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Park MA, Zaha VG, Badawi RD, Bowen SL. Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1125-1137. [PMID: 37948143 PMCID: PMC10986771 DOI: 10.1109/tmi.2023.3330668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to reconstruct μ -maps based on physics principles alone. The low 30 cm3 volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured ≤ 5 % bias in activity concentrations for PET images generated with sTX-MLAA and a TX source strength ≥ 12.7 MBq, relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using counts segmented from the transmission source alone for AC. Results suggest that sTX-MLAA will enable quantitative PET during cardiac PET/CT and PET/MR of human patients.
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Li X, Johnson JM, Strigel RM, Bancroft LCH, Hurley SA, Estakhraji SIZ, Kumar M, Fowler AM, McMillan AB. Attenuation correction and truncation completion for breast PET/MR imaging using deep learning. Phys Med Biol 2024; 69:045031. [PMID: 38252969 DOI: 10.1088/1361-6560/ad2126] [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: 05/22/2023] [Accepted: 01/22/2024] [Indexed: 01/24/2024]
Abstract
Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.
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Affiliation(s)
- Xue Li
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States of America
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Jacob M Johnson
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Roberta M Strigel
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Samuel A Hurley
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - S Iman Zare Estakhraji
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Manoj Kumar
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- ICTR Graduate Program in Clinical Investigation, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Amy M Fowler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
| | - Alan B McMillan
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States of America
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
- University of Wisconsin Carbone Cancer Center, Madison, WI, United States of America
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Krokos G, MacKewn J, Dunn J, Marsden P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys 2023; 10:52. [PMID: 37695384 PMCID: PMC10495310 DOI: 10.1186/s40658-023-00569-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
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Affiliation(s)
- Georgios Krokos
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Jane MacKewn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
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Rajagopal A, Natsuaki Y, Wangerin K, Hamdi M, An H, Sunderland JJ, Laforest R, Kinahan PE, Larson PEZ, Hope TA. Synthetic PET via Domain Translation of 3-D MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:333-343. [PMID: 37396797 PMCID: PMC10311993 DOI: 10.1109/trpms.2022.3223275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.
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Affiliation(s)
- Abhejit Rajagopal
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Yutaka Natsuaki
- Department of Radiation Oncology, University of New Mexico, Albuquerque, NM 87131 USA
| | | | - Mahdjoub Hamdi
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Hongyu An
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - John J Sunderland
- Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA
| | - Richard Laforest
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63130 USA
| | - Paul E Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98195 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
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Zhang H, Wang J, Li N, Zhang Y, Cui J, Huo L, Zhang H. A quantitative clinical evaluation of simultaneous reconstruction of attenuation and activity in time-of-flight PET. BMC Med Imaging 2023; 23:35. [PMID: 36849906 PMCID: PMC9972693 DOI: 10.1186/s12880-023-00987-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/03/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND The maximum likelihood activity and attenuation (MLAA) reconstruction algorithm has been proposed to jointly estimate tracer activity and attenuation at the same time, and proven to be a promising solution to the CT attenuation correction (CT-AC) artifacts in PET images. This study aimed to perform a quantitative evaluation and clinical validation of the MLAA method. METHODS A uniform cylinder phantom filled with 18F-FDG solution was scanned to optimize the reconstruction parameters for the implemented MLAA algorithm. 67 patients who underwent whole-body 18F-FDG PET/CT scan were retrospectively recruited. PET images were reconstructed using MLAA and clinical standard OSEM algorithm with CT-AC (CT-OSEM). The mean and maximum standardized uptake values (SUVmean and SUVmax) in regions of interest (ROIs) of organs, high uptake lesions and areas affected by metal implants and respiration motion artifacts were quantitatively analyzed. RESULTS In quantitative analysis, SUVs in patient's organ ROIs between two methods showed R2 ranging from 0.91 to 0.98 and k ranging from 0.90 to 1.06, and the average SUVmax and SUVmean differences between two methods were within 10% range, except for the lung ROI, which was 10.5% and 16.73% respectively. The average SUVmax and SUVmean differences of a total of 117 high uptake lesions were 7.25% and 7.10% respectively. 20 patients were identified to have apparent respiration motion artifacts in the liver in CT-OSEM images, and the SUVs differences between two methods measured at dome of the liver were significantly larger than measured at middle part of the liver. 10 regions with obvious metal artifacts were identified in CT-OSEM images and the average SUVmean and SUVmax differences in metal implants affected regions were reported to be 52.90% and 56.20% respectively. CONCLUSIONS PET images reconstructed using MLAA are clinically acceptable in terms of image quality as well as quantification and it is a useful tool in clinical practice, especially when CT-AC may cause respiration motion and metal artifacts. Moreover, this study also provides technical reference and data support for the future iteration and development of PET reconstruction technology of SUV accurate quantification.
