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3D printed patient specific vascular models provide the ability to perform precise and repeatable benchtop experiments with simulated physiological blood flow conditions. This approach can be applied to CT-derived patient geometries to determine coronary flow related parameters such as Fractional Flow Reserve (FFR). To demonstrate the utility of this approach we compared bench-top results with non-invasive CT-derived FFR software based on a computational fluid dynamics algorithm and catheter based FFR measurements. Twelve patients for whom catheter angiography was clinically indicated signed written informed consent to CT Angiography (CTA) before their standard care that included coronary angiography (ICA) and conventional FFR (Angio-FFR). The research CTA was used first to determine CT-derived FFR (Vital Images) and second to generate patient specific 3D printed models of the aortic root and three main coronary arteries that were connected to a programmable pulsatile pump. Benchtop FFR was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. All 12 patients completed the clinical study without any complication, and the three FFR techniques (Angio-FFR, CT-FFR, and Benchtop FFR) are reported for one or two main coronary arteries. The Pearson correlation among Benchtop FFR/Angio-FFR, CT-FFR/ Benchtop FFR, and CT-FFR/ Angio-FFR are 0.871, 0.877, and 0.927 respectively. 3D printed patient specific cardiovascular models successfully simulated hyperemic blood flow conditions, matching invasive Angio-FFR measurements. This benchtop flow system could be used to validate CT-derived FFR diagnostic software, alleviating both cost and risk during invasive procedures.

Accurate patient-specific phantoms for device testing or endovascular treatment planning can be 3D printed. We expand the applicability of this approach for cardiovascular disease, in particular, for CT-geometry derived benchtop measurements of Fractional Flow Reserve, the reference standard for determination of significant individual coronary artery atherosclerotic lesions. Coronary CT Angiography (CTA) images during a single heartbeat were acquired with a 320×0.5mm detector row scanner (Toshiba Aquilion ONE). These coronary CTA images were used to create 4 patient-specific cardiovascular models with various grades of stenosis: severe,

Complex vascular anatomies can cause the failure of image-guided endovascular procedures. 3D printed patient-specific vascular phantoms provide clinicians and medical device companies the ability to preemptively plan surgical treatments, test the likelihood of device success, and determine potential operative setbacks. This research aims to present advanced mesh manipulation techniques of stereolithographic (STL) files segmented from medical imaging and post-print surface optimization to match physiological vascular flow resistance. For phantom design, we developed three mesh manipulation techniques. The first method allows outlet 3D mesh manipulations to merge superfluous vessels into a single junction, decreasing the number of flow outlets and making it feasible to include smaller vessels. Next we introduced Boolean operations to eliminate the need to manually merge mesh layers and eliminate errors of mesh self-intersections that previously occurred. Finally we optimize support addition to preserve the patient anatomical geometry. For post-print surface optimization, we investigated various solutions and methods to remove support material and smooth the inner vessel surface. Solutions of chloroform, alcohol and sodium hydroxide were used to process various phantoms and hydraulic resistance was measured and compared with values reported in literature. The newly mesh manipulation methods decrease the phantom design time by 30 - 80% and allow for rapid development of accurate vascular models. We have created 3D printed vascular models with vessel diameters less than 0.5 mm. The methods presented in this work could lead to shorter design time for patient specific phantoms and better physiological simulations.

Background: Quantitative angiography (QA) in two dimensions has been instrumental in assessing neurovascular contrast flow patterns, aiding disease severity evaluation and treatment outcome prediction using data-driven models. However, QA requires high temporal and spatial resolution, restricting its use to digital subtraction angiography (DSA)., Purpose: The 2D projective nature of DSA introduces errors and noise due to the inherently three-dimensional flow dynamics. This study examines whether 3D QA information can be recovered by reconstructing four-dimensional (4D) angiography using data from standard clinical imaging protocols., Methods: Patient-specific 3D vascular geometries were used to generate high-fidelity computational fluid dynamics (CFD) simulations of contrast flow in internal carotid aneurysms. The resulting 4D angiograms, representing ground truth, were used to simulate biplane DSA under clinical imaging protocols, including projection spacing and injection timing. 4D angiography was reconstructed from two views using back-projection constrained by an a priori 3D geometry. Quantitative angiographic parametric imaging (API) metrics obtained from the CFD-based 4D angiography and reconstructed 4D angiography, respectively, were compared using mean square error (MSE) and mean absolute percentage error (MAPE)., Results: The reconstructed 4D datasets effectively captured 3D flow dynamics, achieving an average MSE of 0.007 across models and flow conditions. API metrics such as PH and AUC closely matched the CFD ground truth, with temporal metrics showing some variability in regions with overlapping projections. These results demonstrate the potential to recover 3D QA information using simulated 4D angiography constrained by standard clinical imaging parameters., Conclusions: This study highlights the feasibility of recovering 3D QA information from reconstructed 4D DSA simulated from biplane projections. The method provides a robust framework for evaluating and improving QA in clinical neurovascular applications, offering new insights into the dynamics of aneurysmal contrast flow.