Correlation between k‐space sampling pattern and MTF in compressed sensing MRSI. Issue 10 (23rd September 2016)

• Record Type: Journal Article
• Authors: Heikal, A. A.; Wachowicz, K.; Fallone, B. G.
• Is Part Of: Medical physics. Volume 43:Issue 10(2016)
• Page Start: 5626
• Page End: 5634
• Publication Date: 2016-09-23
• Subjects: biomedical MRI -- compressed sensing -- image reconstruction -- medical image processing -- probability; Magnetic resonance imaging; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Image data processing or generation, in general -- Digital computing or data processing equipment or methods, specially adapted for specific applications; compressed sensing -- MR spectroscopic imaging -- MTF -- sampling -- reconstruction; Modulation transfer functions -- Magnetic resonance imaging -- Probability density functions -- Image reconstruction -- Medical image reconstruction -- Signal generators -- Medical image noise -- Medical image artifacts; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4962930
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To investigate the relationship between the k ‐space sampling patterns used for compressed sensing MR spectroscopic imaging (CS‐MRSI) and the modulation transfer function (MTF) of the metabolite maps. This relationship may allow the desired frequency content of the metabolite maps to be quantitatively tailored when designing an undersampling pattern. Methods: Simulations of a phantom were used to calculate the MTF of Nyquist sampled (NS) 32 × 32 MRSI, and four‐times undersampled CS‐MRSI reconstructions. The dependence of the CS‐MTF on the k ‐space sampling pattern was evaluated for three sets of k ‐space sampling patterns generated using different probability distribution functions (PDFs). CS‐MTFs were also evaluated for three more sets of patterns generated using a modified algorithm where the sampling ratios are constrained to adhere to PDFs. Results: Strong visual correlation as well as high R 2 was found between the MTF of CS‐MRSI and the product of the frequency‐dependant sampling ratio and the NS 32 × 32 MTF. Also, PDF‐constrained sampling patterns led to higher reproducibility of the CS‐MTF, and stronger correlations to the above‐mentioned product. Conclusions: The relationship established in this work provides the user with a theoretical solution for the MTF of CS MRSI that is both predictable and customizable to the user's needs.