. [PMID: 34537130 PMCID: PMC8457531 DOI: 10.1016/j.cpet.2021.06.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.
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Affiliation(s)
- Juan Liu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Masoud Malekzadeh
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA
| | - Niloufar Mirian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Ultra-low dose whole-body CT for attenuation correction in a dual tracer PET/CT protocol for multiple myeloma. Phys Med 2021; 84:1-9. [PMID: 33799056 DOI: 10.1016/j.ejmp.2021.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/22/2021] [Accepted: 03/13/2021] [Indexed: 01/17/2023] Open
Abstract
PURPOSE To investigate within phantoms the minimum CT dose allowed for accurate attenuation correction of PET data and to quantify the effective dose reduction when a CT for this purpose is incorporated in the clinical setting. METHODS The NEMA image quality phantom was scanned within a large parallelepiped container. Twenty-one different CT images were acquired to correct attenuation of PET raw data. Radiation dose and image quality were evaluated. Thirty-one patients with proven multiple myeloma who underwent a dual tracer PET/CT scan were retrospectively reviewed. 18F-fluorodeoxyglucose PET/CT included a diagnostic whole-body low dose CT (WBLDCT: 120 kV-80mAs) and 11C-Methionine PET/CT included a whole-body ultra-low dose CT (WBULDCT) for attenuation correction (100 kV-40mAs). Effective dose and image quality were analysed. RESULTS Only the two lowest radiation dose conditions (80 kV-20mAs and 80 kV-10mAs) produced artifacts in CT images that degraded corrected PET images. For all the other conditions (CTDIvol ≥ 0.43 mGy), PET contrast recovery coefficients varied less than ± 1.2%. Patients received a median dose of 6.4 mSv from diagnostic CT and 2.1 mSv from the attenuation correction CT. Despite the worse image quality of this CT, 94.8% of bone lesions were identifiable. CONCLUSION Phantom experiments showed that an ultra-low dose CT can be implemented in PET/CT procedures without any noticeable degradation in the attenuation corrected PET scan. The replacement of the standard CT for this ultra-low dose CT in clinical PET/CT scans involves a significant radiation dose reduction.
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