Visualization of CFD Results in a Virtual Reality Environment

2009 ◽  
Author(s):  
Timothy J. Gundert ◽  
Paul Hayden ◽  
Raymond Q. Migrino ◽  
John F. LaDisa
Keyword(s):  
Virtual Reality ◽  
3D Ultrasound ◽  
Hemodynamic Parameters ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Virtual Reality Environment ◽  
Spatial Dimensions ◽  
Computational Fluid Dynamics Cfd ◽  
Comprehensive Manner ◽  
Magnetic Resonance Imaging Mri

Imaging modalities such as computed tomography, 3D ultrasound and magnetic resonance imaging (MRI) facilitate detailed viewing of vascular geometries [1], but lack the ability to directly measure important hemodynamic parameters associated with the onset and progression of cardiovascular disease (i.e. pressure, wall shear stress) [2]. Computational fluid dynamics (CFD) is a noninvasive tool to quantify these indices in vessels reconstructed from imaging data. Although image-based CFD can be used to relate altered hemodynamics to vascular disease, a disjunction exists between information gathered from 4-D CFD (3 spatial dimensions and time) and the 2-D screens where results are typically displayed. In contrast, 3D virtual reality environments can be used to visualize CFD results in a comprehensive manner.

scholarly journals LAB–QA2GO: A free, easy-to-use toolbox for the quality assessment of magnetic resonance imaging data

2019 ◽  
Author(s):  
Christoph Vogelbacher ◽  
Miriam H. A. Bopp ◽  
Verena Schuster ◽  
Peer Herholz ◽  
Andreas Jansen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Virtual Machine ◽  
Signal To Noise Ratio ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Data Set ◽  
Magnetic Resonance Imaging Mri ◽  
Movement Parameters ◽  
Set Up

AbstractImage characteristics of magnetic resonance imaging (MRI) data (e.g. signal-to-noise ratio, SNR) may change over the course of a study. To monitor these changes a quality assurance (QA) protocol is necessary. QA can be realized both by performing regular phantom measurements and by controlling the human MRI datasets (e.g. noise detection in structural or movement parameters in functional datasets). Several QA tools for the assessment of MRI data quality have been developed. Many of them are freely available. This allows in principle the flexible set-up of a QA protocol specifically adapted to the aims of one’s own study.However, setup and maintenance of these tools bind time, in particular since the installation and operation often require a fair amount of technical knowledge. In this article we present a light-weighted virtual machine, named LAB-QA2GO, which provides scripts for fully automated QA analyses of phantom and human datasets. This virtual machine is ready for analysis by starting it the first time. With minimal configuration in the guided web-interface the first analysis can start within 10 minutes, while adapting to local phantoms and needs is easily possible. The usability and scope of LAB–QA2GO is illustrated using a data set from the QA protocol of our lab. With LAB–QA2GO we hope to provide an easy-to-use toolbox that is able to calculate QA statistics without high effort.

scholarly journals Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

2021 ◽  
Vol 1 ◽  
Author(s):  
Shanshan Wang ◽  
Guohua Cao ◽  
Yan Wang ◽  
Shu Liao ◽  
Qian Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Volumetric Imaging ◽  
Positron Emission ◽  
Potential Applications ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

scholarly journals Craniosynostosis: prenatal diagnosis by 2D/3D ultrasound, magnetic resonance imaging and computed tomography.

2016 ◽  
Vol 18 (3) ◽  
pp. 378 ◽  
Author(s):  
Talita Micheletti Helfer ◽  
Alberto Borges Peixoto ◽  
Gabriele Tonni ◽  
Edward Araujo Júnior
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Prenatal Diagnosis ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
Resonance Imaging ◽  
Perinatal Complications ◽  
Wide Range ◽  
Magnetic Resonance Imaging Mri

Craniosynostosis is defined as the process of premature fusion of one or more of the cranial sutures.  It is a common condition that occurs in about 1 to 2,000 live births. Craniosynostosis may be classified in primary or secondary. It is also classified as nonsyndromic or syndromic. According to suture commitment, craniosynostosis may affect a single suture or multiple sutures. There is a wide range of syndromes involving craniosynostosis and the most common are Apert, Pffeifer, Crouzon, Shaethre-Chotzen and Muenke syndromes. The underlying etiology of nonsyndromic craniosynostosis is unknown. Mutations in the fibroblast growth factor (FGF) signalling pathway play a crucial role in the etiology of craniosynostosis syndromes. Prenatal ultrasound`s detection rate of craniosynostosis is low. Nowadays, different methods can be applied for prenatal diagnosis of craniosynostosis, such as two-dimensional (2D) and three-dimensional (3D) ultrasound, magnetic resonance imaging (MRI), computed tomography (CT) scan and, finally, molecular diagnosis. The presence of craniosynostosis may affect the birthing process. Fetuses with craniosynostosis also have higher rates of perinatal complications. In order to avoid the risks of untreated craniosynostosis, children are usually treated surgically soon after postnatal diagnosis.

