Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking.

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Bibliographic Details
Title:	Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking.
Authors:	Dietz, Bryson¹, Yip, Eugene¹, Yun, Jihyun¹, Fallone, B. Gino^2,3, Wachowicz, Keith^4,5
Source:	Medical Physics. Aug2017, Vol. 44 Issue 8, p3978-3989. 12p.
Subjects:	Computer simulation of image reconstruction, Image reconstruction, Image processing, Treatment of lung tumors, Compressed sensing
Abstract:	Purpose This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis ( CS- PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Methods Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS- PCA algorithm. Using a database of fully sampled k-space, principal components ( PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS- PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error ( NMSE), as well as the dice coefficients ( DC) and centroid displacement of the tumor segmentations. Results Our results demonstrate that CS- PCA performed superior than CS alone. The CS- PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS- PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710 HQ CPU @ 2.5 GHz), depending on the chosen parameters. Conclusions A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac- MRI system. Our CS- PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS- PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Purpose This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis ( CS- PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Methods Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS- PCA algorithm. Using a database of fully sampled k-space, principal components ( PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS- PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error ( NMSE), as well as the dice coefficients ( DC) and centroid displacement of the tumor segmentations. Results Our results demonstrate that CS- PCA performed superior than CS alone. The CS- PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS- PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710 HQ CPU @ 2.5 GHz), depending on the chosen parameters. Conclusions A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac- MRI system. Our CS- PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS- PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code. [ABSTRACT FROM AUTHOR]
ISSN:	00942405
DOI:	10.1002/mp.12354