# US 2013/0278261 A1 · Systems and methods for regularized reconstructions in mri using side information > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0278261 A1 - Document ID: US20130278261A1 - Status: Published application - Kind: A1 - Application number: 13/867,885 - Filed: 2013-04-22 - Published: 2013-10-24 - Earliest claimed: 2012-04-20 - Assignees: University of Virginia Licensing & Ventures Group - Inventors: Samuel W. Fielden; Craig H. Meyer - Web page: https://patentgrep.com/patent/US20130278261A1 ## CPC - G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)} - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) ## Abstract Systems and methods for magnetic resonance imaging (MRI) using side information for improved reconstruction are disclosed. In one aspect, in accordance with one example embodiment, images obtained using heteronuclei, such as Fluorine-19 (19F), can be reconstructed using one or more methods to provide improved resolution and detail. The system can use anatomical proton MRI scans. The system can use multiple smoothing techniques to improve the signal-to-noise ratio (SNR). The system can also use weighted smoothing using information from anatomical proton MRI scans to improve resolution. ## Claims ### Claim 1 A method comprising: obtaining a first magnetic resonance (MRI) image using hydrogen (1H); obtaining a second MRI image using a secondary nucleus other than hydrogen; and reconstructing the second image using side information from the first image. ### Claim 2 (depends on 1) The method of claim 1, wherein the secondary nucleus is Fluorine-19 (19F). ### Claim 3 (depends on 1) The method of claim 1, wherein the secondary nucleus is one or more selected from the group consisting of sodium (23Na), carbon (13C), phosphorus (31P), helium (3He), and xenon (129Xe). ### Claim 4 (depends on 1) The method of claim 1, wherein reconstructing the second image comprises: regularizing the image using a quadratic roughness penalty; wherein the quadratic roughness penalty comprises a differencing matrix and a plurality of weights. ### Claim 5 (depends on 4) The method of claim 4, wherein the plurality of weights are binary; wherein a first weight of the plurality of weights is set to 0 if the side information from the first image indicates an edge; and wherein a second weight of the plurality of weights is set to 1 if the side information from the first image does not indicate an edge. ### Claim 6 (depends on 4) The method of claim 4, wherein each weight in the plurality of weights varies continuously between 0 and 1 based on the side information from the first image. ### Claim 7 (depends on 6) The method of claim 6, wherein the higher the contrast in the side information of the first image, the closer a first weight of the plurality of weights is to 0; and wherein the lower the contrast in the side information of the first image, the closer a first weight of the plurality of weights is to 1. ### Claim 8 (depends on 1) The method of claim 1, wherein reconstructing the second image comprises: filtering the first image using a filter to obtain a plurality of calculated filter weights; and filtering the second image using the filter and the calculated filter weights from the first image. ### Claim 9 (depends on 8) The method of claim 8, wherein the filter comprises a non-local-means (NLM) filter. ### Claim 10 (depends on 1) The method of claim 1, further comprising including terms in the cost function of the image reconstruction to enforce sparsity. ### Claim 11 (depends on 10) The method of claim 10, wherein the sparsity is in the image domain. ### Claim 12 (depends on 10) The method of claim 10, wherein the sparsity is enforced using an L1 norm. ### Claim 13 A system, comprising: a magnetic resonance imaging (MRI) device; one or more processors; and at least one memory in communication with the MRI device, storing computer-readable instructions that, when executed by the one or more processors, cause the system to: obtain a first magnetic resonance (MRI) image using hydrogen (1H); obtain a second MRI image using a secondary nucleus other than hydrogen; and reconstruct the second image using side information from the first image. ### Claim 14 (depends on 13) The system of claim 13, wherein the secondary nucleus is Fluorine-19 (19F). ### Claim 15 (depends on 13) The system of claim 13, wherein the secondary nucleus is one or more selected from the group consisting of sodium (23Na), carbon (13C), phosphorus (31P), helium (3He), and xenon (129Xe). ### Claim 16 (depends on 13) The system of claim 13, wherein reconstructing the second image comprises: regularizing the image using a quadratic roughness penalty; wherein the quadratic roughness penalty comprises a differencing matrix and a plurality of weights. ### Claim 17 (depends on 16) The system of claim 16, wherein the plurality of weights are binary; wherein a first weight of the plurality of weights is set to 0 if the side information from the first image indicates an edge; and wherein a second weight of the plurality of weights is set to 1 if the side information from the first image does not indicate an edge. ### Claim 18 (depends on 16) The system of claim 16, wherein each weight in the plurality of weights varies continuously between 0 and 1 based on the side information from the first image. ### Claim 19 (depends on 18) The system of claim 18, wherein the higher the contrast in the side information of the first image, the closer a first weight of the plurality of weights is to 0; and wherein the lower the contrast in the side information of the first image, the closer a first weight of the plurality of weights is to 1. ### Claim 20 (depends on 13) The system of claim 13, wherein reconstructing the second image comprises: filtering the first image using a filter to obtain a plurality of calculated filter weights; and filtering the second image using the filter and the calculated filter weights from the first image. ### Claim 21 (depends on 20) The system of claim 20, wherein the filter comprises a non-local-means (NLM) filter. ### Claim 22 (depends on 13) The system of claim 13, further comprising including terms in the cost function of the image reconstruction to enforce sparsity. ### Claim 23 (depends on 22) The system of claim 22, wherein the sparsity is in the image domain. ### Claim 24 (depends on 22) The system of claim 22, wherein the sparsity is enforced using an L1 norm.