Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 2; pp. 388 - 399
• Main Authors: Kofler, Andreas, Kerkering, Kirsten Miriam, Goschel, Laura, Fillmer, Ariane, Kolbitsch, Christoph
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> T_{1}</tex-math> </inline-formula> </named-content>-mapping; Algorithms; Alzheimer's disease; Biomedical measurement; Brain; Brain - diagnostic imaging; Brain mapping; Compressed sensing; Dictionaries; dictionary learning; Encoding; Humans; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Learning; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Matching pursuit algorithms; Medical imaging; Methods; Neural coding; Neurodegenerative diseases; Neuroimaging; Parameters; quantitative imaging; Sparsity; Supervised learning
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2023.3300090

Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a <inline-formula><tex-math notation="LaTeX">T_{1}</tex-math></inline-formula>-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). Results: Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. Conclusion: The proposed method outperforms the reported methods of comparison and yields accurate <inline-formula><tex-math notation="LaTeX">T_{1}</tex-math></inline-formula>-maps. Although presented for <inline-formula><tex-math notation="LaTeX">T_{1}</tex-math></inline-formula>-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. Significance: From a clinical perspective, the obtained <inline-formula><tex-math notation="LaTeX">T_{1}</tex-math></inline-formula>-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.