Featured Research
Mathews Jacob, Ph.D.
The major focus of Dr. Jacob's research is the computational aspects of biomedical imaging. The goal is to apply signal and image processing principles to improve the tradeoff between final image quality/accuracy of quantitative results and scan-time/cost. This involves fundamental developments in sampling theory, optimization theory etc and aids to enhance various practical applications such as functional imaging, cardiac MR imaging. Some of the problems they have worked on include:
Model Based Recognition in Magnetic Resonance Spectroscopic ImagingMagnetic resonance spectroscopic imaging (MRSI) is emerging as a promising tool for the early diagnosis of various diseases. However, the scans are time consuming since an extra dimension (spectral) has to be encoded. We propose a novel approach based on a compartmental model to reduce the number of spatial encodings. It fuses information from anatomical MR scans, the magnetic in-homogeneity map and the chemical shift encodings to reconstruct the spatial-spectral volume with reduced artifacts. See our paper for more information. |
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Levelset Reconstruction in Near-infrared Spectroscopic ImagingWe introduce a new algorithm for the reconstruction of functional brain activations from near infrared spectroscopic imaging (NIRSI) data. While NIRSI offers remarkable biochemical specificity, the attainable spatial resolution with this technique is rather limited. Our approach exploits the support-limited (spatially concentrated) nature of the activations to make the reconstruction problem well-posed. Numerical simulations and experimental data indicate a significant improvement in the quality (resolution and robustness to noise) over standard techniques such as truncated conjugate gradients (TCG) and simultaneous iterative reconstruction technique (SIRT) algorithms. Furthermore, results on experimental data obtained from simultaneous fMRI and optical measurements show much closer agreement of the optical reconstruction using the new approach with fMRI images than TCG and SIRT. See our paper for more information. |
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Optimal Sampling in Parallel MR ImagingParallel imaging is a powerful approach to accelerate the acquisition in most of the MR applications. However, in most cases the speedup factor is far lower than the number of coils. One of the main reasons is the redundancy in the measurements that will lead to an ill-posed reconstructions. We introduce a novel approach to improve the speed up factor by selecting the optimal phase encode lines. By this approach, we reduce the sensitivity of the algorithm to noise; we get much better reconstructions as compared to standard approaches like SPACE-RIP for a specified acceleration factor (2.66 in the images on the left; we used 4 coils). See our paper for more information. |
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