Hemodynamics and stresses in numerical simulations of the thoracic aorta: Stochastic sensitivity analysis to inlet flow-rate waveform

Numerical simulations of the blood flow inside a patient-specific thoracic aorta in presence of an aneurysm are considered. We focus on the impact on the numerical predictions of the inlet flow-rate waveform. First, the results obtained by using an idealized and a MRI-measured flow-rate waveform are compared. The measured boundary condition produces significantly higher wall shear stresses than those obtained in the idealized case. Discrepancies are reduced but they are still present even if the idealized inlet waveform is rescaled in order to match the stroke volume. This motivates a systematic sensitivity analysis of numerical predictions to the shape of the inlet flow-rate waveform that is carried out in the second part of the paper. Two parameters are selected to describe the inlet waveform: the stroke volume and the period of the cardiac cycle. A stochastic approach based on the generalized Polynomial Chaos (gPC) approach, in which continuous response surfaces of the quantities of interest in the parameter space can be obtained from a limited number of simulations, is used. For both selected uncertain parameters, we use beta PDFs reproducing clinical data. The two selected input parameters appear to have a significant influence on wall shear stresses as well as on the velocity distribution in vessel regions characterized by large curvature. This confirms the need of using patient-specific inlet conditions to obtain reliable hemodynamic predictions

Comparison between numerical and mri data of ascending aorta hemodynamics in a circulatory mock loop

The possibility to obtain in-vitro evaluation of hemodynamic flows and pressures is of capital importance in understanding cardiovascular pathologies and validating new devices. The in-vitro approach brings different advantage including the possibility to evaluate fluid dynamic parameters non invasively and to support in-silico simulations. In this study, a patient specific mock circulatory loop is developed to reproduce the fluid dynamic physiological conditions. A full sequence of 4D flow MRI is used as a benchmark for extraction of anatomical and functional patient specific data. The anatomical MRI data were used to realize a 3D printed rigid phantom of the complete aortic branch. The phantom is realized with a single inlet at aortic root level and 4 outlets corresponding to the supra-aortic arteries and the descending aorta. The model is then inserted inside a custom mock-circulatory loop, composed by an active component and a series of passive components to model the systemic resistances and compliances at each branch. The active component is responsible for the imposition of the flow rate waveform at the inlet section and it is constituted by a custom speed-controlled piston pump. The inlet flow rate is set by automatically interpolating the patient specific aortic flow from MRI data. The functional MRI data were used to validate the flow condition at each outlet branch. In the present work the preliminary results of the circulatory mock loop are compared with MRI data and with the results of numerical simulations carried out for the considered aorta geometry and inlet flow rate by using Simvascular. The in-vitro flow profiles are compared with the in-vivo and the numerical ones in the descending aorta and at each outlet branch. The reproduction of the flow rate is successful, with errors at systolic peaks of 5 cc/s and 1.66 cc/s for the supra-aortic and the descending aorta level respectively. Also the physiological pressure range is rather well reproduced in the in-vitro experiments

Impact of uncertainties in outflow boundary conditions on the predictions of hemodynamic simulations of ascending thoracic aortic aneurysms

The impact of outflow boundary conditions on the results of numerical simulations of the flow inside ascending thoracic aortic aneurysms is investigated. The adopted outflow conditions are based on the lumped three-element Windkessel model, in which it is assumed that the pressure at the outflow is related to the flow rate and to a terminal constant pressure, through an electric circuit analogue that has a proximal resistance in series with a parallel arrangement of a capacitance and of a distal resistance. The values of the Windkessel model parameters must be a-priori specified. The impact on the numerical simulation results of uncertainties in the values of the Windkessel model parameters is quantified here through a stochastic approach. The propagation of the considered uncertainties is evaluated, in particular, for the instantaneous and time-averaged wall shear stress. The generalized Polynomial Chaos is used as a surrogate model to obtain continuous response surfaces of the quantities of interest in the parameter space starting from a few deterministic simulations. A patient-specific geometry, obtained through in-vivo imaging, is considered. The analysis is carried out for both rigid and compliant vessel walls. Our results show that, although the uncertainties in the selected outflow parameters may give significant variability of the instantaneous shear stresses in regions characterized by flow recirculation or large streamline curvature, the impact on cycle-averaged shear stresses is moderate. Taking into account the wall compliance seems to reduce the impact of the uncertainties in the outflow parameters compared to the rigid case

Effects of the Distribution in Space of the Velocity-Inlet Condition in Hemodynamic Simulations of the Thoracic Aorta

