A comprehensive deep learning framework for MRI reconstruction

用于 MRI 重建的综合深度学习框架

基本信息

  • 批准号:
    10382334
  • 负责人:
  • 金额:
    $ 57.04万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-07-01 至 2025-03-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY/ABSTRACT The primary goal of this investigation is to develop and validate a comprehensive, robust deep learning (DL) framework that improves MRI reconstruction beyond the limits of existing technology. The proposed framework uses “plug-and-play” algorithms to combine physics-driven MR acquisition models with state-of-the-art learned image models, which are instantiated by image denoising subroutines. To fully exploit the rich structure of MR images, we propose to use DL-based denoisers that are trained in an application-specific manner. The proposed framework, termed PnP-DL, offers advantages over other existing DL methods, as well as compressed sensing (CS). Compared to existing DL methods for MRI reconstruction, PnP-DL is more immune to inevitable variations in the forward model, such as changes in the coil sensitivities or undersampling pattern, allowing it to generalize across applications and acquisition settings. Compared to CS, PnP-DL recovers images faster, with higher quality, and with potentially superior diagnostic value. Our preliminary results highlight the potential of PnP-DL to advance MRI technology. In this work, we will fur- ther develop PnP-DL and validate it in these major applications: cardiac cine, 2D brain, and 3D brain imaging. In Aim 1, we will train and optimize convolutional neural network-based application-specific denoisers for the above-mentioned applications. The denoiser with the best denoising performance will be selected for further investigation. In Aim 2, we will develop and compare different PnP algorithms. The algorithm yielding the best combination of reconstruction accuracy and computational speed will be implemented in Gadgetron for inline processing. In Aim 3, we will compare the performance of PnP-DL to other state-of-the-art methods using retro- spectively undersampled data. This study will demonstrate that, in terms of image quality, PnP-DL is superior to CS and existing DL methods and, despite higher acceleration, is non-inferior to parallel MRI with rate-2 acceler- ation. In Aim 4, we will evaluate the performance of PnP-DL using prospectively undersampled data from adult and pediatric patients. Successful completion of this project will demonstrate that PnP-DL outperforms state- of-the-art methods in terms of image quality while exhibiting a level of robustness and broad applicability that has eluded other DL-based MRI reconstruction methods. The acceleration and image quality improvement afforded by these developments will benefit almost all MRI applications, including pediatric imaging, where reducing sedation is a pressing need, and high-dimensional imaging applications (e.g., whole-heart 4D flow imaging), which are too slow for routine clinical use.
项目总结/文摘

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Rizwan Ahmad其他文献

Rizwan Ahmad的其他文献

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{{ truncateString('Rizwan Ahmad', 18)}}的其他基金

A comprehensive valvular heart disease assessment with stress cardiac MRI
通过负荷心脏 MRI 进行全面的瓣膜性心脏病评估
  • 批准号:
    10664961
  • 财政年份:
    2021
  • 资助金额:
    $ 57.04万
  • 项目类别:
A comprehensive deep learning framework for MRI reconstruction
用于 MRI 重建的综合深度学习框架
  • 批准号:
    10608060
  • 财政年份:
    2021
  • 资助金额:
    $ 57.04万
  • 项目类别:
A comprehensive deep learning framework for MRI reconstruction
用于 MRI 重建的综合深度学习框架
  • 批准号:
    10211757
  • 财政年份:
    2021
  • 资助金额:
    $ 57.04万
  • 项目类别:
A comprehensive valvular heart disease assessment with stress cardiac MRI
通过负荷心脏 MRI 进行全面的瓣膜性心脏病评估
  • 批准号:
    10455412
  • 财政年份:
    2021
  • 资助金额:
    $ 57.04万
  • 项目类别:
Prospective Slice Tracking for Cardiac MRI
心脏 MRI 的前瞻性切片跟踪
  • 批准号:
    9762101
  • 财政年份:
    2018
  • 资助金额:
    $ 57.04万
  • 项目类别:
A New Paradigm for Rapid, Accurate Cardiac Magnetic Resonance Imaging
快速、准确的心脏磁共振成像的新范例
  • 批准号:
    10171886
  • 财政年份:
    2017
  • 资助金额:
    $ 57.04万
  • 项目类别:
A New Paradigm for Rapid, Accurate Cardiac Magnetic Resonance Imaging
快速、准确的心脏磁共振成像的新范例
  • 批准号:
    9330525
  • 财政年份:
    2017
  • 资助金额:
    $ 57.04万
  • 项目类别:
MRI T2 mapping for quantitative assessment of venous oxygen saturation
用于定量评估静脉血氧饱和度的 MRI T2 映射
  • 批准号:
    9325034
  • 财政年份:
    2016
  • 资助金额:
    $ 57.04万
  • 项目类别:
Background phase correction for quantitative cardiovascular MRI
定量心血管 MRI 的背景相位校正
  • 批准号:
    9297307
  • 财政年份:
    2016
  • 资助金额:
    $ 57.04万
  • 项目类别:
Background phase correction for quantitative cardiovascular MRI
定量心血管 MRI 的背景相位校正
  • 批准号:
    9182586
  • 财政年份:
    2016
  • 资助金额:
    $ 57.04万
  • 项目类别:

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