Task-based evaluation of a 4D MAP-RBI-EM image reconstruction method for gated myocardial perfusion SPECT using a human observer study

Lee, Taek Soo; Higuchi, Takahiro; Lautamäki, Riikka; Bengel, Frank M.; Tsui, B.M.W.

doi:10.1088/0031-9155/60/17/6789

Cited by 6 publications

(12 citation statements)

References 51 publications

(62 reference statements)

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“…where x d represents the counts in denoised images, x nf is the counts in the corresponding noise-free DG We computed the normalized standard deviation (NSD) 33 of a 2D uniform ROI (20 × 20) drawn on the polar plots to assess the noise reduction (Figure 4a).…”

Section: Discussionmentioning

confidence: 99%

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Sun

Zhang

et al. 2022

Medical Physics

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Purpose Dual respiratory–cardiac gating reduces respiratory and cardiac motion blur in myocardial perfusion single‐photon emission computed tomography (MP‐SPECT). However, image noise is increased as detected counts are reduced in each dual gate (DG). We aim to develop a denoising method for dual gating MP‐SPECT images using a 3D conditional generative adversarial network (cGAN). Methods Twenty extended cardiac‐torso phantoms with various 99mTc‐sestamibi distributions, defect characteristics, and body and organ sizes were used in the simulation, modeling six respiratory and eight cardiac gates (CGs), that is, 48 DGs for ordered subset expectation maximization reconstruction. Twenty clinical 99mTc‐sestamibi SPECT/CT datasets were re‐binned into 7 respiratory gates and 8 CGs, that is, 56 DGs for maximum likelihood expectation maximization reconstruction. We evaluated the use of (i) phantoms’ own datasets (patient‐specific denoising [PD]) or other phantoms’ datasets (cross‐patient denoising) for training; (ii) the CG or the static (non‐gated [NG]) data as the training references for cGAN; and (iii) cGAN as compared to conventional 3D post‐reconstruction filtering, cardiac gating methods, and convolutional neural network. Normalized mean squared error, noise as assessed by normalized standard deviation, spatial blurring measured as the full‐width‐at‐half‐maximum of left ventricular wall, ejection fraction, joint correlation histogram, and defect size were analyzed as metrics of image quality. Results Training using patients’ own dataset is superior to conventional training based on other patients’ data. Using CG image as training reference provides a better trade‐off in terms of noise and image blur as compared to the use of NG. cGAN‐CG‐PD provides superior performance as compared to other denoising methods for all physical and diagnostic indices evaluated in both simulation and clinical studies. Conclusions cGAN denoising is promising for dual gating MP‐SPECT based on the metrics mentioned earlier.

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Section: Discussionmentioning

confidence: 99%

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Sun

Zhang

et al. 2022

Medical Physics

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“…Correcting for the physical effects of attenuation, detector response, and scatter can be seen to improve the reconstructed images. Further correction of motion using the 4D RBI-MAP method provides the best results as validated using image quality indices (normalized mean squared error and averaged normalized standard deviation) and human observer studies 71 .…”

Section: Evaluation Of Medical Imaging Techniquesmentioning

confidence: 95%

“…In a series of studies by Lee et al 68–71 , the XCAT phantom and its model for the cardiac motion was used to evaluate different reconstruction methods in myocardial (MP) SPECT. The XCAT was used to simulate a male adult (50 th percentile in height and weight) with a normal cardiac motion as well abnormal motions including perfusion and wall motion abnormalities.…”

Section: Evaluation Of Medical Imaging Techniquesmentioning

confidence: 99%

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

Segars

Tsui

Cai

et al. 2018

IEEE Trans. Med. Imaging

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The four dimensional (4D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT’s design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

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“…[317][318][319][320] Recently, VCT studies have played an important role in the understanding of the blurring effects of RM on static 3-D image quality and in the evaluation and development of 4-D image reconstruction methods that reduce RM blurring and improve image quality in both SPECT and PET. 321,322 Also they have been used in the development of a new generation of 4-D image reconstruction methods that include additional compensation of cardiac motion for significant improvement in 4-D cardiac-gated MP SPECT and PET images in terms of reduced RM blur and lower noise levels in the 4-D cardiac-gate MP SPECT and PET images. 323 Similar VCT studies have also been done to evaluate motion-compensated reconstruction algorithms in the context of head motions and PET brain imaging.…”

Section: Nuclear Imagingmentioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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Task-based evaluation of a 4D MAP-RBI-EM image reconstruction method for gated myocardial perfusion SPECT using a human observer study

Cited by 6 publications

References 51 publications

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

Virtual clinical trials in medical imaging: a review

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