Detection of arterial wall abnormalities via Bayesian model selection

Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.

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Detection of arterial wall abnormalities via Bayesian model selection

10.1101/422485 ◽

2018 ◽

Author(s):

Karen Larson ◽

Clark Bowman ◽

Costas Papadimitriou ◽

Petros Koumoutsakos ◽

Anastasios Matzavinos

Keyword(s):

Parameter Estimation ◽

Model Selection ◽

Parallel Implementation ◽

Real Data ◽

Physical Models ◽

Patient Specific ◽

Small Scale ◽

Tree Model ◽

Arterial Tree ◽

Flow Models

AbstractPatient-specific modeling of hemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for hemodynamical flow models.

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Multiscale Computational Fluid Dynamics Modeling for Personalized Liver Cancer Radioembolization Dosimetry

Journal of Biomechanical Engineering ◽

10.1115/1.4047656 ◽

2020 ◽

Vol 143 (1) ◽

Cited By ~ 2

Author(s):

Amirtahà Taebi ◽

Catherine T. Vu ◽

Emilie Roncali

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Radiation Dose ◽

Liver Cancer ◽

Treatment Planning ◽

Invasive Procedure ◽

Patient Specific ◽

Dynamics Modeling ◽

Tree Model ◽

Arterial Tree

Abstract Yttrium-90 (90Y) radioembolization is a minimally invasive procedure increasingly used for advanced liver cancer treatment. In this method, radioactive microspheres are injected into the hepatic arterial bloodstream to target, irradiate, and kill cancer cells. Accurate and precise treatment planning can lead to more efficient and safer treatment by delivering a higher radiation dose to the tumor while minimizing the exposure of the surrounding liver parenchyma. Treatment planning primarily relies on the estimated radiation dose delivered to tissue. However, current methods used to estimate the dose are based on simplified assumptions that make the dosimetry results unreliable. In this work, we present a computational model to predict the radiation dose from the 90Y activity in different liver segments to provide a more realistic and personalized dosimetry. Computational fluid dynamics (CFD) simulations were performed in a 3D hepatic arterial tree model segmented from cone-beam CT angiographic data obtained from a patient with hepatocellular carcinoma (HCC). The microsphere trajectories were predicted from the velocity field. 90Y dose distribution was then calculated from the volumetric distribution of the microspheres. Two injection locations were considered for the microsphere administration, a lobar and a selective injection. Results showed that 22% and 82% of the microspheres were delivered to the tumor, after each injection, respectively, and the combination of both injections ultimately delivered 49% of the total administered 90Y microspheres to the tumor. Results also illustrated the nonhomogeneous distribution of microspheres between liver segments, indicating the importance of developing patient-specific dosimetry methods for effective radioembolization treatment.

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Mathematical Model Based on the Shape of Pulse Waves Measured at a Single Spot for the Non-Invasive Prediction of Blood Pressure

Processes ◽

10.3390/pr8040442 ◽

2020 ◽

Vol 8 (4) ◽

pp. 442

Author(s):

Lukas Peter ◽

Jan Kracik ◽

Martin Cerny ◽

Norbert Noury ◽

Stanislav Polzer

Keyword(s):

Blood Pressure ◽

Computational Models ◽

Patient Specific ◽

Tree Model ◽

Arterial Tree ◽

Single Spot ◽

Accurate Model ◽

Non Invasive ◽

Comparison Results ◽

Pulse Waves

Background: Continuous non-invasive blood pressure (BP) measurement is a desired virtue in clinical practice. Unfortunately, current systems do not allow one for continuous, reliable BP measurement for more than a few hours per day, and they often require a complicated set of sensors to provide the necessary biosignals. Therefore we investigated the possibility of proposing a computational model that would predict the BP from pulse waves recorded in a single spot. Methods: Two experimental circuits were created. One containing a simple plastic tube for model development and a second with a silicone molded patient-specific arterial tree model. The first model served for the measuring of pulse waves under various BP (70–270 mmHg) and heart rate (60–190 beats per minute) values. Four different computational models were used to estimate the BP values from the diastolic time. The most accurate model was further validated using data from the latter experimental circuit containing a molded patient-specific silicone arterial tree. The measured data were averaged over a window of one, three, and five cycles. Two models based on pulse arrival time (PAT) were also analyzed for comparison. Results: The most accurate model exhibits a correlation coefficient of r = 0.967. The Bland–Altman plot revealed standard deviations (SD) between the model predictions and measurement of 10, 8.3, and 7.5 mmHg for the systolic BP and 8.7, 7 and 6.3 mmHg for the diastolic BP (both pressures calculated for the averaging windows of one, three, and five cycles, respectively). The best of the used PAT based model exhibited a SD of 17, 16, and 15 mmHg for the systolic BP and 14, 13, and 12 mmHg for the diastolic BP for the same averaging windows. Discussion: The proposed model showed its capability to predict BP accurately from the shape of the pulse wave measured at a single spot. Its SD was about 50% lower compared to the PAT based models which met the requirements of the Association for the Advancement of Medical Instrumentation.