scholarly journals Open-Source Routines for Building Personalized Left Ventricular Models From Cardiac Magnetic Resonance Imaging Data

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Thien-Khoi N. Phung ◽  
Christopher D. Waters ◽  
Jeffrey W. Holmes
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Geometric Model ◽  
Left Ventricular ◽  
Patient Specific ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Data ◽  
Multiple Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Creating patient-specific models of the heart is a promising approach for predicting outcomes in response to congenital malformations, injury, or disease, as well as an important tool for developing and customizing therapies. However, integrating multimodal imaging data to construct patient-specific models is a nontrivial task. Here, we propose an approach that employs a prolate spheroidal coordinate system to interpolate information from multiple imaging datasets and map those data onto a single geometric model of the left ventricle (LV). We demonstrate the mapping of the location and transmural extent of postinfarction scar segmented from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI), as well as mechanical activation calculated from displacement encoding with stimulated echoes (DENSE) MRI. As a supplement to this paper, we provide MATLAB and Python versions of the routines employed here for download from SimTK.

scholarly journals Prevalence of Nonlesional Focal Epilepsy in an Adult Epilepsy Clinic

2013 ◽  
Vol 40 (2) ◽  
pp. 198-202 ◽  
Author(s):  
Dang Khoa Nguyen ◽  
Manuela Temgoua Mbacfou ◽  
Dong Bach Nguyen ◽  
Maryse Lassonde
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Focal Epilepsy ◽  
Vascular Malformations ◽  
Cortical Development ◽  
Hippocampal Atrophy ◽  
Imaging Data ◽  
Malformations Of Cortical Development ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Purpose:To evaluate the prevalence of nonlesional focal epilepsy in an adult epilepsy clinic and its refractoriness to antiepileptic drug therapy.Background:Focal epilepsy is frequently, but not always, associated with structural epileptogenic lesions identifiable on magnetic resonance imaging (MRI).Methods:We analyzed the data from all patients evaluated at an adult epilepsy clinic from January 2002 to December 2011. Clinical and paraclinical findings were used to diagnose focal epilepsy. Magnetic resonance imaging were reviewed and classified as normal, with an epileptogenic lesion, or with a lesion of unclear epileptogenicity. Epileptogenic lesions were further categorized as tumours, vascular malformations, gliosis (including hippocampal atrophy/sclerosis), and malformations of cortical development. Our study group included patients with no lesions on MRI. Pharmacoresistance of patients with nonlesional focal epilepsy was assessed using the ILAE and Perucca's criterias.Results:Out of 1521 patients evaluated (mean age 44 years; range 14-93 years), 843 had focal epilepsy. Magnetic resonance imaging data, available for 806 (96%) subjects, showed epileptogenic lesions in 65%, no obvious epileptogenic lesions in 31% and lesions of unclear epileptogenicity in 4%. Magnetic resonance imaging-identified lesions included gliosis due to an acquired insult (52% including 17% of hippocampal atrophy or sclerosis), tumours (29%), vascular malformations (16%) and malformations of cortical development (10%). Fifty-two percent of nonlesional focal epileptic patients were drug-refractory.Conclusion:In a tertiary epilepsy clinic, close to a third of patients with focal epilepsy were found to be nonlesional, half of which were drug-resistant.

An Innovative Method to Construct Silicone Cerebrovascular Replicas

2008 ◽  
Author(s):  
Juyu Chueh ◽  
Ajay K. Wakhloo ◽  
Matthew J. Gounis
Keyword(s):  
Three Dimensional ◽  
Manufacturing Processes ◽  
3D Printer ◽  
Rotational Angiography ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Vascular Lumen ◽  
Innovative Method ◽  
Magnetic Resonance Imaging Mri ◽  
Endovascular Devices

Our objective is to create cerebrovascular replicas that offer detailed geometry from clinical imaging data and have versatile applications such as testing new endovascular devices. To facilitate optical observation, simulate physiological environment and obtain reliable data, the replica is designed to be transparent and elastic with uniform thickness and good compatibility with imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and three-dimensional rotational angiography (3DRA). Several different manufacturing processes have been demonstrated in previous studies [1,2]. The physical 3D vasculature models were first obtained either by injecting methylmethacrylate into the human specimens to get vascular lumen casts of the part of interest or sending data derived from images generated from the imaging facilities to 3D printer for rapid prototyping. Different methods including repeated painting, dip-spin processing, and lost-wax technique were then applied to the casts to form the elastomeric replicas.