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Affiliation(s)
- Haiqiong Zhang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.,Medical Science Research Center (MRC), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jingnan Wang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Nan Li
- SinoUnion (Beijing) Healthcare Technologies Co., Ltd, Beijing, 100082, China
| | - Yue Zhang
- SinoUnion (Beijing) Healthcare Technologies Co., Ltd, Beijing, 100082, China
| | - Jie Cui
- SinoUnion (Beijing) Healthcare Technologies Co., Ltd, Beijing, 100082, China
| | - Li Huo
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Hui Zhang
- SinoUnion (Beijing) Healthcare Technologies Co., Ltd, Beijing, 100082, China. .,Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
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Shi L, Zhang J, Toyonaga T, Shao D, Onofrey JA, Lu Y. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Phys Med Biol 2023; 68. [PMID: 36584395 DOI: 10.1088/1361-6560/acaf49] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However,λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predictingμ-DL fromλ-MLAA andμ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies.Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data.Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures.Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.
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Affiliation(s)
- Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China
| | - John A Onofrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.,Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Department of Urology, Yale University, New Haven, CT, United States of America
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
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Hanaoka K, Watanabe S, Ishikawa D, Ishii K. [[PET] 2. Dedicated Breast PET System]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:90-96. [PMID: 36682784 DOI: 10.6009/jjrt.2023-2144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University
| | - Daisuke Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University
- Department of Radiology, Kindai University Faculty of Medicine
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Toyonaga T, Shao D, Shi L, Zhang J, Revilla EM, Menard D, Ankrah J, Hirata K, Chen MK, Onofrey JA, Lu Y. Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. Eur J Nucl Med Mol Imaging 2022; 49:3086-3097. [PMID: 35277742 PMCID: PMC10725742 DOI: 10.1007/s00259-022-05748-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/25/2022] [Indexed: 11/04/2022]
Abstract
A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). METHODS Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. RESULTS µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine. CONCLUSIONS The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.
