Generation of synthetic aortic valve stenosis geometries for in silico trials.

In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid-structure-interaction models to perform in silico trials.

Comparison of true non-contrast and virtual non-contrast images in the characterization of renal lesions using detector-based spectral CT.

OBJECTIVES: Renal lesions are sometimes incidentally detected during computed tomography (CT) examinations in which an unenhanced series is not included, preventing the lesions from being fully characterized. The aim of this study was to investigate the feasibility to use virtual non-contrast (VNC) images, acquired using a detector-based dual-energy CT, for the characterization of renal lesions. METHODS: Twenty-seven patients (12 women) underwent a renal CT scan, including a non-contrast, an arterial, and a venous phase contrast-enhanced series, using a detector-based dual-energy CT scanner. VNC images were reconstructed from the venous contrast-enhanced series. The mean attenuation values of 65 renal lesions in both the VNC and true non-contrast (TNC) images were measured and compared quantitatively. Three radiologists blindly assessed all lesions using either VNC or TNC images in combination with contrast-enhanced images. RESULTS: Sixteen patients had cystic lesions, five had angiomyolipoma (AML), and six had suspected renal cell carcinomas (RCC). Attenuation values in VNC and TNC images were strongly correlated (ρ = 0.7, mean difference -6.0 ± 13 HU). The largest differences were found for unenhanced high-attenuation lesions. Radiologists classified 86% of the lesions correctly using VNC images. CONCLUSIONS: In 70% of the patients, incidentally detected renal lesions could be accurately characterized using VNC images, resulting in less patient burden and a reduction in radiation exposure. ADVANCES IN KNOWLEDGE: This study shows that renal lesions can be accurately characterized using VNC images acquired by detector-based dual-energy CT, which is in agreement with previous studies using dual-source and rapid X-ray tube potential switching technique.

Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms.

Methods for patient-specific abdominal aortic aneurysm (AAA) progression monitoring and rupture risk assessment are widely investigated. Three-dimensional ultrasound can visualize the AAA's complex geometry and displacement fields. However, ultrasound has a limited field of view and low frame rate (i.e., 3-8 Hz). This article describes an approach to enhance the temporal resolution and the field of view. First, the frame rate was increased for each data set by sequencing multiple blood pulse cycles into one cycle. The sequencing method uses the original frame rate and the estimated pulse wave rate obtained from AAA distension curves. Second, the temporal registration was applied to multi-perspective acquisitions of the same AAA. Third, the field of view was increased through spatial registration and fusion using an image feature-based phase-only correlation method and a wavelet transform, respectively. Temporal sequencing was fully correct in aortic phantoms and was successful in 51 of 62 AAA patients, yielding a factor 5 frame rate increase. Spatial registration of proximal and distal ultrasound acquisitions was successful in 32 of 37 different AAA patients, based on the comparison between the fused ultrasound and computed tomography segmentation (95th percentile Haussdorf distances and similarity indices of 4.2 ± 1.7 mm and 0.92 ± 0.02 mm, respectively). Furthermore, the field of view was enlarged by 9%-49%.