MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac <sup>18</sup>F-FDG imaging

Petibon, Yoann; Sun, Tao; Han, Paul Kyu; Ma, Chao; Fakhri, Georges El; Ouyang, Jinsong

doi:10.1088/1361-6560/ab39c2

Cited by 21 publications

(14 citation statements)

References 67 publications

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“…The study did not consider motion correction, which could affect the accuracy of kinetic quantification [45][46][47][48]. Nevertheless, the effect of motion is not expected to result in a significant change to the results of this study since the spatial resolution of the PET scanner is only about 6-8 mm and the quantification was performed on large, global ROIs.…”

Section: Discussionmentioning

confidence: 90%

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Zuo

Badawi

Foster

et al. 2020

IEEE Trans. Radiat. Plasma Med. Sci.

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Cardiac 18 F-FDG PET has been used in clinics to assess myocardial glucose metabolism. Its ability for imaging myocardial glucose transport, however, has rarely been exploited in clinics. Using the dynamic FDG-PET scans of ten patients with coronary artery disease, we investigate in this paper appropriate dynamic scan and kinetic modeling protocols for efficient quantification of myocardial glucose transport. Three kinetic models and the effect of scan duration were evaluated by using statistical fit quality, assessing the impact on kinetic quantification, and analyzing the practical identifiability. The results show that the kinetic model selection depends on the scan duration. The reversible two-tissue model was needed for a one-hour dynamic scan. The irreversible two-tissue model was optimal for a scan duration of around 10-15 minutes. If the scan duration was shortened to 2-3 minutes, a one-tissue model was the most appropriate. For global quantification of myocardial glucose transport, we demonstrated that an early dynamic scan with a duration of 10-15 minutes and irreversible kinetic modeling was comparable to the full one-hour scan with reversible kinetic modeling. Myocardial glucose transport quantification provides an additional physiological parameter on top of the existing assessment of glucose metabolism and has the potential to enable single tracer multiparametric imaging in the myocardium.

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Section: Discussionmentioning

confidence: 90%

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Zuo

Badawi

Foster

et al. 2020

IEEE Trans. Radiat. Plasma Med. Sci.

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“…We performed image transformation for motion correction of the PET data. Affine transformation was operated by matrix generated from motion data [24]. The acquired PET image by measured optimal gate number was separated by each bin from the respiratory phase.…”

Section: Motion Compensationmentioning

confidence: 99%

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

et al. 2021

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Motion estimation and compensation are necessary for improvement of tumor quantification analysis in positron emission tomography (PET) images. The aim of this study was to propose adaptive PET imaging with internal motion estimation and correction using regional artificial evaluation of tumors injected with low-dose and high-dose radiopharmaceuticals. In order to assess internal motion, molecular sieves imitating tumors were loaded with 18F and inserted into the lung and liver regions in rats. All models were classified into two groups, based on the injected radiopharmaceutical activity, to compare the effect of tumor intensity. The PET study was performed with injection of F-18 fluorodeoxyglucose (18F-FDG). Respiratory gating was carried out by external trigger device. Count, signal to noise ratio (SNR), contrast and full width at half maximum (FWHM) were measured in artificial tumors in gated images. Motion correction was executed by affine transformation with estimated internal motion data. Monitoring data were different from estimated motion. Contrast in the low-activity group was 3.57, 4.08 and 6.19, while in the high-activity group it was 10.01, 8.36 and 6.97 for static, 4 bin and 8 bin images, respectively. The results of the lung target in 4 bin and the liver target in 8 bin showed improvement in FWHM and contrast with sufficient SNR. After motion correction, FWHM was improved in both regions (lung: 24.56%, liver: 10.77%). Moreover, with the low dose of radiopharmaceuticals the PET image visualized specific accumulated radiopharmaceutical areas in the liver. Therefore, low activity in PET images should undergo motion correction before quantification analysis using PET data. We could improve quantitative tumor evaluation by considering organ region and tumor intensity.

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“…Respiratory and cardiac motion correction methods in PET have been investigated in the past [ 6 ], [ 7 ]. External motion tracking markers or sensors have been used in some studies [ 5 ], [ 8 ] to track respiratory motion at a high temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning

Shi

Dvornek

et al. 2021

IEEE Trans. Med. Imaging

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Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.

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MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac ¹⁸F-FDG imaging

Cited by 21 publications

References 67 publications

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning

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MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging

Cited by 21 publications

References 67 publications

Multiparametric Cardiac 18F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Multiparametric Cardiac 18F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning

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Product

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MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac ¹⁸F-FDG imaging

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis

Multiparametric Cardiac ¹⁸F-FDG PET in Humans: Kinetic Model Selection and Identifiability Analysis