Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors. Issue 8 (30th November 2016)

• Record Type: Journal Article
• Authors: Yip, Eugene; Yun, Jihyun; Wachowicz, Keith; Heikal, Amr A.; Gabos, Zsolt; Rathee, Satyapal; Fallone, B. G.
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-11-30
• Subjects: Magnetic resonance imaging -- Image quality; biomedical MRI -- compressed sensing -- data acquisition -- lung -- medical image processing -- tumours; compressed sensing -- lung tumor tracking -- dynamic MRI -- linac‐MR -- MRI guidance; 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; Medical image reconstruction -- Cancer -- Image reconstruction -- Medical image noise -- Lungs -- Medical image artifacts -- Medical magnetic resonance imaging -- Fourier transforms; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4885960
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Hybrid radiotherapy‐MRI devices promise real time tracking of moving tumors to focus the radiation portals to the tumor during irradiation. This approach will benefit from the increased temporal resolution of MRI's data acquisition and reconstruction. In this work, the authors propose a novel spatial‐temporal compressed sensing (CS) imaging strategy for the real time MRI–‐prior data assisted compressed sensing (PDACS), which aims to improve the image quality of the conventional CS without significantly increasing reconstruction times. Methods: Conventional 2D CS requires a random sampling of partial k‐space data, as well as an iterative reconstruction that simultaneously enforces the image's sparsity in a transform domain as well as maintains the fidelity to the acquired k‐space. PDACS method requires the additional acquisition of the prior data, and for reconstruction, it additionally enforces fidelity to the prior k‐space domain similar to viewsharing. In this work, the authors evaluated the proposed PDACS method by comparing its results to those obtained from the 2D CS and viewsharing methods when performed individually. All three methods are used to reconstruct images from lung cancer patients whose tumors move and who are likely to benefit from lung tumor tracking. The patients are scanned, using a 3T MRI, under free breathing using the fully sampled k‐space with 2D dynamic bSSFP sequence in a sagittal plane containing lung tumor. These images form a reference set for the evaluation of the partial k‐space methods. To create partial k‐space, the fully sampled k‐space is retrospectively undersampled to obtain a range of acquisition acceleration factors, and reconstructed with 2D‐CS, PDACS, and viewshare methods. For evaluation, metrics assessing global image artifacts as well as tumor contour shape fidelity are determined from the reconstructed images. These analyses are performed both for the original 3T images and those at a simulated 0.5T equivalent noise level. Results: In the 3.0T images, the PDACS strategy is shown to give superior results compared to viewshare and conventional 2D CS using all metrics. The 2D‐CS tends to perform better than viewshare at the low acceleration factors, while the opposite is true at the high acceleration factors. At simulated 0.5T images, PDACS method performs only marginally better than the viewsharing method, both of which are superior compared to 2D CS. The PDACS image reconstruction time (0.3 s/image) is similar to that of the conventional 2D CS. Conclusions: The PDACS method can potentially improve the real time tracking of moving tumors by significantly increasing MRI's data acquisition speeds. In 3T images, the PDACS method does provide a benefit over the other two methods in terms of both the overall image quality and the ability to accurately and automatically contour the tumor shape. MRI's data acquisition may be accelerated using the simpler viewsharing strategy at the lower, 0.5T magnetic field, as the marginal benefit of the PDACS method may not justify its additional reconstruction times.