Dataset Information

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.

ABSTRACT:

Background

This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI).

Methods

We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) and DLIR. Intravascular image noise, edge sharpness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified for ascending aorta, descending aorta, abdominal aorta and iliac arteries. Two readers (one radiologist and one interventional cardiologist) scored task-specific subjective image quality on a five-point scale.

Results

DLIR significantly reduced median image noise by 29-57% at all anatomical locations (all P<0.001). Accordingly, median SNR improved by 44-133% (all P<0.001) and median CNR improved by 44-125% (all P<0.001). DLIR significantly improved subjective image quality for all four pre-specified TAVI-specific tasks (measuring the annulus, assessing valve morphology and calcifications, the coronary ostia, and the suitability of the aorto-iliac access route) for both the radiologist and the interventional cardiologist (P≤0.001). Measurements of the aortic annulus circumference, area and diameter did not differ between ASIR-V and DLIR reconstructions (all P>0.05).

Conclusions

DLIR significantly improves objective and subjective image quality in TAVI planning CT compared to a state-of-the-art iterative reconstruction without affecting measurements of the aortic annulus. This may provide an opportunity for further reductions in contrast medium volume in this population.

SUBMITTER: Heinrich A

PROVIDER: S-EPMC9929406 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.

Heinrich Andra A Yücel Seyrani S Böttcher Benjamin B Öner Alper A Manzke Mathias M Klemenz Ann-Christin AC Weber Marc-André MA Meinel Felix G FG

Quantitative imaging in medicine and surgery 20221220 2

<h4>Background</h4>This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI).<h4>Methods</h4>We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) ...[more]

PMID: 36819291

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Project description:BackgroundTranscatheter aortic valve implantation (TAVI) is a well-established treatment for patients with severe aortic valve stenosis. This procedure requires pre-operative planning by assessment of aortic dimensions on CT Angiography (CTA). It is well-known that the aortic root dimensions vary over the heart cycle. However, sizing is commonly performed at either mid-systole or end-diastole only, which has resulted in an inadequate understanding of its full dynamic behavior.Study goalWe studied the variation in annulus measurements during the cardiac cycle and determined if this variation is dependent on the amount of calcification at the annulus.MethodsWe measured and compared aortic root annular dimensions and calcium volume in CTA acquisitions at 10 cardiac cycle phases in 51 aortic stenosis patients. Sub-group analysis was performed based on the volume of calcium by splitting the population into mildly and severely calcified valves subgroups.ResultsFor most annulus measurements, the largest differences were found between 10% and 70 to 80% cardiac cycle phases. Mean difference (±standard deviation) in annular minimum diameter, maximum diameter, area, and aspect ratio between mid-systole and end-diastole phases were 1.0 ± 0.29 mm (p = 0.065), 0.30 ± 0.24 mm (p = 0.7), 24.1 ± 7.6 mm2 (p < 0.001), and 0.041 ± 0.012 (p = 0.039) respectively. Calcium volume measurements varied strongly during the cardiac cycle. The dynamic annulus area was behaving differently between mildly and severely calcified subgroups (p = 0.02). Furthermore, patients with severe aortic calcification were associated with larger annulus diameters.ConclusionThere is a significant variation of annulus area and calcium volume measurement during the cardiac cycle. In our measurements, only the dynamic variation of the annulus area is dependent on the severity of the aortic calcification. For TAVI candidates, the annulus area is significantly larger in mid-systole compared to end-diastole.

Project description:ObjectiveTranscatheter aortic valve implantation (TAVI) procedures are increasing rapidly, but the durability of tissue valve and periprocedural complications are not satisfactory. Immune reaction to the galactose-α-1,3 galactose β-1,4-N-acetylglucosamine (α-Gal) and conventional processing protocols of cardiac xenografts lead to calcification. Next-generation TAVI needs to be made with α-Gal-free xenografts by multiple anticalcification therapies to avoid immune rejection and enhance durability, and three-dimensional (3D) printing technology to improve the procedural safety.MethodsPorcine pericardia were decellularized and immunologically modified with α-galactosidase. The pericardia were treated by space filler, crosslinked with glutaraldehyde in organic solvent, and detoxified. The sheep-specific nitinol (nickel-titanium memory alloy) wire backbone was made from a 3D-printed model for ovine aortic root. After it passed the fitting test, we manufactured a self-expandable stented valve with the porcine pericardia mounted on the customized nitinol wire-based stent. After in vitro circulation using customized silicone aortic root, we performed TAVI in 9 sheep and obtained hemodynamic, radiological, immunohistopathological, and biochemical results.ResultsThe valve functioned well, with excellent stent fitting and good coronary flow under in vitro circulation. Sheep were sequentially scheduled to be humanely killed until 238 days after TAVI. Echocardiography and cardiac catheterization demonstrated good hemodynamic status and function of the aortic valve. The xenografts were well preserved without α-Gal immune reaction or calcification based on the immunological, radiographic, microscopic, and biochemical examinations.ConclusionsWe proved preclinical safety and efficacy for next-generation α-Gal-free TAVI with multiple anticalcification therapies and 3D-printing technology. A future clinical study is warranted based on these promising preclinical results.

Dataset Information

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.

Background

Methods

Results

Conclusions

Publications

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.

Similar Datasets

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