Abstract
Magnetic resonance spectroscopic imaging (SI) is a unique imaging technique that provides biochemical information from in vivo tissues. The 1H spectra acquired from several spatial regions are quantified to yield metabolite concentrations reflective of tissue metabolism. However, since these metabolites are found in tissues at very low concentrations, SI is often acquired with limited spatial resolution. In this work, we test the hypothesis that deep learning is able to upscale low resolution SI, together with the T1-weighted (T1w) image, to reconstruct high resolution SI. We report on a novel densely connected UNet (D-UNet) architecture capable of producing super-resolution spectroscopic images. The inputs for the D-UNet are the T1w image and the low resolution SI image while the output is the high resolution SI. The results of the D-UNet are compared both qualitatively and quantitatively to simulated and in vivo high resolution SI. It is found that this deep learning approach can produce high quality spectroscopic images and reconstruct entire 1H spectra from low resolution acquisitions, which can greatly advance the current SI workflow.
Highlights
Magnetic resonance imaging (MRI) continues to be a versatile modality capable of providing anatomical, metabolic, and functional information from various regions of the body in vivo
We report a novel work on the development of a deep learning technology capable of producing super-resolution spectroscopic images
It is clear that the spectroscopic imaging (SI) generator is capable of producing a wide variety of SI images that mimic biochemicals that are more prominent in gray matter (GM), more prominent in white matter (WM), or prominent in both tissue types
Summary
Magnetic resonance imaging (MRI) continues to be a versatile modality capable of providing anatomical, metabolic, and functional information from various regions of the body in vivo. Magnetic resonance spectroscopic imaging (SI) [1] is able to yield important data regarding the metabolism of different tissues, and has been especially useful for studying the metabolism of the human brain [2]. Each metabolite plays an important role in regulating energy consumption in the brain, and some metabolites play critical functional roles, including roles as neurotransmitters [4]. It is well-known that metabolic changes occur in parallel with anatomical changes for a myriad of pathologies [2], and these metabolic changes may even occur before structural changes are detected. While SI has continued to be an active area of research over the past several decades, there are still major roadblocks into standardizing this technique and including it into clinical protocols
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