Publication

Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis

Jean-Philippe Thiran
2024

Abstract

Motivation: The infeasibility to collect large datasets of high-resolution, fully-sampled 7T data hinders the training of deep learning reconstructions for accelerated high-resolution 7T scans. Goal(s): Demonstrate that combining pretraining on fully-sampled scans from 1.5/3T and self-supervised finetuning on a few undersampled 7T scans enables robust reconstruction. Approach: High-resolution(0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients are used for finetuning/testing. Results: Deep learning reconstructions from one 7T scan have higher apparent SNR than a median of GRAPPA reconstructions from three scans, currently used for assessment, with similar tissue contrast and lesion conspicuity, potentially reducing scan time by a factor of three. Impact: Very high-resolution 3D scans can require infeasible acquisition time to generate images suitable for clinical use. Our work shows the potential for deep learning reconstructions to reduce scan time by a factor of three, without fully-sampled 7T data.

Official source

https://infoscience.epfl.ch/entities/publication/149ddc3e-ecef-483f-8c41-464bde1e8594

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Jean-Philippe Thiran
2024

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/149ddc3e-ecef-483f-8c41-464bde1e8594

About this result

Related concepts (32)

CT scan

A computed tomography scan (usually abbreviated to CT scan; formerly called computed axial tomography scan or CAT scan) is a medical imaging technique used to obtain detailed internal images of the body. The personnel that perform CT scans are called radiographers or radiology technologists. CT scanners use a rotating X-ray tube and a row of detectors placed in a gantry to measure X-ray attenuations by different tissues inside the body.

Unsupervised learning

Unsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

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Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis

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