Guiding protocol development for magnetic resonance thermometry
Inventors
Majeed, Waqas • Patil, Sunil Goraksha • Schneider, Rainer • Bhat, Himanshu • Campbell, Adrienne
Assignees
Siemens Healthineers AG • US Department of Health and Human Services
Publication Number
US-11176717-B2
Publication Date
2021-11-16
Expiration Date
2039-09-26
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Abstract
A method for decomposing noise into white and spatially correlated components during MR thermometry imaging includes acquiring a series of MR images of an anatomical object and generating a series of temperature difference maps of the anatomical object. The method further includes receiving a selection of a region of interest (ROI) within the temperature difference map and estimating total noise variance values depicting total noise variance in the temperature difference map. Each total noise variance value is determined using a random sampling of a pre-determined number of voxels from the ROI. A white noise component and a spatially correlated noise component of the total noise variance providing a best fit to the total noise variance values for all of the random samplings are identified. The white noise component and the spatially correlated noise component are displayed on a user interface.
Core Innovation
The invention provides methods, systems, and apparatuses for guiding protocol development for Magnetic Resonance (MR) Thermometry, specifically addressing challenges in optimizing MR thermometry imaging/post-processing pipelines. It decomposes total noise in MR thermometry temperature difference maps into constituent white noise and spatially correlated noise components, facilitating targeted strategies to minimize noise.
The method involves acquiring MR images of an anatomical object, generating temperature difference maps, and selecting regions of interest (ROI) within these maps. Through random sampling of voxels within the ROI and fitting predetermined relationships, the white noise and spatially correlated noise components of total noise variance are estimated and displayed via a user interface. This decomposition provides insight into the sources of noise and guides optimization.
Further, the invention enables visualization of effects from changing imaging parameters on total noise, predicts updated noise maps based on new parameter sets including echo time and tissue-specific relaxation values, and supports automatic determination of optimal imaging parameter sets under user-specified constraints by evaluating white noise variance components. These techniques aid in customizing and refining MR thermometry protocols without repeated experiments, thereby enhancing accuracy and efficiency.
Claims Coverage
The patent includes one independent claim focusing on a method for decomposing noise components during MR thermometry imaging with additional dependent claims elaborating features related to user interface interactions, parameter adjustments, and tissue-specific inputs.
Method for decomposing noise into white and spatially correlated components during MR thermometry imaging
A method comprising acquisition of MR images of an anatomical object; generation of temperature difference maps with voxel temperature values; receiving user selection of a region of interest (ROI) within the maps; estimating total noise variances via multiple random samplings of distinct predetermined voxel counts from ROI; identifying white noise and spatially correlated noise components fitting the total variance values based on a predetermined relationship; and displaying these noise components on a user interface.
Dynamic update of noise components based on modified imaging parameters
Upon receiving user selection of a second set of imaging parameters differing from the first set, generating updated signal-to-noise values per voxel; computing updated white noise components of total noise variance using relative signal-to-noise ratio changes and updated echo times; and displaying updated white noise components via the user interface.
User input and retrieval of tissue-specific relaxation parameters for noise estimation
Incorporating T1 and T2* tissue relaxation values associated with the anatomical object either through user selection of tissue type from a drop-down menu to retrieve values from a database or direct user input into the interface, to refine noise component estimation in MR thermometry.
The claims cover a comprehensive approach to decomposing noise into white and spatially correlated components in MR thermometry, facilitating visualization and dynamic updating of noise characteristics with imaging parameter changes, and incorporating tissue-specific parameters to optimize protocol development and noise reduction strategies.
Stated Advantages
Provides insights into noise sources by separating total noise variance into white and spatially correlated components, helping users identify root causes of high variance.
Enables optimization of thermometry imaging protocols without repeated experiments by predicting effects of parameter modifications on noise variance.
Allows automatic determination and comparison of optimal imaging parameter sets under user-specified constraints, improving accuracy and efficiency of MR thermometry.
Documented Applications
Guiding protocol development for MR thermometry in thermal therapy applications such as High Intensity Focused Ultrasound (HIFU) and laser-based heating of anatomical tissue.
Monitoring thermal therapies including radiofrequency, microwave, laser and MR-guided focused ultrasound for treatment of cancers and neurological disorders like essential tremor and Parkinson's disease.
Optimizing imaging/post-processing pipelines in proton resonance frequency (PRF) thermometry to ensure delivery of desired thermal dose to target tissue while minimizing damage to normal tissue.
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