In the present paper the effects of the spatial distribution of the inlet velocity in numerical simulations of the thoracic aorta have been investigated. First, the results obtained by considering in-vivo measured inlet velocity distribution are compared with the ones obtained for a simulation having the same flow rate waveform and plug flow condition at the inlet section. The results of the two simulations are consistent in terms of flow rate waveform, but differences are present in the pressure range and in the wall shear stresses, especially in the foremost part of the ascending aorta. This motivates a stochastic sensitivity analysis on the effect of the distribution in space of the inlet velocity. This distribution is modeled through a truncated-cone shape and the ratio between the upper and the lower base is selected as the uncertain parameter. The uncertainty is propagated through the numerical model and a continuous response surface of the output quantities of interest in the parameter space can be recovered through a “surrogate” model. A stochastic method based on the generalized Polynomial Chaos (gPC) approach is used herein. The selected parameter appears to have a significant influence on the velocity distribution in the ascending aorta, whereas it has a negligible effect in the descending part. This, in turn, produces significant effects on the wall shear stresses in the ascending aorta, confirming the need of using patient-specific inlet conditions if interested in the hemodynamics and stresses of this region

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long

Uncertainty quantification applied to hemodynamic simulations of thoracic aorta aneurysms: Sensitivity to inlet conditions

In this work, the numerical simulation of the blood flow inside a patient specific aorta in presence of an aneurysm is considered. A systematic sensitivity analysis of numerical predictions to the shape of the inlet flow rate waveform is carried out. In particular, two parameters are selected to describe the inlet waveform: the stroke volume and the period of the cardiac cycle. In order to limit the number of hemodynamic simulations required, we used a stochastic method based on the generalized polynomial chaos (gPC) approach, in which the selected parameters are considered as random variables with a given probability distribution. The uncertainty is propagated through the numerical model and a continuous response surface of the output quantities of interest in the parameter space can be recovered through a “surrogate” model. For both selected uncertain parameters, we first assumed uniform Probability Density Functions (PDFs) on a given variation range, and then we used clinical data to construct more accurate beta PDFs. In all cases, the two input parameters appeared to have a significant influence on wall shear stresses, confirming the need of using patient-specific inlet conditions

Gastrinomas and non-functioning pancreatic endocrine tumors in multiple endocrine neoplasia syndrome type-1 (MEN-1)

Multiple endocrine neoplasia type-1 (MEN-1) is a rare hereditary autosomal dominant syndrome due to frameshift and non-sense mutations in the MEN-1 tumor suppressor gene localized on the long arm of chromosome 11 [1]. Also known as Wermer syndrome, it has a prevalence of 2–20/100,000 individuals who may develop multiple neoplastic lesions arising in the parathyroid (90–95%) as well as the pituitary glands (40–50%), the pancreatic islet cells (50–60%) and the duodenal wall (35–40%) [2]. While the most common clinical onset of patients affected by MEN-1 is due to primary hyperparathyroidism [3], pancreatic endocrine tumors (PNETs) represent the main cause of cancer-related death, which is most commonly due to non-functioning (NF) subtypes [4]. Indeed, these tend to have a more aggressive behavior compared to their sporadic counterparts with a malignant potential reported to be size-related with a cut-off value set at 2 cm [5,6,7]. Hence, active surveillance with endoscopic ultrasonography (EUS) combined with either contrast-enhanced multi-detector-CT (MDCT) [8] or magnetic resonance imaging [9] is strongly recommended in patients with MEN-1 syndrome. As far as contrast-enhanced MDCT is concerned, recent advances suggest that contrast-enhancement patterns of PNETs may be indeed predictive of tumor grading defined as the rate of expression of the proliferation index Ki-67 [10]. As most G1 (Ki-67 <3%) tumors usually appear as hypervascular lesions, G2 (Ki-67 3–20%) or G3 (Ki-67 >20%) tumors typically manifest as hypovascular lesions [11,12,13]. However, as PNETs in MEN-1 syndrome are usually multifocal [14], the co-existence of lesions with different contrast-enhancement patterns and different biological behavior may indeed occur in clinical practice. Herein, we describe a case of 48-year-old male with a genetic diagnosis of MEN-1 syndrome who had a Zollinger–Ellison syndrome due to duodenal gastrinomas shown by an EUS and confirmed by contrast-enhanced MDCT, which also depicted loco-regional adenopathies and three other NF-PNETs with different contrast-enhancement patterns and biological behavior

Distribution of NHBA peptides in the 300 isolates panel.

<p>Others^a: NHBA peptides not present in more than one isolate (peptides 8, 9, 13, 25, 47, 53, 115, 160, 187, 237, 304, 307, 308, 309, 310, 355, 367, 368, 370, 385, 460, 461, 462, 463, 464, 465, 468, 469, 470, 471). Others cc^b: Others clonal complexes. NA^c: Clonal complexes non assigned.</p