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Affiliation(s)
- Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | | | | | - Kenji Hirata
- Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Yale New Haven Hospital, New Haven, CT, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Urology, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
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Pan T, Thomas MA, Luo D. Data-driven gated (DDG) CT: An automated respiratory gating method to enable DDG PET/CT. Med Phys 2022; 49:3597-3611. [PMID: 35324002 DOI: 10.1002/mp.15620] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/23/2022] [Accepted: 02/23/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND The accuracy of PET quantification and localization can be compromised if a misregistered CT is used for attenuation correction (AC) in PET/CT. As data-driven gating (DDG) continues to grow in clinical use, these issues are becoming more relevant with respect to solutions for gated CT. PURPOSE In this work, a new automated data-driven gated (DDG) CT method was developed to provide average CT and DDG CT for AC of PET and DDG PET, respectively. METHODS An automatic DDG CT was developed to provide the end-expiratory (EE) and end-inspiratory (EI) phases of images from low-dose cine CT images, with all phases being averaged to generate an average CT. The respiratory phases of EE and EI were determined according to lung region Hounsfield unit (HU) values and body outline contours. The average CT was used for AC of baseline PET and DDG CT at EE phase was used for AC of DDG PET at the quiescent or EE phase. The EI and EE phases obtained with DDG CT were used for assessing the magnitude of respiratory motion. The proposed DDG CT was compared to two commercial CT gating methods: 1) 4D CT (external device based) and 2) D4D CT (DDG based) in 38 patient data sets with respect to respiratory phase image selection, lung HU, lung volume, and image artifacts. In a separate set of twenty consecutive PET/CT studies containing a mix of 18 F-FDG, 68 Ga-Dotatate, and 64 Cu-Dotatate scans, the proposed DDG CT was compared with D4D CT for impacts on registration and quantification in DDG PET/CT. RESULTS In the EE phase, the images selected by DDG CT and 4D CT were identical 62.5±21.6% of the time, while DDG CT and D4D CT were 6.5±9.7%, and 4D CT and D4D CT were 8.6±12.2%. These differences in EE phase image selection were significant (p<0.0001). In the EI phase, the images selected by DDG CT and 4D CT were identical 68.2±18.9% of the time, DDG CT and D4D CT were 63.9±18.8%, and 4D CT and D4D CT were 61.2±19.8%. These differences were not significant. The mean lung HU and volumes were not statistically different (p > 0.1) among the three methods. In some studies, DDG CT was better than D4D or 4D CT in appropriate selection of the EE and EI phases, and D4D CT was found to reverse the EE and EI phases or not select the correct images by visual inspection. A statistically significant improvement of DDG CT over D4D CT for AC of DDG PET was also demonstrated with PET quantification analysis. When irregular breath cycles were present in the cine CT, DDG CT could be used to replace average CT for improved AC of baseline PET. CONCLUSION A new automatic DDG CT was developed to tackle the issues of misregistration and tumor motion in PET/CT imaging. DDG CT was significantly more consistent than D4D CT in selecting the EE phase images as the clinical standard of 4D CT. When compared to both commercial gated CT methods of 4D CT and D4D CT, DDG CT appeared to be more robust in the lower lung and upper diaphragm regions where misregistration and tumor motion often occur. DDG CT offered improved AC for DDG PET relative to D4D CT. In cases with irregular respiratory motion, DDG CT improved AC over average CT for baseline PET. The new DDG CT provides the benefits of 4D CT without the need for external device gating. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - M Allan Thomas
- Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Dershan Luo
- Department of Radiation Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Arabi H, Zaidi H. MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers. Magn Reson Med 2021; 87:686-701. [PMID: 34480771 PMCID: PMC9292636 DOI: 10.1002/mrm.29003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/14/2021] [Accepted: 08/21/2021] [Indexed: 12/22/2022]
Abstract
Purpose We compare the performance of three commonly used MRI‐guided attenuation correction approaches in torso PET/MRI, namely segmentation‐, atlas‐, and deep learning‐based algorithms. Methods Twenty‐five co‐registered torso 18F‐FDG PET/CT and PET/MR images were enrolled. PET attenuation maps were generated from in‐phase Dixon MRI using a three‐tissue class segmentation‐based approach (soft‐tissue, lung, and background air), voxel‐wise weighting atlas‐based approach, and a residual convolutional neural network. The bias in standardized uptake value (SUV) was calculated for each approach considering CT‐based attenuation corrected PET images as reference. In addition to the overall performance assessment of these approaches, the primary focus of this work was on recognizing the origins of potential outliers, notably body truncation, metal‐artifacts, abnormal anatomy, and small malignant lesions in the lungs. Results The deep learning approach outperformed both atlas‐ and segmentation‐based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation‐based method with up to 20% SUV bias in bony structures and the atlas‐based method with 9% bias in the lung. The deep learning‐based method exhibited superior performance. Yet, in case of sever truncation and metallic‐artifacts in the input MRI, this approach was outperformed by the atlas‐based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods. Conclusion The deep learning‐based method provides promising outcome for synthetic CT generation from MRI. However, metal‐artifact and body truncation should be specifically addressed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - 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, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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11
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Schaart DR, Schramm G, Nuyts J, Surti S. Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:598-618. [PMID: 34553105 PMCID: PMC8454900 DOI: 10.1109/trpms.2021.3084539] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.
