An iterative reduced field‐of‐view reconstruction for periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI. Issue 10 (11th September 2015)

• Record Type: Journal Article
• Authors: Lin, Jyh‐Miin; Patterson, Andrew J.; Chang, Hing‐Chiu; Gillard, Jonathan H.; Graves, Martin J.
• Is Part Of: Medical physics. Volume 42:Issue 10(2015)
• Page Start: 5757
• Page End: 5767
• Publication Date: 2015-09-11
• Subjects: biomedical MRI -- graphics processing units -- image denoising -- image reconstruction -- interpolation -- iterative methods -- medical image processing -- object‐oriented methods -- phantoms -- statistical analysis -- Toeplitz matrices; Magnetic resonance imaging -- Numerical linear algebra -- Noise; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Processor architectures; Processor configuration, e.g. pipelining -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; PROPELLER -- iterative reconstruction -- total variation denoising -- rFOV reconstruction -- split‐Bregman method -- Toeplitz method; Image reconstruction -- Medical image reconstruction -- Medical image quality -- Computer software -- Magnetic resonance imaging -- Interpolation -- Parallel processing -- Gold -- Computer hardware; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4929560
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To propose a new reduced field‐of‐view (rFOV) strategy for iterative reconstructions in a clinical environment. Iterative reconstructions can incorporate regularization terms to improve the image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI. However, the large amount of calculations required for full FOV iterative reconstructions has posed a huge computational challenge for clinical usage. By subdividing the entire problem into smaller rFOVs, the iterative reconstruction can be accelerated on a desktop with a single graphic processing unit (GPU). Methods: This rFOV strategy divides the iterative reconstruction into blocks, based on the block‐diagonal dominant structure. A near real‐time reconstruction system was developed for the clinical MR unit, and parallel computing was implemented using the object‐oriented model. In addition, the Toeplitz method was implemented on the GPU to reduce the time required for full interpolation. Using the data acquired from the PROPELLER MRI, the reconstructed images were then saved in the digital imaging and communications in medicine format. Results: The proposed rFOV reconstruction reduced the gridding time by 97%, as the total iteration time was 3 s even with multiple processes running. A phantom study showed that the structure similarity index for rFOV reconstruction was statistically superior to conventional density compensation ( p < 0.001). In vivo study validated the increased signal‐to‐noise ratio, which is over four times higher than with density compensation. Image sharpness index was improved using the regularized reconstruction implemented. Conclusions: The rFOV strategy permits near real‐time iterative reconstruction to improve the image quality of PROPELLER images. Substantial improvements in image quality metrics were validated in the experiments. The concept of rFOV reconstruction may potentially be applied to other kinds of iterative reconstructions for shortened reconstruction duration.