D-SPECT: New technology, old tricks

Kao, Yung Hsiang; Better, Nathan

doi:10.1007/s12350-015-0290-5

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…However, artifacts of dedicated cardiac SPECT cameras are commonly observed in daily clinical practice as for conventional SPECT cameras (4). Soft tissue attenuation is the most common cause for the artifacts usually due to attenuation caused by the diaphragm, breast tissue or obesity (5).…”

Section: Introductionmentioning

confidence: 99%

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

et al. 2021

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Purpose: Dedicated cardiac SPECT scanners with cadmium-zinc-telluride (CZT) cameras have shown capabilities of shortened scan times or reduced radiation doses as well as improved image quality. Since most of the dedicated scanners do not have an integrated CT, image quantification with attenuation correction (AC) is challenging and artifacts are routinely encountered in daily clinical practice. In this work, we demonstrate a direct AC technique using deep learning (DL) for myocardial perfusion imaging (MPI). Methods:In an IRB-approved retrospective study, 100 cardiac SPECT/CT datasets with 99m Tctetrofosmin using a GE Discovery NM/CT 570c scanner were collected at the Yale New Haven Hospital. A U-Net-based network was used for generating attenuation-corrected SPECT (SPECTDL) directly from non-corrected SPECT (SPECTNC) without undergoing an additional image reconstruction step. The accuracy of SPECTDL was evaluated by voxel-wise and segment-wise analyses against the reference CT-based AC (SPECTCTAC) using American Heart Association 17 segments in the myocardium. Polar maps of representative (best/median/worst) cases were visually compared for illustrating potential benefits and pitfalls of the DL approach. Results:The voxel-wise correlations with SPECTCTAC were 92.2% ± 3.7 (slope = 0.87; R 2 = 0.81) and 97.7% ± 1.8 (slope = 0.94; R 2 = 0.91) for SPECTNC and SPECTDL, respectively. The segmental errors of SPECTNC scattered from -35% up to 21% (p < 0.001); while, the errors of SPECTDL stayed mostly within ±10% (p < 0.001). The average segmental errors (mean±SD) were -6.11 ± 8.06% and 0.49 ± 4.35% for SPECTNC and SPECTDL, respectively. The average absolute segmental errors were 7.96 ± 6.23% and 3.31 ± 2.87% for SPECTNC and SPECTDL, respectively. Review of polar maps revealed successful demonstration of reduced attenuation artifacts; however, the performance of SPECTDL was not consistent for all subjects likely due to different amount of attenuation and uptake patterns. Conclusion:We demonstrated the feasibility of direct AC using DL for SPECT MPI. Overall, our DL approach reduced attenuation artifacts substantially compared to SPECTNC, justifying further studies to establish safety and consistency for clinical applications in stand-alone SPECT systems suffered from attenuation artifacts.

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Section: Introductionmentioning

confidence: 99%

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

et al. 2021

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“…It is necessary to select a suitable detector for further improvement. E-SPECT replaced NaI (TI) crystals with CdZnTe semiconductors [17]. The CdZnTe can work normally at room temperature, and its volume is relatively small.…”

Section: ) Elliptical Orbit Cardiac Imaging Systemmentioning

confidence: 99%

Hybrid Cardiac Imaging Integrating Elliptical Orbit SPECT With CT

Sheng

Liu

et al. 2019

IEEE Access

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The cardiac single photon emission computed tomography (SPECT) is an important research topic because of the large clinical demand and the need for improving image quality. It usually suffers from low count sensitivity and poor spatial resolution due to patient radiation safety concerns. In this study, hybrid cardiac imaging integrating elliptical orbit SPECT (E-SPECT) with computed tomography (CT) is proposed to better obtain the anatomical and physiological information of the cardiac and vascular systems. The geometric structure of this proposed hybrid imaging system is similar to that of an ellipse, containing multiple detectors with slit-slat collimation. The hybrid imaging system utilizes the elliptical geometry and multi-detectors to improve the sensitivity and signal to noise ratio (SNR), and incorporates a CT scanner into the system to obtain an accurate attenuation mapping matrix of the patient for image reconstruction. The iterative reconstruction algorithm with some constraints is used to reconstruct images. The constraint-based body contour is introduced to deal with severely truncated data from E-SPECT. The feasibility and advantage of the system is validated by simulations. Simulation results show that it is optimal for system design and the quality of reconstructed images are greatly improved. With an optimal sampling coverage angle between 210 • and 240 • , the hybrid cardiac imaging system proposed in the paper can achieves significant improvements in image spatial resolution, SNR and image fidelity. INDEX TERMS Attenuation correction (AC), computed tomography (CT), elliptical orbit, ordered subsets expectation maximization (OSEM), single photon emission computed tomography (SPECT), slit-slat collimation.

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“…However, the acquisition time for the conventional NaI-based MP-SPECT is relatively long (15-20 min) (2), leading to potential motion artifacts, e.g., upward creep (3), patient's discomfort, lower patient throughput (4), and mismatch artifacts between the sequential MP-SPECT and CT (5,6). New scanner geometries with parallel-hole (7) or multi-pinhole collimations (8,9) for MP-SPECT are proposed for better photons detection efficiency and reduced scan time (2-8 min) (10,11). Advanced reconstruction algorithms (12) also facilitate the possibility of reducing acquisition time without degrading image quality.…”

Section: Introductionmentioning

confidence: 99%

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

Sun

Yang²,

Li³

et al. 2023

Front. Med.

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PurposeDeep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images.MethodsFifty patients who underwent 1184 MBq 99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed.ResultsAll quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet.ConclusionDenoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

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D-SPECT: New technology, old tricks

Cited by 9 publications

References 7 publications

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

Hybrid Cardiac Imaging Integrating Elliptical Orbit SPECT With CT

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

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