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Affiliation(s)
- Dennis R Schaart
- Section Medical Physics & Technology, Radiation Science and Technology Department, Delft University of Technology, 2629 JB Delft, The Netherlands
| | - Georg Schramm
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, 3000 Leuven, Belgium
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
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12
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Abstract
PET/CT has become a preferred imaging modality over PET-only scanners in clinical practice. However, along with the significant improvement in diagnostic accuracy and patient throughput, pitfalls on PET/CT are reported as well. This review provides a general overview on the potential influence of the limitations with respect to PET/CT instrumentation and artifacts associated with the modality integration on the image appearance and quantitative accuracy of PET. Approaches proposed in literature to address the limitations or minimize the artifacts are discussed as well as their current challenges for clinical applications. Although the CT component can play an important role in assisting clinical diagnosis, we concentrate on the imaging scenarios where CT is used to provide auxiliary information for attenuation compensation and scatter correction in PET.
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Affiliation(s)
- Yu-Jung Tsai
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT; Department of Biomedical Engineering, Yale University, New Haven, CT.
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13
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Chen Y, Ying C, Binkley MM, Juttukonda MR, Flores S, Laforest R, Benzinger TL, An H. Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging. Magn Reson Med 2021; 86:499-513. [PMID: 33559218 PMCID: PMC8091494 DOI: 10.1002/mrm.28689] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/23/2020] [Accepted: 12/29/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging. METHODS DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE R 2 ∗ , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC. RESULTS DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC. CONCLUSION DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
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Affiliation(s)
- Yasheng Chen
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Chunwei Ying
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Michael M. Binkley
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Meher R. Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Hongyu An
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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14
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Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff D, Jones T, James M, Sutcliffe J, Ouyang J, Petibon Y, Ma C, El Fakhri G, Surti S, Karp JS, Badawi RD, Yamaya T, Akamatsu G, Schramm G, Rezaei A, Nuyts J, Fulton R, Kyme A, Lois C, Sari H, Price J, Boellaard R, Jeraj R, Bailey DL, Eslick E, Willowson KP, Dutta J. Quantitative PET in the 2020s: a roadmap. Phys Med Biol 2021; 66:06RM01. [PMID: 33339012 PMCID: PMC9358699 DOI: 10.1088/1361-6560/abd4f7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.
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Affiliation(s)
- Steven R Meikle
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Brain and Mind Centre, The University of Sydney, Australia
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Canada
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, United States of America
| | - Simon R Cherry
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Radiology, University of California, Davis, United States of America
| | - Richard Banati
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Brain and Mind Centre, The University of Sydney, Australia
- Australian Nuclear Science and Technology Organisation, Sydney, Australia
| | - David Mankoff
- Department of Radiology, University of Pennsylvania, United States of America
| | - Terry Jones
- Department of Radiology, University of California, Davis, United States of America
| | - Michelle James
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), CA, United States of America
- Department of Neurology and Neurological Sciences, Stanford University, CA, United States of America
| | - Julie Sutcliffe
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Internal Medicine, University of California, Davis, CA, United States of America
| | - Jinsong Ouyang
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Yoann Petibon
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, United States of America
| | - Joel S Karp
- Department of Radiology, University of Pennsylvania, United States of America
| | - Ramsey D Badawi
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Radiology, University of California, Davis, United States of America
| | - Taiga Yamaya
- National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Go Akamatsu
- National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Georg Schramm
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Ahmadreza Rezaei
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Roger Fulton
- Brain and Mind Centre, The University of Sydney, Australia
- Department of Medical Physics, Westmead Hospital, Sydney, Australia
| | - André Kyme
- Brain and Mind Centre, The University of Sydney, Australia
- School of Biomedical Engineering, Faculty of Engineering and IT, The University of Sydney, Australia
| | - Cristina Lois
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Hasan Sari
- Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
- Athinoula A. Martinos Center, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
| | - Julie Price
- Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
- Athinoula A. Martinos Center, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
| | - Ronald Boellaard
- Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, location VUMC, Netherlands
| | - Robert Jeraj
- Departments of Medical Physics, Human Oncology and Radiology, University of Wisconsin, United States of America
- Faculty of Mathematics and Physics, University of Ljubljana, Slovenia
| | - Dale L Bailey
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
- Faculty of Science, The University of Sydney, Australia
| | - Enid Eslick
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
| | - Kathy P Willowson
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
- Faculty of Science, The University of Sydney, Australia
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, United States of America
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15
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Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 2021; 83:122-137. [DOI: 10.1016/j.ejmp.2021.03.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/18/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
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Abstract
Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.
