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Related Experiment Video

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Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Hamza Kebiri1, Ali Gholipour2, Rizhong Lin3

  • 1CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.

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A new deep learning method enables accurate brain white matter fiber analysis using minimal diffusion-weighted imaging (dWI) measurements. This approach significantly improves fiber orientation distribution function (FOD) estimation in newborns and fetuses, overcoming previous data acquisition limitations.

Keywords:
Brain microstructureDeep learningFetusesFiber orientation distributionNewborns

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Computational Neuroscience

Background:

  • Diffusion-weighted magnetic resonance imaging (dMRI) is crucial for evaluating brain white matter structure.
  • Estimating fiber orientation distribution functions (FODs) accurately typically requires extensive dMRI measurements, posing challenges for vulnerable populations like newborns and fetuses due to limited acquisition times.
  • Existing standard and deep learning methods struggle with limited dMRI data, particularly in developing brains.

Purpose of the Study:

  • To develop and validate a novel deep learning approach for computing FODs from a reduced number of dMRI measurements.
  • To overcome the limitations of standard FOD computation methods in scenarios with scarce dMRI data, such as in neonatal and fetal imaging.
  • To demonstrate the generalizability and accuracy of the proposed deep learning method across different scanners, protocols, and anatomical structures.

Main Methods:

  • A deep learning model was trained to map a minimal set of six dMRI measurements to target FODs, using high angular resolution data as the ground truth.
  • The method's performance was quantitatively evaluated against standard techniques like Constrained Spherical Deconvolution and other deep learning approaches.
  • Generalizability was assessed using two independent clinical datasets of newborns and fetuses, with fetal FODs validated against post-mortem histological data.

Main Results:

  • The deep learning method achieved comparable or superior results to existing methods, despite using significantly fewer dMRI measurements.
  • For single and double fiber voxels, the method demonstrated higher agreement rates (77.5% and 22.2%) and lower angular errors (10° and 20°) compared to other deep learning techniques.
  • The model showed robust performance across different scanners, acquisition protocols, and anatomical variations, successfully estimating fetal FODs for the first time using dMRI.

Conclusions:

  • Deep learning offers a powerful solution for accurate fiber orientation density computation in developing brains using limited in-vivo dMRI data.
  • The proposed method significantly enhances the feasibility of advanced white matter tractography in neonates and fetuses.
  • Despite advancements, inherent limitations of dMRI in probing developing brain microstructure persist, warranting further research.