Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Neuroscience

Background:

• Accurate quantification of white matter (WM) lesion load is critical for managing multiple sclerosis (MS).
• Accelerated imaging techniques are desirable to reduce patient burden and increase throughput.
• Deep learning-based reconstruction offers potential for image quality enhancement.

Purpose of the Study:

• To evaluate the efficacy of combining accelerated 3D FLAIR MRI with deep learning-based denoising for MS lesion quantification.
• To determine if this combined strategy can maintain diagnostic accuracy while reducing scan time.

Main Methods:

• Prospective examination of 28 MS patients using four accelerated 3D FLAIR sequences with varying scan times.
• Reconstruction of each sequence with and without deep learning-based denoising.
• Assessment of image quality using Likert scale, SNR, and CNR.
• Quantitative evaluation of manual and automatic lesion segmentation against ground truth using multiple metrics (e.g., Dice, Hausdorff).

Main Results:

• Accelerated FLAIR sequences showed image quality deterioration, but deep learning denoising significantly improved subjective and quantitative metrics.
• Denoising improved lesion detection, recovering contours and identifying lesions missed in standard accelerated scans.
• The 2-minute 35-second FLAIR with denoising achieved comparable lesion quantification to the reference 4-minute 54-second scan.

Conclusions:

• Deep learning-based denoising is a valuable strategy for recognizing MS lesions in accelerated FLAIR acquisitions.
• This approach can effectively shorten MRI scan times in clinical practice without compromising essential diagnostic information.
• The combination offers a promising method for efficient and accurate MS lesion load assessment.