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Affiliation(s)
- Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, Leuven, Belgium; University of Stellenbosch, Department of Nuclear Medicine, Cape Town, South Africa.
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17
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Wang G. PET-enabled dual-energy CT: image reconstruction and a proof-of-concept computer simulation study. Phys Med Biol 2020; 65:245028. [PMID: 33120376 DOI: 10.1088/1361-6560/abc5ca] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Standard dual-energy computed tomography (CT) uses two different x-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrades or increasing radiation exposure. This paper proposes a different dual-energy CT imaging method that is enabled by PET. Instead of using a second x-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray attenuation image is then combined with the low-energy x-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which would not compromise the PET activity reconstruction too much but may significantly affect the performance of the gamma-ray attenuation image for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available x-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results demonstrate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.
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Affiliation(s)
- Guobao Wang
- Department of Radiology, University of California, Davis, CA, United States of America
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18
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Shiri I, Arabi H, Geramifar P, Hajianfar G, Ghafarian P, Rahmim A, Ay MR, Zaidi H. Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network. Eur J Nucl Med Mol Imaging 2020; 47:2533-2548. [DOI: 10.1007/s00259-020-04852-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 05/01/2020] [Indexed: 12/22/2022]
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19
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Oehmigen M, Lindemann ME, Tellmann L, Lanz T, Quick HH. Improving the CT (140 kVp) to PET (511 keV) conversion in PET/MR hardware component attenuation correction. Med Phys 2020; 47:2116-2127. [DOI: 10.1002/mp.14091] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 02/10/2020] [Accepted: 02/10/2020] [Indexed: 01/21/2023] Open
Affiliation(s)
- Mark Oehmigen
- High‐Field and Hybrid MR Imaging University Hospital Essen Essen Germany
| | - Maike E. Lindemann
- High‐Field and Hybrid MR Imaging University Hospital Essen Essen Germany
| | - Lutz Tellmann
- Institute for Neuroscience and Medicine (INM‐4) Forschungszentrum Jülich GmbH Jülich Germany
| | | | - Harald H. Quick
- High‐Field and Hybrid MR Imaging University Hospital Essen Essen Germany
- Erwin L. Hahn Institute for MR Imaging University Duisburg‐Essen Essen Germany
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20
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García-Pérez P, España S. Simultaneous emission and attenuation reconstruction in time-of-flight PET using a reference object. EJNMMI Phys 2020; 7:3. [PMID: 31932984 PMCID: PMC6957598 DOI: 10.1186/s40658-020-0272-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 01/07/2020] [Indexed: 12/20/2022] Open
Abstract
Background Simultaneous reconstruction of emission and attenuation images in time-of-flight (TOF) positron emission tomography (PET) does not provide a unique solution. In this study, we propose to solve this limitation by including additional information given by a reference object with known attenuation placed outside the patient. Different configurations of the reference object were studied including geometry, material composition, and activity, and an optimal configuration was defined. In addition, this configuration was tested for different timing resolutions and noise levels. Results The proposed strategy was tested in 2D simulations obtained by forward projection of available PET/CT data and noise was included using Monte Carlo techniques. Obtained results suggest that the optimal configuration corresponds to a water cylinder inserted in the patient table and filled with activity. In that case, mean differences between reconstructed and true images were below 10%. However, better results can be obtained by increasing the activity of the reference object. Conclusion This study shows promising results that might allow to obtain an accurate attenuation map from pure TOF-PET data without prior knowledge obtained from CT, MRI, or transmission scans.
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Affiliation(s)
- Pablo García-Pérez
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, IdISSC, Ciudad Universitaria, 28040, Madrid, Spain
| | - Samuel España
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, IdISSC, Ciudad Universitaria, 28040, Madrid, Spain. .,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
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21
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Lillington J, Brusaferri L, Kläser K, Shmueli K, Neji R, Hutton BF, Fraioli F, Arridge S, Cardoso MJ, Ourselin S, Thielemans K, Atkinson D. PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques. Med Phys 2020; 47:790-811. [PMID: 31794071 PMCID: PMC7027532 DOI: 10.1002/mp.13943] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 07/23/2019] [Accepted: 11/20/2019] [Indexed: 12/16/2022] Open
Abstract
Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single‐valued population‐based lung LAC, and better estimation is needed to improve quantification. Given the under‐appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single‐valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission‐based schemes. Potential strategies for future developments are also presented.
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Affiliation(s)
- Joseph Lillington
- Centre for Medical Imaging, University College London, London, W1W 7TS, UK
| | - Ludovica Brusaferri
- Institute of Nuclear Medicine, University College London, London, NW1 2BU, UK
| | - Kerstin Kläser
- Centre for Medical Image Computing, University College London, London, WC1E 7JE, UK
| | - Karin Shmueli
- Magnetic Resonance Imaging Group, Department of Medical Physics & Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, GU16 8QD, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, NW1 2BU, UK
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London, London, NW1 2BU, UK
| | - Simon Arridge
- Centre for Medical Image Computing, University College London, London, WC1E 7JE, UK
| | - Manuel Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, NW1 2BU, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, W1W 7TS, UK
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22
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Berker Y, Schulz V, Karp JS. Algorithms for joint activity-attenuation estimation from positron emission tomography scatter. EJNMMI Phys 2019; 6:18. [PMID: 31659488 PMCID: PMC6816692 DOI: 10.1186/s40658-019-0254-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/10/2019] [Indexed: 12/31/2022] Open
Abstract
Background Attenuation correction in positron emission tomography remains challenging in the absence of measured transmission data. Scattered emission data may contribute missing information, but quantitative scatter-to-attenuation (S2A) reconstruction needs to input the reconstructed activity image. Here, we study S2A reconstruction as a building block for joint estimation of activity and attenuation. Methods We study two S2A reconstruction algorithms, maximum-likelihood expectation maximization (MLEM) with one-step-late attenuation (MLEM-OSL) and a maximum-likelihood gradient ascent (MLGA). We study theoretical properties of these algorithms with a focus on convergence and convergence speed and compare convergence speeds and the impact of object size in simulations using different spatial scale factors. Then, we propose joint estimation of activity and attenuation from scattered and nonscattered (true) emission data, combining MLEM-OSL or MLGA with scatter-MLEM as well as trues-MLEM and the maximum-likelihood transmission (MLTR) algorithm. Results Shortcomings of MLEM-OSL inhibit convergence to the true solution with high attenuation; these shortcomings are related to the linearization of a nonlinear measurement equation and can be linked to a new numerical criterion allowing geometrical interpretations in terms of low and high attenuation. Comparisons using simulated data confirm that while MLGA converges largely independent of the attenuation scale, MLEM-OSL converges if low-attenuation data dominate, but not with high attenuation. Convergence of MLEM-OSL can be improved by isolating data satisfying the aforementioned low-attenuation criterion. In joint estimation of activity and attenuation, scattered data helps avoid local minima that nonscattered data alone cannot. Combining MLEM-OSL with trues-MLEM may be sufficient for low-attenuation objects, while MLGA, scatter-MLEM, and MLTR may additionally be needed with higher attenuation. Conclusions The performance of S2A algorithms depends on spatial scales. MLGA provides lower computational complexity and convergence in more diverse setups than MLEM-OSL. Finally, scattered data may provide additional information to joint estimation of activity and attenuation through S2A reconstruction.
