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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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MRI
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Imaging Studies IV: Magnetic Resonance Imaging01:27

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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Privacy-Preserving Virtual Contrast-enhanced MRI for Nasopharyngeal Carcinoma: A Multi-center Study.

Wen Li1, Zhen Li2, Yiming Shi1

  • 1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.

International Journal of Radiation Oncology, Biology, Physics
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Summary

A new federated learning model creates virtual contrast-enhanced MRI (VCE-MRI) from standard scans for nasopharyngeal carcinoma (NPC) patients. This non-invasive approach offers a safer alternative to gadolinium contrast agents, proving clinically reliable for diagnosis and staging.

Keywords:
Contrast-enhanced MRIfederated learninggeneralizabilitynasopharyngeal carcinoma

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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Medical Imaging
  • Oncology

Background:

  • Contrast-enhanced MRI is vital for nasopharyngeal carcinoma (NPC) management.
  • Gadolinium-based contrast agents pose risks, especially for patients with kidney issues.
  • A safer alternative for MRI contrast enhancement is needed.

Purpose of the Study:

  • To develop and assess the clinical feasibility of a federated learning model for synthesizing virtual contrast-enhanced MRI (VCE-MRI) images.
  • To create VCE-MRI from contrast-free scans for NPC patients, avoiding gadolinium contrast agents.
  • To provide a safer imaging alternative for NPC diagnosis, staging, and treatment.

Main Methods:

  • Developed and evaluated a federated learning-based VCE-MRI synthesis model (FL-VCE-MRI).
  • Trained the model on pre-treatment contrast-free T1-weighted and T2-weighted MRI scans from 14 centers (2,061 patients).
  • Validated externally using data from 25 centers (126 patients) and assessed by ten clinicians.

Main Results:

  • The FL-VCE-MRI model demonstrated high generalizability with a mean absolute error (MAE) of 45.85 in external validation.
  • Improved average MAE from 53.93 to 45.93 for centers with challenging single-center models.
  • Synthesized VCE-MRI images were deemed reliable and clinically valuable, comparable to gadolinium-enhanced MRI for diagnosis, staging, and delineation.

Conclusions:

  • The FL-VCE-MRI model shows significant potential as a non-invasive alternative to gadolinium-enhanced MRI for NPC patients.
  • This approach addresses the need for safer contrast enhancement in MRI for cancer imaging.
  • Federated learning enables robust VCE-MRI synthesis across multiple institutions.