Cardiac SPECT/CT Imaging: CT Attenuation Correction and SPECT/CT Hybrid Imaging

Fukushima, Yoshimitsu; Kumita, Shin‐ichiro

doi:10.15344/2456-446x/2016/113

Cited by 1 publication

(1 citation statement)

References 71 publications

(89 reference statements)

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“…The diaphragm in men, breast in women, lateral chest walls, and abdomen in patients with large body mass index can be the most common source of photon attenuation artifacts in myocardial perfusion SPECT imaging. The photon attenuation is more significant at the low energy photons which reduces the true value of activity concentration and may present with a pseudo perfusion or metabolism defect [5,9,10].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-guided attenuation correction in the image domain for myocardial perfusion SPECT imaging

Mostafapour

Gholamiankhah

Maroufpour

et al. 2022

Journal of Computational Design and Engineering

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We investigate the accuracy of direct attenuation correction (AC) in the image domain for myocardial perfusion SPECT (single-photon emission computed tomography) imaging (MPI-SPECT) using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT 99mTc-sestamibi images of 99 patients were retrospectively included. UNet and ResNet networks were trained using non-attenuation-corrected SPECT images as input, whereas CT-based attenuation-corrected (CT-AC) SPECT images served as reference. Chang’s calculated AC approach considering a uniform attenuation coefficient within the body contour was also implemented. Clinical and quantitative evaluations of the proposed methods were performed considering SPECT CT-AC images of 19 subjects (external validation set) as reference. Image-derived metrics, including the voxel-wise mean error (ME), mean absolute error, relative error, structural similarity index (SSI), and peak signal-to-noise ratio, as well as clinical relevant indices, such as total perfusion deficit (TPD), were utilized. Overall, AC SPECT images generated using the deep learning networks exhibited good agreement with SPECT CT-AC images, substantially outperforming Chang’s method. The ResNet and UNet models resulted in an ME of −6.99 ± 16.72 and −4.41 ± 11.8 and an SSI of 0.99 ± 0.04 and 0.98 ± 0.05, respectively. Chang’s approach led to ME and SSI of 25.52 ± 33.98 and 0.93 ± 0.09, respectively. Similarly, the clinical evaluation revealed a mean TPD of 12.78 ± 9.22% and 12.57 ± 8.93% for ResNet and UNet models, respectively, compared to 12.84 ± 8.63% obtained from SPECT CT-AC images. Conversely, Chang’s approach led to a mean TPD of 16.68 ± 11.24%. The deep learning AC methods have the potential to achieve reliable AC in MPI-SPECT imaging.

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Section: Introductionmentioning

confidence: 99%