Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application

Catapano, Federica; Lisi, Costanza; Savini, Giovanni; Olivieri, Marzia; Figliozzi, Stefano; Caracciolo, Alessandra; Monti, Lorenzo; Francone, Marco

doi:10.1097/rct.0000000000001537

Cited by 2 publications

(1 citation statement)

References 29 publications

(43 reference statements)

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“…We applied DLIR to CTPA studies in suspected PE. Our results are in line with those that have been described for various cardiovascular CT applications including coronary CTA 19 – 23 , CT for planning of transcatheter aortic valve repair 24 , 25 , CT of the aorta 26 , head and neck CT angiography 27 and triple-rule-out CT 28 . In the field of thoracic imaging, several previous studies have investigated the value of DLIR with regards to pulmonary imaging and lung nodule detection.…”

Section: Discussionsupporting

confidence: 90%

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

Klemenz,

Albrecht,

Manzke

et al. 2024

Sci Rep

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We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA studies were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V 30%, 60% and 90%) and DLIR (low, medium and high strength). Contrast-to-noise ratio (CNR) served as the primary parameter of objective image quality. Subgroup analyses were performed for normal weight, overweight and obese individuals. For patients with confirmed PE (n = 40), we further measured PE-specific CNR. Subjective image quality was assessed independently by two experienced radiologists. CNR was lowest for FBP and enhanced with increasing levels of ASiR-V and, even more with increasing strength of DLIR. High strength DLIR resulted in an additional improvement in CNR by 29–67% compared to ASiR-V 90% (p < 0.05). PE-specific CNR increased by 75% compared to ASiR-V 90% (p < 0.05). Subjective image quality was significantly higher for medium and high strength DLIR compared to all other image reconstructions (p < 0.05). In CT pulmonary angiography, DLIR significantly outperforms iterative reconstruction for increasing objective and subjective image quality. This may allow for further reductions in radiation exposure in suspected PE.

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Section: Discussionsupporting

confidence: 90%

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

Klemenz,

Albrecht,

Manzke

et al. 2024

Sci Rep

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CT imaging post-TAVI: Murphy’s first law in action—preparing to recognize the unexpected

Lisi,

Catapano,

Brilli

et al. 2024

Insights Imaging

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Transfemoral aortic valve implantation (TAVI) has been long considered the standard of therapy for high-risk patients with severe aortic-stenosis and is now effectively employed in place of surgical aortic valve replacement also in intermediate-risk patients. The potential lasting consequences of minor complications, which might have limited impact on elderly patients, could be more noteworthy in the longer term when occurring in younger individuals. That’s why a greater focus on early diagnosis, correct management, and prevention of post-procedural complications is key to achieve satisfactory results. ECG-triggered multidetector computed tomography angiography (CTA) is the mainstay imaging modality for pre-procedural planning of TAVI and is also used for post-interventional early detection of both acute and long-term complications. CTA allows detailed morphological analysis of the valve and its movement throughout the entire cardiac cycle. Moreover, stent position, coronary artery branches, and integrity of the aortic root can be precisely evaluated. Imaging reliability implies the correct technical setting of the computed tomography scan, knowledge of valve type, normal post-interventional findings, and awareness of classic and life-threatening complications after a TAVI procedure. This educational review discusses the main post-procedural complications of TAVI with a specific imaging focus, trying to clearly describe the technical aspects of CTA Imaging in post-TAVI and its clinical applications and challenges, with a final focus on future perspectives and emerging technologies. Critical relevance statement This review undertakes an analysis of the role computed tomography angiography (CTA) plays in the assessment of post-TAVI complications. Highlighting the educational issues related to the topic, empowers radiologists to refine their clinical approach, contributing to enhanced patient care. Key Points Prompt recognition of TAVI complications, ranging from value issues to death, is crucial. Adherence to recommended scanning protocols, and the optimization of tailored protocols, is essential. CTA is central in the diagnosis of TAVI complications and functions as a gatekeeper to treatment. Graphical Abstract

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Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application

Cited by 2 publications

References 29 publications

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

CT imaging post-TAVI: Murphy’s first law in action—preparing to recognize the unexpected

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