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Affiliation(s)
- Yannick Berker
- Division of X-ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany. .,Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Forckenbeckstraße 55, Aachen, 52074, Germany. .,Department of Radiology, University of Pennsylvania, 3620 Hamilton Walk, Philadelphia, 19104, PA, USA.
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Forckenbeckstraße 55, Aachen, 52074, Germany.,III. Physikalisches Institut B, RWTH Aachen University, Otto-Blumenthal-Straße, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Forckenbeckstraße 55, Aachen, 52074, Germany
| | - Joel S Karp
- Department of Radiology, University of Pennsylvania, 3620 Hamilton Walk, Philadelphia, 19104, PA, USA
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Arabi H, Zeng G, Zheng G, Zaidi H. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. Eur J Nucl Med Mol Imaging 2019; 46:2746-2759. [PMID: 31264170 DOI: 10.1007/s00259-019-04380-x] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 05/28/2019] [Indexed: 10/26/2022]
Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Guodong Zeng
- Institute for Surgical Technology and Biomechanics, University of Bern, CH-3014, Bern, Switzerland
| | - Guoyan Zheng
- Institute for Surgical Technology and Biomechanics, University of Bern, CH-3014, Bern, Switzerland
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
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Rezaei A, Schramm G, Willekens SMA, Delso G, Van Laere K, Nuyts J. A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging. J Nucl Med 2019; 60:1649-1655. [PMID: 30979823 DOI: 10.2967/jnumed.118.220871] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/04/2019] [Indexed: 11/16/2022] Open
Abstract
Time-of-flight (TOF) PET data provide an effective means for attenuation correction (AC) when no (or incomplete or inaccurate) attenuation information is available. Since MR scanners provide little information on photon attenuation of different tissue types, AC in hybrid PET/MR scanners has always been challenging. In this contribution, we aim at validating the activity reconstructions of the maximum-likelihood ordered-subsets activity and attenuation (OSAA) reconstruction algorithm on a patient brain data set. We present a quantitative comparison of joint reconstructions with the current clinical gold standard-ordered-subsets expectation maximization-using CT-based AC in PET/CT, as well as the current state of the art in PET/MR, that is, zero time echo (ZTE)-based AC. Methods: The TOF PET emission data were initially used in a preprocessing stage to estimate crystal maps of efficiencies, timing offsets, and timing resolutions. Applying these additional corrections during reconstructions, OSAA, ZTE-based, and the vendor-provided atlas-based AC techniques were analyzed and compared with CT-based AC. In our initial study, we used the CT-based estimate of the expected scatter and later used the ZTE-based and OSAA attenuation estimates to compute the expected scatter contribution of the data during reconstructions. In all reconstructions, a maximum-likelihood scaling of the single-scatter simulation estimate to the emission data was used for scatter correction. The reconstruction results were analyzed in the 86 segmented regions of interest of the Hammers atlas. Results: Our quantitative analysis showed that, in practice, a tracer activity difference of +0.5% (±2.1%) and +0.1% (±2.3%) could be expected for the state-of-the-art ZTE-based and OSAA AC methods, respectively, in PET/MR compared with the clinical gold standard in PET/CT. Conclusion: Joint activity and attenuation estimation methods can provide an effective solution to the challenging AC problem for brain studies in hybrid TOF PET/MR scanners. With an accurate TOF-based (timing offsets and timing resolutions) calibration, and similar to the results of the state-of-the-art method in PET/MR, regional errors of joint TOF PET reconstructions are within a few percentage points.
