Academic Journal
Optimization of Diffusion MRI With Consideration of the Signal Decay in Biological Tissues.
| Τίτλος: | Optimization of Diffusion MRI With Consideration of the Signal Decay in Biological Tissues. |
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| Συγγραφείς: | Kuczera S; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden., Maier SE; Department of Radiology, Brigham Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. |
| Πηγή: | Magnetic resonance in medicine [Magn Reson Med] 2026 Jul; Vol. 96 (1), pp. 460-468. Date of Electronic Publication: 2026 Mar 19. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1999- : New York, NY : Wiley Original Publication: San Diego : Academic Press |
| Ιατρικοί όροι (MeSH): | Diffusion Magnetic Resonance Imaging*/methods , Image Enhancement*/methods , Image Interpretation, Computer-Assisted*/methods , Prostate*/anatomy & histology , Signal Processing, Computer-Assisted*, Algorithms ; Reproducibility of Results ; Phantoms, Imaging ; Humans ; Sensitivity and Specificity ; Male ; Signal-To-Noise Ratio ; Computer Simulation |
| Περίληψη: | Purpose: Devising methodology to characterize and optimize acquisition schemes for biological tissues, such as the prostate, that produce model-based synthetic diffusion-weighted images and derived model parameters with predictable SNR improvement. Methods: The averaging effect (AE) in synthetic diffusion-weighted images obtained through fitting of various signal decay models to signals measured over 21 linearly spaced b-values between 0 and INLINEMATH is determined with analytic expressions. Similarly, the standard deviation of a retrospective 2-point ADC fit based on the synthetic data is analyzed. Furthermore, acquisition schemes that achieve constant SNR or constant AE for synthetic images over the same b-value range are devised by means of numerical optimization using either a custom iterative method or a standard function optimizer. These acquisition schemes are verified by measurements on a phantom with a non-monoexponential diffusion signal decay combined with a bootstrapping approach to increase the number of data samples. Results: The dependence of AE on model function and parameters is complex. Repeated measurements at specific b-values can boost AE locally, while improvements in ADC uncertainty are particularly pronounced for repetitions of the higher b-value. Optimization of acquisition schemes generally results in discrete b-values, whereby the number of b-values corresponds to the number of model parameters. Results from phantom measurements are in agreement with the theoretical predictions. Conclusion: The presented analytical calculations and numerical optimizations can be useful to improve acquisition schemes under various experimental conditions and clinical needs. (© 2026 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.) |
| References: | D. K. Jones, “The Effect of Gradient Sampling Schemes on Measures Derived From Diffusion Tensor MRI: A Monte Carlo Study,” Magnetic Resonance in Medicine 51, no. 4 (2004): 807–815. S. Kuczera, F. Langkilde, and S. E. Maier, “Truly Reproducible Uniform Estimation of the ADC With Multi‐b Diffusion Data— Application in Prostate Diffusion Imaging,” Magnetic Resonance in Medicine 89, no. 4 (2023): 1586–1600. P. H. Richter, “Estimating Errors in Least‐Squares Fitting,” Telecommunications and Data Acquisition Report 42–122 (1995): 107–137. D. Le Bihan, E. Breton, D. Lallemand, M. L. Aubin, J. Vignaud, and M. Laval‐Jeantet, “Separation of Diffusion and Perfusion in Intravoxel Incoherent Motion MR Imaging,” Radiology 168, no. 2 (1988): 497–505. F. Langkilde, T. Kobus, A. Fedorov, et al., “Evaluation of Fitting Models for Prostate Tissue Characterization Using Extended‐Range b‐Factor Diffusion‐Weighted Imaging,” Magnetic Resonance in Medicine 79, no. 4 (2018): 2346–2358. D. Malyarenko, T. Chenevert, S. Ono, T. Lynch, and S. Swanson, Temperature and Concentration Dependence of Diffusion Kurtosis Parameters in a Quantitative Phantom, vol. 2429 (ISMRM, 2022). D. Malyarenko, S. Ono, T. J. E. Lynch, and S. D. Swanson, “Technical Note: Hydrogel‐Based Mimics of Prostate Cancer With Matched Relaxation, Diffusion and Kurtosis for Validating Multi‐Parametric MRI,” Medical Physics 51, no. 5 (2024): 3590–3596. S. Kuczera, M. Alipoor, F. Langkilde, and S. E. Maier, “Optimized Bias and Signal Inference in Diffusion‐Weighted Image Analysis (OBSIDIAN),” Magnetic Resonance in Medicine 86, no. 5 (2021): 2716–2732. S. O. Rice, “Statistical Properties of a Sine Wave Plus Random Noise,” Bell System Technical Journal 27, no. 1 (1948): 109–157. B. Gürses, N. Kabakci, A. Kovanlikaya, et al., “Diffusion Tensor Imaging of the Normal Prostate at 3 Tesla,” European Radiology 18 (2008): 716–721. K. Malshy, A. Ochsner, R. Ortiz, et al., “Comparison of the Incidence of Clinically Significant Prostate Cancer in Patients With Isolated Peripheral Versus Transitional Zone PIRADS 3 Lesions,” Urologia 92 (2025): 51–58. J. Veraart, D. S. Novikov, D. Christiaens, B. Ades‐aron, J. Sijbers, and E. Fieremans, “Denoising of Diffusion MRI Using Random Matrix Theory,” NeuroImage 142 (2016): 394–406. H. Cramer, Mathematical Methods of Statistics (Princton University Press, 1922). |
| Grant Information: | P41EB028741 United States NH NIH HHS; ALFGBG 932648 Swedish Governmental Funding of Clinical Research (ALF); R01 CA241817 United States CA NCI NIH HHS; Cancerfonden; Vetenskapsrådet; Barncancerfonden; R01CA241817 United States NH NIH HHS; P41 EB028741 United States EB NIBIB NIH HHS |
| Contributed Indexing: | Keywords: diffusion MRI; noise; optimization; prostate |
| Entry Date(s): | Date Created: 20260320 Date Completed: 20260711 Latest Revision: 20260711 |
| Update Code: | 20260711 |
| PubMed Central ID: | PMC13112234 |
| DOI: | 10.1002/mrm.70346 |
| PMID: | 41857486 |
| Βάση Δεδομένων: | MEDLINE |
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