We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




3D Modeling System Accurately Predicts Pediatric Donor Heart Volumes

By MedImaging International staff writers
Posted on 24 Nov 2015
Print article
Image: 3D scan a child’s heart born with congenital heart defects (Photo courtesy of the Phoenix Children’s Hospital).
Image: 3D scan a child’s heart born with congenital heart defects (Photo courtesy of the Phoenix Children’s Hospital).
A new three dimensional (3D) computer modeling system may more accurately identify the best donor heart for a pediatric transplant patient.

To develop the new 3D system, researchers at Arizona State University (ASU; Tempe, USA) and Phoenix Children’s Hospital (AZ, USA) first created a library of 3D reconstructed hearts in healthy children weighing up to 45 kilograms, using magnetic resonance imaging (MRI) and computerized tomography (CT) scans. They then used the virtual library to predict the best donor body weight/heart size correlation needed for pediatric transplant recipients. Concomitantly, they examined before and after images from infants who had already received a heart transplant.

When the researchers compared the post-operative data from the real infants with the virtual transplant images, they found that the 3D imaging system accurately identified an appropriate size heart, validating their findings. The researchers are currently expanding the virtual library to improve prognostic capabilities, thus allowing more effective organ allocation and minimizing the number of otherwise acceptable organs that are ultimately discarded. The study was presented at the annual American Heart Association (AHA) Scientific Sessions, held during November 2015 in Orlando (FL, USA).

“It is critical to optimize the range of acceptable donors for each child. 3D reconstruction has tremendous potential to improve donor size matching,” said lead author and study presenter Jonathan Plasencia, BSc, of the ASU image processing applications lab. “We feel that we now have evidence that 3D matching can improve selection and hope this will soon help transplant doctors, patients, and their parents make the best decision by taking some of the uncertainty out of this difficult situation.”

“Analyzing future transplant cases using 3D matching will allow us to predict the true upper and lower limits of acceptable donor size. The big question is how long it will take to further test the technique and move it into actual use,” concluded Mr. Plasencia, who is a PhD student at ASU. “One day transplant teams may be able to use the 3D process to perform virtual transplants before an actual procedure to rapidly measure a donated heart to ensure a better fit and to reduce the risk of mismatching in pediatric transplants.”

Transplant centers currently assess compatibility of a potential donor heart by comparing the donor weight to the recipient weight, and then picking an upper and lower limit based on the size of the patient’s heart on chest X-ray. But the assessment is not precise and variations in size and volume can have a major effect on the recipient’s outcome.

Related Links:

Arizona State University
Phoenix Children’s Hospital 


New
Gold Member
X-Ray QA Meter
T3 AD Pro
New
40/80-Slice CT System
uCT 528
Radiation Therapy Treatment Software Application
Elekta ONE
New
Digital Radiographic System
OMNERA 300M

Print article
Radcal

Channels

Radiography

view channel
Image: The new X-ray detector produces a high-quality radiograph (Photo courtesy of ACS Central Science 2024, DOI: https://doi.org/10.1021/acscentsci.4c01296)

Highly Sensitive, Foldable Detector to Make X-Rays Safer

X-rays are widely used in diagnostic testing and industrial monitoring, from dental checkups to airport luggage scans. However, these high-energy rays emit ionizing radiation, which can pose risks after... Read more

MRI

view channel
Image: Artificial intelligence models can be trained to distinguish brain tumors from healthy tissue (Photo courtesy of 123RF)

AI Can Distinguish Brain Tumors from Healthy Tissue

Researchers have made significant advancements in artificial intelligence (AI) for medical applications. AI holds particular promise in radiology, where delays in processing medical images can often postpone... Read more

Nuclear Medicine

view channel
Image: Example of AI analysis of PET/CT images (Photo courtesy of Academic Radiology; DOI: 10.1016/j.acra.2024.08.043)

AI Analysis of PET/CT Images Predicts Side Effects of Immunotherapy in Lung Cancer

Immunotherapy has significantly advanced the treatment of primary lung cancer, but it can sometimes lead to a severe side effect known as interstitial lung disease. This condition is characterized by lung... Read more

General/Advanced Imaging

view channel
Image: Cleerly offers an AI-enabled CCTA solution for personalized, precise and measurable assessment of plaque, stenosis and ischemia (Photo courtesy of Cleerly)

AI-Enabled Plaque Assessments Help Cardiologists Identify High-Risk CAD Patients

Groundbreaking research has shown that a non-invasive, artificial intelligence (AI)-based analysis of cardiac computed tomography (CT) can predict severe heart-related events in patients exhibiting symptoms... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.