Color-coded patient-specific physical models of congenital heart disease

2014 ◽  
Vol 20 (4) ◽  
pp. 336-343 ◽  
Author(s):  
Fariha Ejaz ◽  
Justin Ryan ◽  
Megan Henriksen ◽  
Lillee Stomski ◽  
Megan Feith ◽  
...  

Purpose – The purpose of this study was to develop and apply new physical heart defect models (PHDMs) that are patient-specific and color-coded with an optimized map. Design/methodology/approach – Heart defect anatomies were segmented from medical images and reconstructed to form virtual models, which were then color-coded and rapid prototyped. The resulting PHDMs were used in a medical educational study to evaluate their pedagogical efficacy and in clinical case studies to investigate their utility in surgical planning. Findings – A growing library of 36 PHDMs (including the most common defects) was generated. Results from the educational study showed that the PHDMs enabled uniquely effective learning, and the clinical case studies indicated that the models added value as surgical planning aids. Research limitations/implications – The education study involved a limited number of students, so future work should consider a larger sample size. The clinical case studies favored use of the PHDMs in surgical planning, but provided only qualitative support. Practical implications – Workflow optimization is critical for PHDMs to be used effectively in surgical planning because some operations must be performed in emergently. Social implications – Because PHDMs have potential to influence surgeons’ actions as surgical planning aids, their use in that context must be thoroughly vetted. Originality/value – The proposed models represent the first PHDMs that are patient-specific and fully color-coded with a standardized map optimized for the human visual system. The models enhanced medical education and facilitated effective surgical planning in this study.

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8048
Author(s):  
Declan O’Loughlin ◽  
Muhammad Adnan Elahi ◽  
Benjamin R. Lavoie ◽  
Elise C. Fear ◽  
Martin O’Halloran

Microwave breast imaging has seen increasing use in clinical investigations in the past decade with over eight systems having being trialled with patients. The majority of systems use radar-based algorithms to reconstruct the image shown to the clinician which requires an estimate of the dielectric properties of the breast to synthetically focus signals to reconstruct the image. Both simulated and experimental studies have shown that, even in simplified scenarios, misestimation of the dielectric properties can impair both the image quality and tumour detection. Many methods have been proposed to address the issue of the estimation of dielectric properties, but few have been tested with patient images. In this work, a leading approach for dielectric properties estimation based on the computation of many candidate images for microwave breast imaging is analysed with patient images for the first time. Using five clinical case studies of both healthy breasts and breasts with abnormalities, the advantages and disadvantages of computational patient-specific microwave breast image reconstruction are highlighted.


2013 ◽  
Vol 12 (2) ◽  
pp. 75-75

The case studies below are referred to in the articles “Pulmonary Hypertension in Patients with Chronic Kidney Disease: Noninvasive Strategies for Patient Phenotyping and Risk Assessment” by Amresh Raina, MD, and “Hemodynamic Evaluation of Pulmonary Hypertension in Chronic Kidney Disease” by Ryan Tedford, MD, and Paul Forfia, MD, on the following pages.


Author(s):  
Luc J. Jordaens ◽  
Dominic A.M.J. Theuns

Author(s):  
E. Nocerino ◽  
F. Remondino ◽  
F. Uccheddu ◽  
M. Gallo ◽  
G. Gerosa

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice.


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