Meeting Banner
Abstract #3083

A Manifold Learning-based Approach for Denoising in Deuterium Metabolic Imaging

Didi Chi1, Paul K. Han1, Henk M. De Feyter1, Robin A. de Graaf1, and Chao Ma1
1Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States

Synopsis

Keywords: Deuterium, Deuterium, Data processing, Simulation/validation, Spectroscopy

Motivation: Conventional deuterium metabolic imaging (DMI) suffers from a low signal-to-noise ratio (SNR), which imposes challenges such as long acquisition time, low spatial resolution, or poor accuracy in metabolite concentration estimation.

Goal(s): Our goal is to improve the SNR of the DMI signals and thus enhance the estimation of metabolite concentration.

Approach: A manifold learning-based method, Linear Tangent Space Alignment (LTSA) model, was proposed to denoise the DMI signal.

Results: Results from theoretical calculation, numerical simulation and in vivo study showed that the proposed method provided a reduction in noise, and in the variance of the metabolite concentration estimates.

Impact: The LTSA model reduces the noise in DMI signal and thus improves the estimation of metabolite concentration. This improvement prospectively allows DMI with high spatial-temporal resolution, which can assist tumor diagnosis and treatment response assessment in clinical settings.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords