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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Kidney Transplant I: Introduction01:28

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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Updated: Oct 15, 2025

Non-invasive Imaging of Acute Allograft Rejection after Rat Renal Transplantation Using 18F-FDG PET
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EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS.

M Shehata1, A Shalaby1, M Ghazal2,1

  • 1Bioengineering Department, University of Louisville, Louisville, KY, USA.

Proceedings. International Conference on Image Processing
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

A new computer-assisted diagnostic (CAD) system uses blood oxygenation MRI scans to non-invasively assess kidney transplant function. This system accurately detects acute renal rejection, aiding in transplant management.

Keywords:
BOLD-MRIRenal Transplantsmachine learningmean R2*pixel-wise R2*

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Area of Science:

  • Medical Imaging
  • Nephrology
  • Artificial Intelligence

Background:

  • Non-invasive monitoring of renal transplant function is crucial for managing rejection.
  • Current methods may be invasive or lack sensitivity in early detection.
  • Accurate assessment of kidney allograft status is vital for patient outcomes.

Purpose of the Study:

  • To develop and evaluate a computer-assisted diagnostic (CAD) system for non-invasive assessment of renal transplant function.
  • To utilize blood oxygen-level-dependent magnetic resonance imaging (BOLD-MRI) to detect acute renal rejection.
  • To establish the efficacy of machine learning classifiers in differentiating between renal transplant rejection and non-rejection.

Main Methods:

  • Acquired 3D BOLD-MRI scans at multiple echo times from 15 transplant patients.
  • Segmented kidneys using the level-sets method.
  • Estimated apparent relaxation rate (R2*) from deoxyhemoglobin levels as global and local features.
  • Trained and tested machine learning classifiers, including artificial neural networks (ANN), using a leave-one-out cross-validation approach.

Main Results:

  • The CAD system achieved 93.3% accuracy in differentiating between acute renal rejection and non-rejection.
  • Demonstrated 100% sensitivity and 90% specificity in detecting renal allograft status.
  • Preliminary results indicate the system's effectiveness in non-invasive detection of rejection.

Conclusions:

  • The developed CAD system shows significant potential for non-invasive evaluation of renal transplant function.
  • BOLD-MRI combined with machine learning offers a promising approach for real-time monitoring of kidney allografts.
  • This technology could improve the management of renal transplant recipients by enabling early detection of rejection.