Deep Learning-Based MR Image Re-parameterization
Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis; however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the non-linearities that govern the re-parameterization.
Code (1)
Tasks
Deep LearningSimilar Papers 제목 키워드 기반
Differentiable Image Parameterizations
Typically, we parameterize the input image as the RGB values of each pixel, but that isn’t the only way. As long as the mapping from parameters to images is differentiable, we can still optimize alternative parameterizat…
Image GenerationEfficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge to select the reparameterization operat…
Neural Architecture SearchLearning Surface Parameterization for Document Image Unwarping
In this paper, we present a novel approach to learn texture mapping for a 3D surface and apply it to document image unwarping. We propose an efficient method to learn surface parameterization by learning a continuous bij…
3D Scene ReconstructionSpectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when c…
Deep Learningimage-classificationImage ClassificationImage Generation+1StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of parameterization. We prove that state-space models without any reparameterization exhibit a me…
State Space Models