PEAR: PEriodic And fixed Rank separation for fast fMRI

In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements. We decompose the fMRI signal into a component which a has fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI. Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real data). PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods

First measurements of radon-220 diffusion in mice tumors, towards treatment planning in diffusing alpha-emitters radiation therapy

Alpha-DaRT is a new method for treating solid tumors with alpha particles, relying on the release of the alpha-emitting daughter atoms of radium-224 from sources inserted into the tumor. The most important model parameters for Alpha-DaRT dosimetry are the diffusion lengths of radon-220 and lead-212, and their estimation is essential for treatment planning. The aim of this work is to provide first experimental estimates for the diffusion length of radon-220. The diffusion length of radon-220 was estimated from autoradiography measurements of histological sections taken from 24 mice-borne subcutaneous tumors of five different types. Experiments were done in two sets: fourteen in-vivo tumors, where during the treatment the tumors were still carried by the mice with active blood supply, and ten ex-vivo tumors, where the tumors were excised before source insertion and kept in a medium at 37 degrees C with the source inside. The measured diffusion lengths of radon-220 lie in the range 0.25-0.6 mm, with no significant difference between the average values measured in in-vivo and ex-vivo tumors: 0.40 $\pm$ 0.08 mm for in-vivo vs. 0.39 $\pm$ 0.07 mm for ex-vivo. However, in-vivo tumors display an enhanced spread of activity 2-3 mm away from the source. This effect is not explained by the current model and is much less pronounced in ex-vivo tumors. The average measured radon-220 diffusion lengths in both in-vivo and ex-vivo tumors lie close to the upper limit of the previously estimated range of 0.2-0.4 mm. The observation that close to the source there is no apparent difference between in-vivo and ex-vivo tumors, and the good agreement with the theoretical model in this region suggest that the spread of radon-220 is predominantly diffusive in this region. The departure from the model prediction in in-vivo tumors at large radial distances may hint at potential vascular contribution

Appendix

Table.1a. Patient’s Demographic and Clinical Characteristics. Table.1b. Screening Criteria. Fig.1. CONSORT Flow Diagram. Fig.2. ROIs Constructing The Chronic Pain Matrix Used For Graph Theory Analysis. Fig.3. Adjacency Matrix Threshol

Joint Multicontrast MRI Reconstruction

Joint reconstruction is relevant for a variety of medical imaging applications, where multiple images are acquired in parallel or within a single scanning procedure. Examples include joint reconstruction of different medical imaging modalities (e.g. CT and PET) and various MRI applications (e.g. different MR imaging contrasts of the same patient). In this paper we present an approach for joint reconstruction of two MR images, based on partial sampling of both. We assume each MR image has a limited number of edges, that is, low total variation, but they are similar in the sense that many of the edges overlap. We examine synthetic phantoms representing T1 and T2 imaging contrasts and realistic T1-weighted and T2-weighted images of the same patient. We show that our joint reconstruction approach outperforms conventional TV-based MRI reconstruction for each image solely. Results are shown both visually and numerically for sampling ratios of 4%-20%, and consist of an improvement of up to 3.6dB