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Affiliation(s)
- Ahmadreza Rezaei
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Georg Schramm
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Stefanie M A Willekens
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Gaspar Delso
- MR Applications and Workflow, GE Healthcare, Waukesha, Wisconsin
| | - Koen Van Laere
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Johan Nuyts
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
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Zhuang M, Karakatsanis NA, Dierckx RAJO, Zaidi H. Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI. Mol Imaging Biol 2019; 21:1147-1156. [PMID: 30838550 DOI: 10.1007/s11307-019-01338-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
PURPOSE The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (Ki) imaging in PET/MRI studies. PROCEDURES WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-mapCTAC) of continuous attenuation coefficient values (Acoeff). The μ-mapCTAC were then segmented into four tissue classes (μ-map4-classes), namely background (air), lung, fat, and soft tissue, where a predefined Acoeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-mapCTAC were then assigned a predefined soft tissue Acoeff (0.1 cm-1) to produce an AC μ-map without bones (μ-mapno-bones). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-mapCTAC, μ-map4-classes, and μ-mapno-bones (PETCTAC, PET4-classes, and PETno-bones), respectively. WB indirect and direct parametric Ki images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROIpeak), maximum region-of-interest (ROImax), mean region-of-interest (ROImean), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET4-classes and PETno-bones against PETCTAC for all images. The same test was also adopted to compare the differences between SUV, indirect Ki, and direct Ki images for each evaluated AC method. RESULTS No significant differences in MATV, TBR, and CNR were observed between PET4-classes and PETCTAC for either SUV or Ki images. PET4-classes significantly overestimated ROIpeak, ROImax, ROImean, as well as MVP scores compared with PETCTAC in both SUV and Ki images. SUV images exhibited the highest median relative errors for PET4-classes with respect to PETCTAC (RE4-classes): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROIpeak, ROImax, ROImean, and MVP, respectively. On the contrary, Ki images showed slightly reduced RE4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROIpeak, ROImax, ROImean, and MVP, respectively. A higher TBR was observed on indirect and direct Ki images relative to SUV, while direct Ki images demonstrated the highest CNR. CONCLUSIONS Four-tissue class AC may impact SUV and Ki parameter estimation but only to a limited extent, thereby suggesting that WB Patlak Ki imaging for dynamic WB PET/MRI studies is feasible. Patlak Ki imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific Acoeff for each tissue class should be used when possible to address the high inter-patient variability of Acoeff distributions.
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Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands.,Department of Radiation Oncology, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, China
| | - Nicolas A Karakatsanis
- Division of Radiopharmaceutical Sciences, Department of Radiology, Weill Cornell Medical College, Cornell University, New York, NY, 10021, USA
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands
| | - Habib Zaidi
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center, 9700 RB, Groningen, The Netherlands. .,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland. .,Geneva University Neurocenter, University of Geneva, 1205, Geneva, Switzerland. .,Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
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Ropella-Panagis KM, Seiberlich N, Gulani V. Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:388-399. [PMID: 32864537 DOI: 10.1109/trpms.2019.2897425] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Magnetic Resonance Imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for quantitative and functional evaluation, MRI is typically used qualitatively, in which the underlying tissue properties are not measured, and thus the brightness of each pixel is not quantitatively meaningful. Positron Emission Tomography (PET) is an inherently quantitative imaging modality that interrogates functional activity within a tissue, probed by a molecule of interest coupled with an appropriate tracer. These modalities can complement one another to provide clinical information regarding both structure and function, but there are still technical and practical hurdles in the way of the integrated use of both modalities. Recent advances in MRI have moved the field in an increasingly quantitative direction, which is complementary to PET, and could also potentially help solve some of the challenges in PET/MR. Magnetic Resonance Fingerprinting (MRF) is a recently described MRI-based technique which can efficiently and simultaneously quantitatively map several tissue properties in a single exam. Here, the basic principles behind the quantitative approach of MRF are laid out, and the potential implications for combined PET/MR are discussed.
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Affiliation(s)
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
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