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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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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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A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling

Anne Oyarzun-Domeño1, Izaskun Cia2, Rebeca Echeverria-Chasco3

  • 1Electrical Electronics and Communications Engineering, Public University of Navarre, 31006 Pamplona, Spain; IdiSNA, Health Research Institute of Navarra, 31008, Spain.

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|September 30, 2023
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Summary

This study introduces an automated method using deep learning for renal perfusion estimation from MRI scans, improving kidney transplant evaluation. The AI accurately segments kidney tissues, providing reliable perfusion values for allograft assessment.

Keywords:
AllograftDeep learningMRIRenal perfusionSegmentation

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Area of Science:

  • Medical Imaging
  • Biomarkers
  • Transplant Surgery

Background:

  • Accurate renal tissue segmentation is crucial for estimating kidney perfusion and assessing allograft health post-transplant.
  • Manual segmentation is time-consuming and susceptible to human error, hindering efficient clinical workflows.
  • Non-contrast pseudo-continuous arterial spin labeling (PCASL) MRI offers a valuable, non-invasive method for evaluating kidney transplant status.

Purpose of the Study:

  • To develop and validate an automated image analysis method for renal perfusion estimation using PCASL MRI.
  • To segment and classify renal cortical and medullary tissues automatically.
  • To automate the estimation of perfusion values for improved kidney transplant evaluation.

Main Methods:

  • Utilized machine/deep learning algorithms for automated segmentation and classification of renal tissues (cortex and medulla).
  • Employed time-intensity curves from non-contrasted T1-weighted MRI series for tissue analysis.
  • Validated the method using data from 16 kidney transplant patients.

Main Results:

  • Achieved high accuracy in tissue segmentation: Dice similarity coefficients of >93% (whole kidney), >92% (cortex), and >82% (medulla).
  • Automated estimation of cortical and medullary perfusion values.
  • Demonstrated that the estimated perfusion values fall within clinically acceptable ranges.

Conclusions:

  • The proposed automated method accurately segments renal tissues and estimates perfusion using PCASL MRI.
  • This AI-driven approach enhances the efficiency and reliability of kidney transplant evaluation.
  • The technique provides a valuable biomarker for assessing allograft status in clinical practice.