Usefulness of abdominal belt for restricting respiratory cardiac motion and improving image quality in myocardial perfusion PET

Ichikawa, Yasutaka; Tomita, Yoya; Ishida, Masaki; Kobayashi, Shigeki; Takeda, Kan; Sakuma, Hajime

doi:10.1007/s12350-016-0623-z

Cited by 3 publications

(2 citation statements)

References 19 publications

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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. Electrocardiography (ECG) is still the gold-standard when it comes to track the cardiac motion due to 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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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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“…Immobilization devices have also been proposed to control or suppress respiratory and/or body motion during PET acquisitions. 88 In the context of cardiac perfusion studies, Ichikawa and colleagues 130 investigated the use of an abdominal belt worn by patients to suppress the extent of respiratory motion. They found that the belt system, which the study participants reported as being well tolerated, significantly reduced the motion of the heart due to respiration compared to free-breathing: 8 mm of motion compared to 12 mm, respectively.…”

Section: Breath-hold Techniques and Patient Immobilizationmentioning

confidence: 99%

Data-Driven Patient Motion Compensation in Cardiac Positron Emission Tomography

Manwell¹

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Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2018. Part 1 of 2: Positron emission tomography, computed tomography, and magnetic resonance

AlJaroudi

Hage

2019

Journal of Nuclear Cardiology

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Usefulness of abdominal belt for restricting respiratory cardiac motion and improving image quality in myocardial perfusion PET

Abstract: The belt technique restricts LV respiratory motion and improves the image quality of myocardial PET/CT, and it is well tolerated by patients.

Cited by 3 publications

References 19 publications

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

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

Data-Driven Patient Motion Compensation in Cardiac Positron Emission Tomography

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2018. Part 1 of 2: Positron emission tomography, computed tomography, and magnetic resonance

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