Artificial intelligence-based attenuation correction; closer to clinical reality?

Miller, Robert J.H.; Slomka, Piotr J.

doi:10.1007/s12350-021-02724-5

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…The use of artificial intelligence and deep learning to improve and facilitate different aspects of the diagnostic process has become increasingly popular. In nuclear cardiology, such methods have been used to facilitate attenuation correction 17 and automate reports of myocardial perfusion imaging. [18][19][20][21] However, very few have used artificial intelligence to optimize the interpretation of PET imaging.…”

Section: Strengths and Weaknesses Of The Studymentioning

confidence: 99%

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Piri

Edenbrandt

Larsson³

et al. 2022

Journal of Nuclear Cardiology

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Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.Methods: A trained convolutional neural network (CNN) was used for cardiac segmentation in 18 Fsodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analysed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%.Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans.

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Section: Strengths and Weaknesses Of The Studymentioning

confidence: 99%

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Piri

Edenbrandt

Larsson³

et al. 2022

Journal of Nuclear Cardiology

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“…Conventionally, arti cial intelligence has been applied in nuclear cardiology to predict obstructive disease on SPECT (4). Recently, an arti cial intelligence-based attenuation correction has been demonstrated (5). Several attenuation correction approaches were proposed, such as generating attenuation maps (µ-maps) from emission images (6, 7), and predicting attenuation-corrected images from non-attenuation-corrected images (8)(9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

Virtual myocardial PET generated from deep learning of SPECT and PET datasets: potential for attenuation correction in CT-less SPECT

Kawakubo

Nagao

Kaimoto

et al. 2023

Preprint

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Objective Deep-learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computerized tomography (SPECT). This study evaluated the improvement in visual ischemia scoring accuracy to investigate the performance of virtual positron emission tomography (vPET) generated by a deep-learning model. Methods This retrospective study included the patient-to-patient stress, resting SPECT, and PET datasets of 54 patients. The vPET generation model was trained and validated using 34 cases with over 1200 image pairs using an image-to-image translation network. The SPECT, PET, and vPET images from another 20 cases were blindly scored in the stress and resting states. Results The SPECT rest scores at septal and inferior walls (segments #2 and #15) were significantly higher than those of PET. However, no significant differences were observed between the vPET and PET scores. Diagnostic performance of SPECT for detecting PET defect areas were improved with the use of vPET visual scores. Conclusions vPET, a new approach for improving ischemic visual score at rest in the well-known attenuated region on SPECT, can be applied as a clinical support tool that provides powerful auxiliary information for myocardial blood flow diagnosis since standalone SPECT is used worldwide.

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“…8 Attempts to incorporate artificial intelligence into nuclear cardiology have been made for automatic image segmentation, diagnosis and risk stratification of CAD, and image reconstruction. 9 Recently, several studies have been conducted on attenuation correction for MPS images. [10][11][12][13] A convolutional neural network or generative adversarial network (GAN) has been used to generate an attenuation map or perform direct attenuation correction in the image domain or polar map without using CT.…”

mentioning

confidence: 99%

“…Attempts to incorporate artificial intelligence into nuclear cardiology have been made for automatic image segmentation, diagnosis and risk stratification of CAD, and image reconstruction 9 . Recently, several studies have been conducted on attenuation correction for MPS images 10–13 .…”

mentioning

confidence: 99%

Clinical Feasibility of Deep Learning–Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT

Lim,

Park,

Lee

et al. 2024

Clin Nucl Med

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Purpose We aimed to develop deep learning (DL)–based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility. Patients and Methods We conducted a retrospective study of patients with suspected or known coronary artery disease. We proposed a DL-based image-to-image translation technique to transform non–attenuation-corrected images into CT-based attenuation-corrected (CTAC) images. The model was trained using a modified U-Net with structural similarity index (SSIM) loss and mean squared error (MSE) loss and compared with other models. Segment-wise analysis using a polar map and visual assessment for the generated attenuation-corrected (GENAC) images were also performed to evaluate clinical feasibility. Results This study comprised 657 men and 328 women (age, 65 ± 11 years). Among the various models, the modified U-Net achieved the highest performance with an average mean absolute error of 0.003, an SSIM of 0.990, and a peak signal-to-noise ratio of 33.658. The performance of the model was not different between the stress and rest datasets. In the segment-wise analysis, the myocardial perfusion of the inferior wall was significantly higher in GENAC images than in the non–attenuation-corrected images in both the rest and stress test sets (P < 0.05). In the visual assessment of patients with diaphragmatic attenuation, scores of 4 (similar to CTAC images) or 5 (indistinguishable from CTAC images) were assigned to most GENAC images (65/68). Conclusions Our clinically feasible DL-based attenuation correction models can replace the CT-based method in Tl-201 MPS, and it would be useful in case SPECT/CT is unavailable for MPS.

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Artificial intelligence-based attenuation correction; closer to clinical reality?

Cited by 8 publications

References 19 publications

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Virtual myocardial PET generated from deep learning of SPECT and PET datasets: potential for attenuation correction in CT-less SPECT

Clinical Feasibility of Deep Learning–Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT

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