4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT

Qi, Wenyuan; Yang, Yongyi; Song, Chao; Wernick, Miles N.; Pretorius, P. Hendrik; King, Michael A.

doi:10.1109/tmi.2017.2690819

Cited by 21 publications

(14 citation statements)

References 41 publications

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“…Another method is to reconstruct each respiratory window separately, co-register these images and compute their average as a motion-compensated image [ 16 – 20 ]. One other method involves estimating the inter-window motion from individually reconstructed windows and then modifying the observation matrix to finally reconstruct a motion-compensated image [ 4 , 16 , 21 – 23 ]. Alternatively, one can simultaneously reconstruct the image and estimate the cardiac displacement vectors by combining motion estimation and reconstruction into one optimization problem [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of respiratory motion on cardiac defect contrast in myocardial perfusion SPECT: a physical phantom study

et al. 2019

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Objective Correction for respiratory motion in myocardial perfusion imaging requires sorting of emission data into respiratory windows where the intra-window motion is assumed to be negligible. However, it is unclear how much intra-window motion is acceptable. The aim of this study was to determine an optimal value of intra-window residual motion. Methods A custom-designed cardiac phantom was created and imaged with a standard dual-detector SPECT/CT system using Tc-99m as the radionuclide. Projection images were generated from the list-mode data simulating respiratory motion blur of several magnitudes from 0 (stationary phantom) to 20 mm. Cardiac defect contrasts in six anatomically different locations, as well as myocardial perfusion of apex, anterior, inferior, septal and lateral walls, were measured at each motion magnitude. Stationary phantom data were compared to motion-blurred data. Two physicians viewed the images and evaluated differences in cardiac defect visibility and myocardial perfusion. Results Significant associations were observed between myocardial perfusion in the anterior and inferior walls and respiratory motion. Defect contrasts were found to decline as a function of motion, but the magnitude of the decline depended on the location and shape of the defect. Defects located near the cardiac apex lost contrast more rapidly than those located on the anterior, inferior, septal and lateral wall. The contrast decreased by less than 5% at every location when the motion magnitude was 2 mm or less. According to a visual evaluation, there were differences in myocardial perfusion if the magnitude of the motion was greater than 1 mm, and there were differences in the visibility of the cardiac defect if the magnitude of the motion was greater than 9 mm. Conclusions Intra-window respiratory motion should be limited to 2 mm to effectively correct for respiratory motion blur in myocardial perfusion SPECT.

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Section: Introductionmentioning

confidence: 99%

Effect of respiratory motion on cardiac defect contrast in myocardial perfusion SPECT: a physical phantom study

et al. 2019

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“…To quantify the effect on the spatial resolution of the output images by the denoising model, we computed the full‐width at half‐maximum (FWHM) of the cross‐sectional image intensity profile for a midsection of the LV wall, 42 as illustrated in Fig. 3.…”

Section: Methodsmentioning

confidence: 99%

Deep learning with noise‐to‐noise training for denoising in SPECT myocardial perfusion imaging

et al. 2020

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Purpose Post‐reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single‐photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoising in conventional SPECT‐MPI acquisitions, and investigate whether it can be more effective for improving the detectability of perfusion defects compared to traditional postfiltering. Methods Owing to the lack of ground truth in clinical studies, we adopt a noise‐to‐noise (N2N) training approach for denoising in SPECT‐MPI images. We consider a coupled U‐Net (CU‐Net) structure which is designed to improve learning efficiency through feature map reuse. For network training we employ a bootstrap procedure to generate multiple noise realizations from list‐mode clinical acquisitions. In the experiments we demonstrated the proposed approach on a set of 895 clinical studies, where the iterative OSEM algorithm with three‐dimensional (3D) Gaussian postfiltering was used to reconstruct the images. We investigated the detection performance of perfusion defects in the reconstructed images using the non‐prewhitening matched filter (NPWMF), evaluated the uniformity of left ventricular (LV) wall in terms of image intensity, and quantified the effect of smoothing on the spatial resolution of the reconstructed LV wall by using its full‐width at half‐maximum (FWHM). Results Compared to OSEM with Gaussian postfiltering, the DL denoised images with CU‐Net significantly improved the detection performance of perfusion defects at all contrast levels (65%, 50%, 35%, and 20%). The signal‐to‐noise ratio (SNRD) in the NPWMF output was increased on average by 8% over optimal Gaussian smoothing (P < 10−4, paired t‐test), while the inter‐subject variability was greatly reduced. The CU‐Net also outperformed a 3D nonlocal means (NLM) filter and a convolutional autoencoder (CAE) denoising network in terms of SNRD. In addition, the FWHM of the LV wall in the reconstructed images was varied by less than 1%. Furthermore, CU‐Net also improved the detection performance when the images were processed with less post‐reconstruction smoothing (a trade‐off of increased noise for better LV resolution), with SNRD improved on average by 23%. Conclusions The proposed DL with N2N training approach can yield additional noise suppression in SPECT‐MPI images over conventional postfiltering. For perfusion defect detection, DL with CU‐Net could outperform conventional 3D Gaussian filtering with optimal setting as well as NLM and CAE.

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“…The motion-pattern can be estimated directly from the data, or can be obtained using an external motion-tracking device or MRI data. Motion correction can be incorporated in the reconstruction process [94][95][96].…”

Section: Accounting For Temporal Changesmentioning

confidence: 99%

Advances in clinical molecular imaging instrumentation

Hutton

Erlandsson

Thielemans

2018

Clin Transl Imaging

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In this article, we describe recent developments in the design of both single-photon emission computed tomography (SPECT) and positron emission tomography (PET) instrumentation that have led to the current range of superior performance instruments. The adoption of solid-state technology for either complete detectors [e.g., cadmium zinc telluride (CZT)] or read-out systems that replace photomultiplier tubes [avalanche photodiodes (APD) or silicon photomultipliers (SiPM)] provide the advantage of compact technology, enabling flexible system design. In SPECT, CZT is well suited to multi-radionuclide and kinetic studies. For PET, SiPM technology provides MR compatibility and superior time-of-flight resolution, resulting in improved signal-to-noise ratio. Similar SiPM technology has also been used in the construction of the first SPECT insert for clinical brain SPECT/MRI.

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4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT

Cited by 21 publications

References 41 publications

Effect of respiratory motion on cardiac defect contrast in myocardial perfusion SPECT: a physical phantom study

Effect of respiratory motion on cardiac defect contrast in myocardial perfusion SPECT: a physical phantom study

Deep learning with noise‐to‐noise training for denoising in SPECT myocardial perfusion imaging

Advances in clinical molecular imaging instrumentation

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