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EP-4737933-A1 - QUANTITATIVE MATERIAL CHARACTERIZATION OF AN OBJECT WITH MAGNETIC RESONANCE IMAGING

EP4737933A1EP 4737933 A1EP4737933 A1EP 4737933A1EP-4737933-A1

Abstract

For material characterization, first and second measured MRI data (20, 23, 26) representing an object (6) and corresponding first and second sequence descriptions (21, 24, 27) are received. For each iteration, first and second simulated MRI data (22, 25, 28) is generated according to the sequence descriptions (21, 24, 27) based on a model (29) comprising model values for at least one material parameter. An error (31) is determined, which depends on a deviation of the simulated MRI data (22, 25, 28) from the measured MRI data (20, 23, 26). The model (29) is adapted depending on the error (31). For an initial iteration, the model (29) corresponds to an initial model (29) and otherwise it corresponds to the adapted model (29) of the preceding iteration. The quantitative material characterization is determined depending on the adapted model (29) of a final iteration of the two or more iterations.

Inventors

  • Endres, Jonathan
  • NICKEL, MARCEL DOMINIK
  • Pfaff, Laura
  • Weinmüller, Simon
  • Würfl, Tobias
  • ZAISS, MORITZ

Assignees

  • Siemens Healthineers AG
  • Friedrich-Alexander-Universität Erlangen-Nürnberg

Dates

Publication Date
20260506
Application Date
20241104

Claims (15)

  1. Computer-implemented method for determining a quantitative material characterization of an object (6), wherein - first measured magnetic resonance imaging, MRI, data (20, 23, 26) representing the object (6) is received and a first sequence description (21, 24, 27) specifying a first acquisition sequence used for generating the first measured MRI data (20, 23, 26) is received; - second measured MRI data (20, 23, 26) representing the object (6) is received and a second sequence description (21, 24, 27) specifying a second acquisition sequence used for generating the second measured MRI data (20, 23, 26) is received; - for each iteration of two or more iterations i) first simulated MRI data (22, 25, 28) is generated by simulating an MRI acquisition according to the first sequence description (21, 24, 27) based on a model (29) for the object (6) comprising respective model values for at least one material parameter of the object (6); ii) second simulated MRI data (22, 25, 28) is generated by simulating an MRI acquisition according to the second sequence description (21, 24, 27) based on the model (29); iii) an error (31) is determined, which depends on a deviation of the first simulated MRI data (22, 25, 28) from the first measured MRI data (20, 23, 26) and on a deviation of the second simulated MRI data (22, 25, 28) from the second measured MRI data (20, 23, 26); and iv) the model (29) is adapted by adapting the model values depending on the error (31); - if the respective iteration corresponds to an initial iteration of the two or more iterations, the model (29) of the respective iteration corresponds to an initial model (29) for the object (6) comprising respective predefined initial model values for the at least one material parameter, and otherwise the model (29) of the respective iteration corresponds to the adapted model (29) of a respective preceding iteration; and - the quantitative material characterization is determined depending on the adapted model (29) of a final iteration of the two or more iterations.
  2. Computer-implemented method according to claim 1, wherein a type of the first acquisition sequence differs from a type of the second acquisition sequence.
  3. Computer-implemented method according to claim 2, wherein - the type of the first acquisition sequence corresponds to a first T1-weighted MRI acquisition, a first T2-weighted MRI acquisition, a first proton density, PD, weighted MRI acquisition, a first fluid attenuated inversion recovery, FLAIR, MRI acquisition or a first diffusion weighted MRI acquisition; and/or - the type of the second acquisition sequence corresponds to a second T1-weighted MRI acquisition, a second T2-weighted MRI acquisition, a second PD-weighted MRI acquisition, a second FLAIR MRI acquisition or a second diffusion weighted MRI acquisition.
  4. Computer-implemented method according to one of the preceding claims, wherein - the first sequence description (21, 24, 27) specifies a first echo time and the second sequence description (21, 24, 27) specifies a second echo time, which is different from the first echo time; and/or - the first sequence description (21, 24, 27) specifies a first repetition time and the second sequence description (21, 24, 27) specifies a second repetition time, which is different from the first repetition time.
  5. Computer-implemented method according to one of the preceding claims, wherein the model (29) for the object (6) comprises a respective spatially resolved parameter map for each material parameter of the at least one material parameter.
  6. Computer-implemented method according to one of the preceding claims, wherein the at least one material parameter comprises a T1-relaxation time and/or a T2-relaxation time and/or a T2*-relaxation time and/or a proton density and/or an apparent diffusion coefficient.
  7. Computer-implemented method according to one of the preceding claims, wherein in step i), a differentiable MRI simulator (30) is applied to the model (29) of the respective iteration and the first sequence description (21, 24, 27) in order to generate the respective first simulated MRI data (22, 25, 28) and the differentiable MRI simulator (30) is applied to the model (29) of the respective iteration and the second sequence description (21, 24, 27) in order to generate the respective second simulated MRI data (22, 25, 28).
  8. Computer-implemented method according to one of the preceding claims, wherein in step iii), the respective error (31) is computed depending on a mean squared error of the deviation of the first simulated MRI data (22, 25, 28) from the first measured MRI data (20, 23, 26) and depending on a mean squared error of the deviation of the second simulated MRI data (22, 25, 28) from the second measured MRI data (20, 23, 26).
  9. Computer-implemented method according to one of the preceding claims, wherein the two or more iterations are terminated after the final iteration of the two or more iterations, wherein the error (31) of the final iteration is equal to or less than a predefined maximum error.
  10. Computer-implemented method according to one of the preceding claims, wherein the first measured MRI data (20, 23, 26) and the second measured MRI data (20, 23, 26) are respective raw data in k-space.
  11. Computer-implemented method according to one of the preceding claims, wherein - third measured MRI data (20, 23, 26) representing the object (6) is received and a third sequence description (21, 24, 27) specifying a third acquisition sequence used for generating the third measured MRI data (20, 23, 26) is received; - for each iteration of the two or more iterations, third simulated MRI data (22, 25, 28) is generated by simulating an MRI acquisition according to the third sequence description (21, 24, 27) based on the model (29) of the respective iteration and the error (31) is determined depending on a deviation of the third simulated MRI data (22, 25, 28) from the third measured MRI data (20, 23, 26).
  12. Method for quantitative MRI, wherein - first measured MRI data (20, 23, 26) representing an object (6) is generated by an MRI device according to a predefined first acquisition sequence and second measured MRI data (20, 23, 26) representing the object (6) is generated by the MRI device according to a predefined second acquisition sequence; and - a computer-implemented method according to one of the preceding claims is carried out.
  13. Data processing system (14), which is configured to carry out a computer-implemented method according to one of claims 1 to 11.
  14. MRI system (1) comprising a data processing system (14) according to claim 13 and an MRI device, which is configured to generate the first measured MRI data (20, 23, 26) according to the first acquisition sequence and the second measured MRI data (20, 23, 26) according to the second acquisition sequence.
  15. Computer program product comprising - first instructions, which, when carried out by a data processing system (14), cause the data processing system (14) to carry out a computer-implemented method according to one of claims 1 to 11; and/or - second instructions, which, when carried out by an MRI system (1) according to claim 14, cause the MRI system (1) to carry out a method according to claim 12.

