Super-resolution MRI Using Finite Rate of Innovation Curves

We propose a two-stage algorithm for the super-resolution of MR images from their low-frequency k-space samples. In the first stage we estimate a resolution-independent mask whose zeros represent the edges of the image. This builds off recent work extending the theory of sampling signals of finite rate of innovation (FRI) to two-dimensional curves. We enable its application to MRI by proposing extensions of the signal models allowed by FRI theory, and by developing a more robust and efficient means to determine the edge mask. In the second stage of the scheme, we recover the super-resolved MR image using the discretized edge mask as an image prior. We evaluate our scheme on simulated single-coil MR data obtained from analytical phantoms, and compare against total variation reconstructions. Our experiments show improved performance in both noiseless and noisy settings.Comment: Conference paper accepted to ISBI 2015. 4 pages, 2 figure

Motion Compensation for Free-Breathing Abdominal Diffusion-Weighted Imaging (MoCo DWI)

Diffusion-weighted imaging (DWI) is a common technique in medical diagnostics. One challenge of thoracic and abdominal DWI is respiratory motion which can result in motion artifacts. To eliminate these artifacts, a new kind of retrospective, respiratory motion compensation for DWI was developed and tested. This new technique — MoCo DWI — is the first in DWI which provides fully-deformable motion compensation. To enable this, despite the low image quality of DWI, two free-breathing sequences were used: (1) a gradient echo sequence (GRE) with a configuration for optimal respiratory motion estimation and (2) a DWI in a configuration of clinical interest. The DWI acquisition was gated into 10 motion phases. Each motion phase was then co-aligned with the motion estimation. The implementation was tested with eleven volunteers. The results showed that MoCo DWI can reduce motion blurring in single b-value images, especially at the liver-lung interface. The improvement of ADC-maps was even more prominent. Individual slices showed motion induced artifacts which could be reduced or even eliminated by MoCo DWI. This was also reflected by expected more homogeneous ADC values in the liver in all data sets. These results promise to reduce measurements with limited diagnostic value while keeping or increasing patient comfort

A New Compressive Video Sensing Framework for Mobile Broadcast

A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise discrete cosine transform coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast