A Technique for Generating Volumetric Cine MRI (VC-MRI)

Publication date: Available online 6 February 2016
Source:International Journal of Radiation Oncology*Biology*Physics
Author(s): Wendy Harris, Lei Ren, Jing Cai, You Zhang, Zheng Chang, Fang-Fang Yin
PurposeTo develop a technique to generate on-board volumetric-cine MRI (VC-MRI) using patient prior images, motion modeling and on-board 2D-cine MRI.MethodsOne phase of a 4D-MRI acquired during patient simulation is used as patient prior images. 3 major respiratory deformation patterns of the patient are extracted from 4D-MRI based on principal-component-analysis. The on-board VC-MRI at any instant is considered as a deformation of the prior MRI. The deformation field is represented as a linear combination of the 3 major deformation patterns. The coefficients of the deformation patterns are solved by the data fidelity constraint using the acquired on-board single 2D-cine MRI. The method was evaluated using both XCAT simulation of lung cancer patients and MRI data from four real liver cancer patients. The accuracy of the estimated VC-MRI was quantitatively evaluated using Volume-Percent-Difference(VPD), Center-of-Mass-Shift(COMS), and target tracking errors. Effects of acquisition orientation, region-of-interest(ROI) selection, patient breathing pattern change and noise on the estimation accuracy were also evaluated.ResultsImage subtraction of ground-truth with estimated on-board VC-MRI shows fewer differences than image subtraction of ground-truth with prior image. Agreement between profiles in the estimated and ground-truth VC-MRI was achieved with less than 6% error for both XCAT and patient data. Among all XCAT scenarios, the VPD between ground-truth and estimated lesion volumes was on average 8.43±1.52% and the COMS was on average 0.93±0.58mm across all time-steps for estimation based on the ROI region in the sagittal cine images. Matching to ROI in the sagittal view achieved better accuracy when there was substantial breathing pattern change. The technique was robust against noise levels up to SNR=20. For patient data, average tracking errors were less than 2 mm in all directions for all patients.ConclusionsPreliminary studies demonstrated the feasibility to generate real-time VC-MRI for on-board localization of moving targets in radiotherapy.

Teaser

A novel technique has been developed to generate volumetric cine MRI (VC-MRI) using patient prior information. The VC-MRI was generated by deforming the prior MRI images based on the on-board 2D cine MRI and patient respiratory breathing model. The technique was evaluated using both anthropomorphic digital phantom and patient data. Results demonstrated the feasibility of generating VC-MRI for both inter- and intra-fraction verification of moving targets in radiotherapy.


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