scholarly journals Construction of patient‐specific computational models for organ dose estimation in radiological imaging

2019 ◽  
Vol 46 (5) ◽  
pp. 2403-2411 ◽  
Author(s):  
Tianwu Xie ◽  
Azadeh Akhavanallaf ◽  
Habib Zaidi
Keyword(s):  
Computational Models ◽  
Organ Dose ◽  
Patient Specific ◽  
Radiological Imaging ◽  
Dose Estimation

From patient-informed to patient-specific organ dose estimation in clinical computed tomography

2018 ◽  
Author(s):  
Wanyi Fu ◽  
William P. Segars ◽  
Ehsan Abadi ◽  
Shobhit Sharma ◽  
Anuj J. Kapadia ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Organ Dose ◽  
Patient Specific ◽  
Dose Estimation ◽  
Specific Organ

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

scholarly journals Patient-Informed Organ Dose Estimation in Clinical CT: Implementation and Effective Dose Assessment in 1048 Clinical Patients

2021 ◽  
Vol 216 (3) ◽  
pp. 824-834
Author(s):  
Wanyi Fu ◽  
Francesco Ria ◽  
William Paul Segars ◽  
Kingshuk Roy Choudhury ◽  
Joshua M. Wilson ◽  
...  
Keyword(s):  
Effective Dose ◽  
Organ Dose ◽  
Dose Assessment ◽  
Dose Estimation

EFFECTIVE DOSE ESTIMATION FROM ORGAN DOSE MEASUREMENTS IN FAST-kV SWITCH DUAL ENERGY COMPUTED TOMOGRAPHY

2018 ◽  
Author(s):  
Justin Raudabaugh ◽  
Giao Nguyen ◽  
Carolyn Lowry ◽  
Natalie Januzis ◽  
James Colsher ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Effective Dose ◽  
Organ Dose ◽  
Dual Energy ◽  
Dose Estimation ◽  
Dual Energy Computed Tomography ◽  
Dose Measurements

Accuracy of Cartilage Geometry Reconstruction From Volumetric CT Data: A Phantom Study

2007 ◽  
Author(s):  
Andrew E. Anderson ◽  
Benjamin J. Ellis ◽  
Christopher L. Peters ◽  
Jeffrey A. Weiss
Keyword(s):  
Articular Cartilage ◽  
Contrast Agent ◽  
Computational Models ◽  
Image Data ◽  
Cartilage Thickness ◽  
Patient Specific ◽  
Joint Spacing ◽  
Ct Arthrography ◽  
Diarthrodial Joints ◽  
Joint Contact

Segmentation of medical image data is often used for the construction of computational models to study the mechanics of diarthrodial joints such as the hip and knee. The analyst must demonstrate that the reconstructed geometry is an accurate representation of the true continuum to ensure model validity. This becomes especially important for computational modeling of joint contact, which requires accurate reconstruction of articular cartilage. Although volumetric computed tomography (CT) is often used to image diarthrodial joints, the lower bounds for detecting articular cartilage thickness and the influence of imaging parameters on the ability to image cartilage have not been reported. The use of contrast agent (CT arthrography) is necessary to visualize the surface of articular cartilage in live patients. Thus, it is of primary interest to quantify the accuracy of CT arthrography to demonstrate the feasibility of patient-specific modeling. The objectives of this study were to assess the accuracy and detection limits of CT for measuring simulated cartilage thickness using a phantom and to quantify changes in accuracy due to alterations in contrast agent concentration, imaging plane direction, spatial resolution and joint spacing.

Stereoscopic Particle Image Velocimetry of Native Ovine Mitral Valves in a Pulsatile Left Heart Simulator

2012 ◽  
Author(s):  
Jean-Pierre Rabbah ◽  
Neelakantan Saikrishnan ◽  
Ajit P. Yoganathan
Keyword(s):  
Particle Image Velocimetry ◽  
Mitral Valve ◽  
Computational Models ◽  
Particle Image ◽  
Numerical Models ◽  
Stereoscopic Particle Image Velocimetry ◽  
Patient Specific ◽  
Flow Loop ◽  
Mitral Valves ◽  
Image Velocimetry

Patient specific mitral valve computational models are being actively developed to facilitate surgical planning. These numerical models increasingly employ more realistic geometries, kinematics, and mechanical properties, which in turn requires rigorous experimental validation [1]. However, to date, native mitral flow dynamics have not been accurately and comprehensively characterized. In this study, we used Stereoscopic Particle Image Velocimetry (SPIV) to characterize the ventricular flow field proximal to a native mitral valve in a pulsatile experimental flow loop.

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