BackgroundQuantitative single photon emission computed tomography (SPECT) is challenging, especially for pancreatic beta cell imaging with 111In-exendin due to high uptake in the kidneys versus much lower uptake in the nearby pancreas. Therefore, we designed a three-dimensionally (3D) printed phantom representing the pancreas and kidneys to mimic the human situation in beta cell imaging. The phantom was used to assess the effect of different reconstruction settings on the quantification of the pancreas uptake for two different, commercially available software packages.Methods3D-printed, hollow pancreas and kidney compartments were inserted into the National Electrical Manufacturers Association (NEMA) NU2 image quality phantom casing. These organs and the background compartment were filled with activities simulating relatively high and low pancreatic 111In-exendin uptake for, respectively, healthy humans and type 1 diabetes patients. Images were reconstructed using Siemens Flash 3D and Hermes Hybrid Recon, with varying numbers of iterations and subsets and corrections. Images were visually assessed on homogeneity and artefacts, and quantitatively by the pancreas-to-kidney activity concentration ratio.ResultsPhantom images were similar to clinical images and showed comparable artefacts. All corrections were required to clearly visualize the pancreas. Increased numbers of subsets and iterations improved the quantitative performance but decreased homogeneity both in the pancreas and the background. Based on the phantom analyses, the Hybrid Recon reconstruction with 6 iterations and 16 subsets was found to be most suitable for clinical use.ConclusionsThis work strongly contributed to quantification of pancreatic 111In-exendin uptake. It showed how clinical images of 111In-exendin can be interpreted and enabled selection of the most appropriate protocol for clinical use.

Purpose: This study investigated the effect of different EndoAnchor configurations on aortic endograft displacement resistance in an in vitro model. Materials and Methods: An in vitro model was developed and validated to perform displacement force measurements on different EndoAnchor configurations within an endograft and silicone tube. Five EndoAnchor configurations were created: (1) 6 circumferentially deployed EndoAnchors, (2) 5 EndoAnchors within 120° of the circumference and 1 additional, contralateral EndoAnchor, (3) 4 circumferentially deployed EndoAnchors, (4) 2 rows of 4 circumferentially deployed EndoAnchors, and (5) a configuration of 2 columns of 3 EndoAnchors. An experienced vascular surgeon deployed EndoAnchors under C-arm guidance at the proximal sealing zone of the endograft. A constant force with increments of 1 newton (N) was applied to the distal end of the endograft. The force necessary to displace a part of the endograft by 3 mm was defined as the endograft displacement force (EDF). Two video cameras recorded the measurements. Videos were examined to determine the exact moment 3-mm migration had occurred at part of the endograft. Five measurements were performed after each deployed EndoAnchor for each configuration. Measurements are given as the median and interquartile range (IQR) Q1, Q3. Results: Baseline displacement force measurement of the endograft without EndoAnchors resulted in a median EDF of 5.1 N (IQR 4.8, 5.2). The circumferential distribution of 6 EndoAnchors resulted in a median EDF of 53.7 N (IQR 49.0, 59.0), whereas configurations 2 through 5 demonstrated substantially lower EDFs of 29.0 N (IQR 28.5, 30.1), 24.6 N (IQR 21.9, 27.2), 36.7 N, and 9.6 N (IQR 9.4, 10.0), respectively. Decreasing the distance between the EndoAnchors over the circumference of the endograft increased the displacement resistance. Conclusion: This in vitro study demonstrates the influence EndoAnchor configurations have on the displacement resistance of an aortic endograft. Parts of the endograft where no EndoAnchor has been deployed remain sensitive to migration. In the current model, the only configuration that rivaled a hand-sewn anastomosis was the one with 6 EndoAnchors. A circumferential distribution of EndoAnchors with small distances between EndoAnchors should be pursued, if possible. This study provides a quantification of different EndoAnchor configurations that clinicians may have to adopt in clinical practice, which can help them make a measured decision on where to deploy EndoAnchors to ensure good endograft fixation.

Background Absolute myocardial perfusion imaging (MPI) is beneficial in the diagnosis and prognosis of patients with suspected or known coronary artery disease. However, validation and standardization of perfusion estimates across centers is needed to ensure safe and adequate integration into the clinical workflow. Physical myocardial perfusion models can contribute to this clinical need as these can provide ground-truth validation of perfusion estimates in a simplified, though controlled setup. This work presents the design and realization of such a myocardial perfusion phantom and highlights initial performance testing of the overall phantom setup using dynamic single photon emission computed tomography. Results Due to anatomical and (patho-)physiological representation in the 3D printed myocardial perfusion phantom, we were able to acquire 22 dynamic MPI datasets in which 99mTc-labelled tracer kinetics was measured and analyzed using clinical MPI software. After phantom setup optimization, time activity curve analysis was executed for measurements with normal myocardial perfusion settings (1.5 mL/g/min) and with settings containing a regional or global perfusion deficit (0.8 mL/g/min). In these measurements, a specific amount of activated carbon was used to adsorb radiotracer in the simulated myocardial tissue. Such mimicking of myocardial tracer uptake and retention over time satisfactorily matched patient tracer kinetics. For normal perfusion levels, the absolute mean error between computed myocardial blood flow and ground-truth flow settings ranged between 0.1 and 0.4 mL/g/min. Conclusion The presented myocardial perfusion phantom is a first step toward ground-truth validation of multimodal, absolute MPI applications in the clinical setting. Its dedicated and 3D printed design enables tracer kinetic measurement, including time activity curve and potentially compartmental myocardial blood flow analysis.

We aim to facilitate phantom-based (ground truth) evaluation of dynamic, quantitative myocardial perfusion imaging (MPI) applications. Current MPI phantoms are static representations or lack clinical hard- and software evaluation capabilities. This proof-of-concept study demonstrates the design, realisation and testing of a dedicated cardiac flow phantom. The 3D printed phantom mimics flow through a left ventricular cavity (LVC) and three myocardial segments. In the accompanying fluid circuit, tap water is pumped through the LVC and thereafter partially directed to the segments using adjustable resistances. Regulation hereof mimics perfusion deficit, whereby flow sensors serve as reference standard. Seven phantom measurements were performed while varying injected activity of 99mTc-tetrofosmin (330–550 MBq), cardiac output (1.5–3.0 L/min) and myocardial segmental flows (50–150 mL/min). Image data from dynamic single photon emission computed tomography was analysed with clinical software. Derived time activity curves were reproducible, showing logical trends regarding selected input variables. A promising correlation was found between software computed myocardial flows and its reference ($$\rho$$ ρ = − 0.98; p = 0.003). This proof-of-concept paper demonstrates we have successfully measured first-pass LV flow and myocardial perfusion in SPECT-MPI using a novel, dedicated, myocardial perfusion phantom. Graphical abstract This proof-of-concept study focuses on the development of a novel, dedicated myocardial perfusion phantom, ultimately aiming to contribute to the evaluation of quantitative myocardial perfusion imaging applications.

Validation of an image registration and segmentation method to measure stent graft motion on ECG-gated CT using a physical dynamic stent graft model,"