Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Tatsugami, Fuminari; Nakaura, Takeshi; Yanagawa, Masahiro; Fujita, Shohei; Kamagata, Koji; Ito, Rintaro; Kawamura, Mitsuo; Fushimi, Yasutaka; Ueda, Daiju; Matsui, Yusuke; Yamada, Akira; Fujima, Noriyuki; Fujioka, Tomoyuki; Nozaki, Taiki; Tsuboyama, Takahiro; Hirata, Kenji; Naganawa, Shinji

doi:10.1016/j.diii.2023.06.011

Cited by 25 publications

(2 citation statements)

References 108 publications

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“…Recently, 2nd generation deep learning-based reconstruction (DLR), which is a newly developed DLR algorithm that uses U-HRCT data as supervised data with further noise reduction, has been clinically introduced for conventional 320-row multidetector CT (MDCT) ( 10 ). The use of 2nd generation DLR is expected to improve image quality (e.g., vessel sharpness) with minimal image noise in the prospective ECG-gating coronary CTA scan at 320-row MDCT ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Sawamura,

Kato,

Funama

et al. 2024

Quant Imaging Med Surg

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Background Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

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Section: Introductionmentioning

confidence: 99%

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Sawamura,

Kato,

Funama

et al. 2024

Quant Imaging Med Surg

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“…A single inhalation clench was used to complete an intravenously sharpened enhanced heart angiography, and perception was used to rebuild pictures that matched the echocardiograph [8]. Two researchers compared algebraic average cardiac angiography results to each portion and sector of the coronary artery without stents [9]. Three clusters are used to calculate standard deviations and to classify patients based on average heart rate [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

Devi,

Ramaswami

2024

Nano Biomedicine and Engineering

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Heart angiography is a test that allows the treating physician to identify heart vessel abnormalities. The concerned doctor must spend a lot of time on this diagnosis. The heart vessels' targeted areas are segmented, and the blood vessel types are classified in the proposed approach. The heart angiography segmentation and classification provide essential information for the patient and the doctor. In contrast, the charge for cardiac angiography is a risky, mentally challenging task for the doctor (a heart specialist). Improved transparency and faster detection of heart diseases can result from automated segmentation and categorization of representations of heart blood vessels. Since accuracy is critical in classification, computer vision researchers are introducing several techniques; however, achieving high accuracy remains challenging when classifying heart diseases. This research proposes a multi-class ensemble classification mechanism-based computer-aided hybrid deep learning (DL) network model for localizing human blood vessels inside heart coronary computed tomography (CT) angiography images. First, the modified U-Net model is used to segment the heart blood vessels, and the self-attention network (SAT-Net) model is used to extract various features from the segmented blood vessel images. In segmented human heart blood vessels, low-level features are retrieved, including geometrical, statistical, and texture features. The enhanced elman spike neural network (EESNN) model has been used to finally classify cardiac blood vessels into four distinct groups: blood flow-reduced, narrow, block, and regular vessels. The suggested strategy has produced the best results, creating a conducive environment for cardiologists to diagnose heart-related disorders that is quick, simple, accurate, and time-saving.

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Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions – new opportunities for precision medicine

Androshchuk,

Montarello,

Lahoti

et al. 2024

Int J Cardiovasc Imaging

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The last decade has witnessed a substantial growth in percutaneous treatment options for heart valve disease. The development in these innovative therapies has been mirrored by advances in multi-detector computed tomography (MDCT). MDCT plays a central role in obtaining detailed pre-procedural anatomical information, helping to inform clinical decisions surrounding procedural planning, improve clinical outcomes and prevent potential complications. Improvements in MDCT image acquisition and processing techniques have led to increased application of advanced analytics in routine clinical care. Workflow implementation of patient-specific computational modeling, fluid dynamics, 3D printing, extended reality, extracellular volume mapping and artificial intelligence are shaping the landscape for delivering patient-specific care. This review will provide an insight of key innovations in the field of MDCT for planning transcatheter heart valve interventions.

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Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Cited by 25 publications

References 108 publications

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions – new opportunities for precision medicine

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