The Value of Quantitative Musculoskeletal Imaging

2020 ◽  
Vol 24 (04) ◽  
pp. 460-474
Author(s):  
Jacob J. Visser ◽  
Stacy K. Goergen ◽  
Stefan Klein ◽  
Teodoro Martín Noguerol ◽  
Perry J. Pickhardt ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Clinical Utility ◽  
T2 Mapping ◽  
Quantitative Imaging ◽  
Musculoskeletal Imaging ◽  
Perfusion Magnetic Resonance Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Patient Prognosis

AbstractMusculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

scholarly journals The Application Value of MRI in the Training and Physical Rehabilitation of Gastrocnemius with Acute Sports Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Xingwei Zhang ◽  
Yunfa Liu
Keyword(s):  
Clinical Diagnosis ◽  
Muscle Injury ◽  
Gastrocnemius Muscle ◽  
Sports Injury ◽  
Pathological Changes ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Good Imaging ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

The purpose of this study was to analyze the magnetic resonance imaging (MRI) signs of acute gastrocnemius injury and to provide the basis for clinical diagnosis. Thirty-two patients with acute gastrocnemius muscle injury (19 males and 13 females, aged 18–63 years, mean 46 years) were recruited, and MRI scans were performed. The results showed that all the lesions of calf gastrocnemius were detected in 32 patients, including first-degree injury in 15 cases, second-degree injury in 15 cases, and third-degree injury in 2 cases. MRI can display the location and pathological changes of acute gastrocnemius injury, providing good imaging data for clinical diagnosis and treatment.

scholarly journals Computational Fluid Dynamics Support for Fontan Planning in Minutes, Not Hours: The Next Step in Clinical Pre-Interventional Simulations

2021 ◽  
Author(s):  
Petter Frieberg ◽  
Nicolas Aristokleous ◽  
Pia Sjöberg ◽  
Johannes Töger ◽  
Petru Liuba ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Computation Time ◽  
Left Pulmonary Artery ◽  
Cfd Modeling ◽  
Resonance Imaging ◽  
Computational Fluid Dynamics Cfd ◽  
Magnetic Resonance Imaging Mri ◽  
Cfd Method ◽  
Fontan Patients

AbstractComputational fluid dynamics (CFD) modeling may aid in planning of invasive interventions in Fontan patients. Clinical application of current CFD techniques is however limited by complexity and long computation times. Therefore, we validated a “lean” CFD method to magnetic resonance imaging (MRI) and an “established” CFD method, ultimately aiming to reduce complexity to enable predictive CFD during ongoing interventions. Fifteen Fontan patients underwent MRI for CFD modeling. The differences between lean and established approach, in hepatic and total flow percentage to the left pulmonary artery (%LPA), power loss and relative wall shear stress area were 1.5 ± 4.0%, -0.17 ± 1.1%, -0.055 ± 0.092 mW and 1.1 ± 1.4%. Compared with MRI, the lean and established method showed a bias in %LPA of -1.9 ± 3.4% and -1.8 ± 3.1%. Computation time was for the lean and established approach 3.0 ± 2.0 min and 7.0 ± 3.4 h, respectively. We conclude that the proposed lean method provides fast and reliable results for future CFD support during interventions. Graphical abstract

scholarly journals L4-L5-S1 human dermatomes: a clinical, electromyographical, imaging and surgical findings

2009 ◽  
Vol 67 (2a) ◽  
pp. 265-267 ◽  
Author(s):  
Antonio Tadeu de Souza Faleiros ◽  
Luiz Antonio de Lima Resende ◽  
Marco Antonio Zanini ◽  
Heloisa Amélia de Lima Castro ◽  
Roberto Colichio Gabarra
Keyword(s):  
Clinical Signs ◽  
Signs And Symptoms ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Lateral Aspect ◽  
Posterior Aspect ◽  
Clinical Signs And Symptoms ◽  
Magnetic Resonance Imaging Mri ◽  
First Time ◽  
Inferior Limb

There is substantial controversy in literature about human dermatomes. We studied L4, L5, and S1 inferior limb dermatomes by comparing clinical signs and symptoms with conduction studies, electromyographical data, neurosurgical findings, and imaging data from computerized tomography (CT) or magnetic resonance imaging (MRI). After analyzing 60 patients, we concluded that L4 is probably located in the medial aspect of the leg, L5 in the lateral aspect of the leg and foot dorsus, and S1 in the posterior aspect of the backside, tight, leg and plantar foot skin. This is the first time that these human dermatomes have been evaluated by combined analysis of clinical, electromyographical, neurosurgical, and imaging data.

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