scholarly journals An augmented patient-specific approach to administration of contrast agent for CT renal angiography

2019 ◽  
Vol 45 (5) ◽  
pp. 1022-1032 ◽  
Author(s):  
Charbel Saade ◽  
Nadine Hamieh ◽  
Ibrahim Al-Sheikh Deeb ◽  
Maurice Haddad ◽  
Alain S. Abi-Ghanem ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Patient Specific ◽  
Renal Angiography ◽  
Specific Approach

An optimised patient-specific approach to administration of contrast agent for CT pulmonary angiography

2013 ◽  
Vol 23 (11) ◽  
pp. 3205-3212 ◽  
Author(s):  
Charbel Saade ◽  
Roger Bourne ◽  
Fadi El-Merhi ◽  
Arjuna Somanathan ◽  
Dev Chakraborty ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Pulmonary Angiography ◽  
Patient Specific ◽  
Ct Pulmonary Angiography ◽  
Specific Approach

A Patient-specific Approach for Measuring Functional Status in Low Back Pain

Physiotherapy ◽  
1996 ◽  
Vol 82 (8) ◽  
pp. 467 ◽  
Author(s):  
AJHM Beurskens ◽  
HCW de Vet ◽  
AJA Köke
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Functional Status ◽  
Patient Specific ◽  
Low Back ◽  
Specific Approach

scholarly journals Development of a tool-kit for the detection of healthy and injured cardiac tissue based on MR imaging

2017 ◽  
Vol 3 (2) ◽  
pp. 195-198
Author(s):  
Philip Westphal ◽  
Sebastian Hilbert ◽  
Michael Unger ◽  
Claire Chalopin
Keyword(s):  
Contrast Agent ◽  
Cardiac Tissue ◽  
Software Tool ◽  
Ease Of Use ◽  
Free Software ◽  
Patient Specific ◽  
Initial Contour ◽  
Data Sets ◽  
Heart Model ◽  
Data Set

AbstractPlanning of interventions to treat cardiac arrhythmia requires a 3D patient specific model of the heart. Currently available commercial or free software dedicated to this task have important limitations for routinely use. Automatic algorithms are not robust enough while manual methods are time-consuming. Therefore, the project attempts to develop an optimal software tool. The heart model is generated from preoperative MR data-sets acquired with contrast agent and allows visualisation of damaged cardiac tissue. A requirement in the development of the software tool was the use of semi-automatic functions to be more robust. Once the patient image dataset has been loaded, the user selects a region of interest. Thresholding functions allow selecting the areas of high intensities which correspond to anatomical structures filled with contrast agent, namely cardiac cavities and blood vessels. Thereafter, the target-structure, for example the left ventricle, is coarsely selected by interactively outlining the gross shape. An active contour function adjusts automatically the initial contour to the image content. The result can still be manually improved using fast interaction tools. Finally, possible scar tissue located in the cavity muscle is automatically detected and visualized on the 3D heart model. The model is exported in format which is compatible with interventional devices at hospital. The evaluation of the software tool included two steps. Firstly, a comparison with two free software tools was performed on two image data sets of variable quality. Secondly, six scientists and physicians tested our tool and filled out a questionnaire. The performance of our software tool was visually judged more satisfactory than the free software, especially on the data set of lower quality. Professionals evaluated positively our functionalities regarding time taken, ease of use and quality of results. Improvements would consist in performing the planning based on different MR modalities.

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.

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