Description

The present invention is directed to a computer-implemented method for determining a quantitative material characterization of an object, wherein first measured magnetic resonance imaging, MRI, data representing the object is received. The invention is further directed to a data processing system for carrying out said computer-implemented method, to a corresponding method for quantitative MRI, to an MRI system comprising said data processing system, and to corresponding computer program products. Quantitative MRI, qMRI, plays an important role in medical imaging by providing quantitative, objective measurements of tissue properties, such as relaxation times or proton density. Unlike conventional MRI, which produces images with qualitative contrasts only, qMRI aims to derive material parameters that have direct relevance to the underlying tissue composition and microstructure. This shift towards quantification enhances the precision and reproducibility of MRI examinations and facilitates comparisons across different subjects and scanners. Various approaches have been suggested for the quantification of tissue parameters. However, most of them require excessive scan time, which poses challenges for their practical implementation in clinical settings and provides limited reproducibility. In acquisition processes, parametric mapping frequently involves a delicate balance between precision and accuracy versus the time required for measurement. Precise and accurate quantification often relies on "clean" acquisitions of multiple contrasts that only depend on few parameters. These parameters are later determined by fitting the different contrasts to a signal model. Prime examples include inversion or saturation recovery measurements for determining T1 and T2, which are often considered as gold standard. Accelerated acquisition methods frequently require trade-offs in contrast accuracy. For instance, variable flip angle, vFA, T1 mapping often relies on less precise steady states to expedite the process. Magnetic resonance fingerprinting, MRF, is an alternative approach in qMRI, which simultaneously acquires multiple parameters in a single scan. Instead of isolating specific sequences for individual measurements, MRF aims to generate a "fingerprint" of tissue properties, allowing for more efficient and comprehensive quantification. The underlying idea is based on a complete Bloch simulation of the applied acquisition sequence diagram for an imaged voxel. A significant compromise in MRF lies in the calculation of the signal evolution in a given voxel from the acquired k-space data. Since many samples for the signal evolution are required and a complete optimization of the whole volume is intractable with conventional reconstruction methods, typically simple regridding reconstructions are done with high undersampling. The method relies on the hope that corresponding undersampling artifacts average out in the voxel-wise dictionary matching. Moreover, establishing standardized dictionaries and protocols across different scanners and sites is a challenge for widespread clinical adoption. MRI simulators are software tools designed to replicate the processes and outcomes of MRI without the need for an actual MRI scanner. They work by using a model for an object and simulations to emulate the physical principles involved in MRI. Some simulators are based on solving the Bloch equations directly, as for example described in the publication of H. Benoit-Cattin et al.: "The SIMRI project: a versatile and interactive MRI simulator.", Journal of Magnetic Resonance, 173(1), 97-115. Others rely on the extended phase graph algorithm, as explained in S. Rakshit et al.: "GPU-accelerated extended phase graph algorithm for differentiable optimization and learning." Proc. Intl. Soc. Mag. Reson. Med. 29 (2021), available at https://somnathrakshit.github.io/projects/ project-mri-sim-py-epg/3754.html (retrieved October 11, 2024). MRzero is a comprehensive framework that emulates an MRI pipeline, encompassing sequence and phantom definition, signal simulation, and image reconstruction, as described in A. Loktyushin et al.: "MRzero - Automated discovery of MRI sequences using supervised learning.", Magnetic Resonance in Medicine, 86: 709-724 and H. Dang et al.: "MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2-induced blurring in spin echo sequences.", Magnetic Resonance in Medicine, 90(4): 1345-1362. At its core, MRzero incorporates a phase distribution graph, PDG, simulator inspired by the EPG concept, as described in J. Endres et al.: "Phase distribution graphs for fast, differentiable, and spatially encoded Bloch simulations of arbitrary MRI sequences." Magnetic Resonance in Medicine, 92, 10. Sequence descriptions for MRI acquisition sequences can, for example, be provided in the form of files following the pulseq standard, as described in K.J. Layton et al.: "Pulseq: A rapid and hardw