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Justifying the data analytical choice in single case research in relation to the expected data pattern

10.31234/osf.io/2b9mu ◽

2019 ◽

Author(s):

Rumen Manolov

Keyword(s):

Data Analysis ◽

Visual Analysis ◽

Multilevel Models ◽

Single Case ◽

Analytical Techniques ◽

Real Data ◽

Small Scale ◽

Data User ◽

Single Case Research ◽

User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

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Full tractography for detecting the position of cranial nerves in preoperative planning for skull base surgery: technical note

Journal of Neurosurgery ◽

10.3171/2019.1.jns182638 ◽

2020 ◽

Vol 132 (5) ◽

pp. 1642-1652 ◽

Cited By ~ 2

Author(s):

Timothee Jacquesson ◽

Fang-Chang Yeh ◽

Sandip Panesar ◽

Jessica Barrios ◽

Arnaud Attyé ◽

...

Keyword(s):

Skull Base ◽

Preoperative Planning ◽

Skull Base Surgery ◽

Cranial Nerves ◽

Patient Specific ◽

Small Scale ◽

Skull Base Tumor ◽

Skull Base Tumors ◽

Brain White Matter ◽

Human Connectome Project

OBJECTIVEDiffusion imaging tractography has allowed the in vivo description of brain white matter. One of its applications is preoperative planning for brain tumor resection. Due to a limited spatial and angular resolution, it is difficult for fiber tracking to delineate fiber crossing areas and small-scale structures, in particular brainstem tracts and cranial nerves. New methods are being developed but these involve extensive multistep tractography pipelines including the patient-specific design of multiple regions of interest (ROIs). The authors propose a new practical full tractography method that could be implemented in routine presurgical planning for skull base surgery.METHODSA Philips MRI machine provided diffusion-weighted and anatomical sequences for 2 healthy volunteers and 2 skull base tumor patients. Tractography of the full brainstem, the cerebellum, and cranial nerves was performed using the software DSI Studio, generalized-q-sampling reconstruction, orientation distribution function (ODF) of fibers, and a quantitative anisotropy–based generalized deterministic algorithm. No ROI or extensive manual filtering of spurious fibers was used. Tractography rendering was displayed in a tridimensional space with directional color code. This approach was also tested on diffusion data from the Human Connectome Project (HCP) database.RESULTSThe brainstem, the cerebellum, and the cisternal segments of most cranial nerves were depicted in all participants. In cases of skull base tumors, the tridimensional rendering permitted the visualization of the whole anatomical environment and cranial nerve displacement, thus helping the surgical strategy.CONCLUSIONSAs opposed to classical ROI-based methods, this novel full tractography approach could enable routine enhanced surgical planning or brain imaging for skull base tumors.

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Parameter estimation techniques for transport equations with application to population dispersal and tissue bulk flow models

Journal of Mathematical Biology ◽

10.1007/bf00276516 ◽

1983 ◽

Vol 17 (3) ◽

Cited By ~ 34

Author(s):

H.T. Banks ◽

P. Kareiva

Keyword(s):

Parameter Estimation ◽

Transport Equations ◽

Bulk Flow ◽

Estimation Techniques ◽

Flow Models ◽

Population Dispersal ◽

Parameter Estimation Techniques

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Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth-death Markov chain Monte Carlo

Water Resources Research ◽

10.1029/2007wr006427 ◽

2009 ◽

Vol 45 (6) ◽

Cited By ~ 28

Author(s):

Salaheddine El Adlouni ◽

Taha B. M. J. Ouarda

Keyword(s):

Monte Carlo ◽

Parameter Estimation ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Model Selection ◽

Bayesian Model ◽

Extreme Value ◽

Extreme Value Model ◽

Value Model ◽

Generalized Extreme Value Model

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Toward a noninvasive subject-specific estimation of abdominal aortic pressure

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.01154.2007 ◽

2008 ◽

Vol 295 (3) ◽

pp. H1156-H1164 ◽

Cited By ~ 2

Author(s):

Carl-Johan Thore ◽

Jonas Stålhand ◽

Matts Karlsson

Keyword(s):

Aortic Pressure ◽

Processing Technique ◽

Distributed Model ◽

Specific Information ◽

Peripheral Artery ◽

Tree Model ◽

Signal Processing Technique ◽

Arterial Tree ◽

One Dimensional ◽

Abdominal Aortic

A method for estimation of central arterial pressure based on linear one-dimensional wave propagation theory is presented in this paper. The equations are applied to a distributed model of the arterial tree, truncated by three-element windkessels. To reflect individual differences in the properties of the arterial trees, we pose a minimization problem from which individual parameters are identified. The idea is to take a measured waveform in a peripheral artery and use it as input to the model. The model subsequently predicts the corresponding waveform in another peripheral artery in which a measurement has also been made, and the arterial tree model is then calibrated in such a way that the computed waveform matches its measured counterpart. For the purpose of validation, invasively recorded abdominal aortic, brachial, and femoral pressures in nine healthy subjects are used. The results show that the proposed method estimates the abdominal aortic pressure wave with good accuracy. The root mean square error (RMSE) of the estimated waveforms was 1.61 ± 0.73 mmHg, whereas the errors in systolic and pulse pressure were 2.32 ± 1.74 and 3.73 ± 2.04 mmHg, respectively. These results are compared with another recently proposed method based on a signal processing technique, and it is shown that our method yields a significantly ( P < 0.01) lower RMSE. With more extensive validation, the method may eventually be used in clinical practice to provide detailed, almost individual, specific information as a valuable basis for decision making.

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