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Four-dimensional tissue deformation reconstruction (4D TDR) validation using a real tissue phantom.

ABSTRACT: Calculation of four-dimensional (4D) dose distributions requires the remapping of dose calculated on each available binned phase of the 4D CT onto a reference phase for summation. Deformable image registration (DIR) is usually used for this task, but unfortunately almost always considers only endpoints rather than the whole motion path. A new algorithm, 4D tissue deformation reconstruction (4D TDR), that uses either CT projection data or all available 4D CT images to reconstruct 4D motion data, was developed. The purpose of this work is to verify the accuracy of the fit of this new algorithm using a realistic tissue phantom. A previously described fresh tissue phantom with implanted electromagnetic tracking (EMT) fiducials was used for this experiment. The phantom was animated using a sinusoidal and a real patient-breathing signal. Four-dimensional computer tomography (4D CT) and EMT tracking were performed. Deformation reconstruction was conducted using the 4D TDR and a modified 4D TDR which takes real tissue hysteresis (4D TDR(Hysteresis)) into account. Deformation estimation results were compared to the EMT and 4D CT coordinate measurements. To eliminate the possibility of the high contrast markers driving the 4D TDR, a comparison was made using the original 4D CT data and data in which the fiducials were electronically masked. For the sinusoidal animation, the average deviation of the 4D TDR compared to the manually determined coordinates from 4D CT data was 1.9 mm, albeit with as large as 4.5 mm deviation. The 4D TDR calculation traces matched 95% of the EMT trace within 2.8 mm. The motion hysteresis generated by real tissue is not properly projected other than at endpoints of motion. Sinusoidal animation resulted in 95% of EMT measured locations to be within less than 1.2 mm of the measured 4D CT motion path, enabling accurate motion characterization of the tissue hysteresis. The 4D TDR(Hysteresis) calculation traces accounted well for the hysteresis and matched 95% of the EMT trace within 1.6 mm. An irregular (in amplitude and frequency) recorded patient trace applied to the same tissue resulted in 95% of the EMT trace points within less than 4.5 mm when compared to both the 4D CT and 4D TDR(Hysteresis) motion paths. The average deviation of 4D TDR(Hysteresis) compared to 4D CT datasets was 0.9 mm under regular sinusoidal and 1.0 mm under irregular patient trace animation. The EMT trace data fit to the 4D TDR(Hysteresis) was within 1.6 mm for sinusoidal and 4.5 mm for patient trace animation. While various algorithms have been validated for end-to-end accuracy, one can only be fully confident in the performance of a predictive algorithm if one looks at data along the full motion path. The 4D TDR, calculating the whole motion path rather than only phase- or endpoints, allows us to fully characterize the accuracy of a predictive algorithm, minimizing assumptions. This algorithm went one step further by allowing for the inclusion of tissue hysteresis effects, a real-world effect that is neglected when endpoint-only validation is performed. Our results show that the 4D TDR(Hysteresis) correctly models the deformation at the endpoints and any intermediate points along the motion path.

SUBMITTER: Szegedi M

PROVIDER: S-EPMC5713919 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMotion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.PurposeThis study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.MethodsA 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).ResultsThe trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.ConclusionsCNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Project description:3DVH software (Sun Nuclear Corp., Melbourne, FL) is capable of generating a volumetric patient VMAT dose by applying a volumetric perturbation algorithm based on comparing measurement-guided dose reconstruction and TPS-calculated dose to a cylindrical phantom. The primary purpose of this paper is to validate this dose reconstruction on an anthropomorphic heterogeneous thoracic phantom by direct comparison to independent measurements. The dosimetric insert to the phantom is novel, and thus the secondary goal is to demonstrate how it can be used for the hidden target end-to-end testing of VMAT treatments in lung. A dosimetric insert contains a 4 cm diameter unit-density spherical target located inside the right lung (0.21 g/cm(3) density). It has 26 slots arranged in two orthogonal directions, milled to hold optically stimulated luminescent dosimeters (OSLDs). Dose profiles in three cardinal orthogonal directions were obtained for five VMAT plans with varying degrees of modulation. After appropriate OSLD corrections were applied, 3DVH measurement-guided VMAT dose reconstruction agreed 100% with the measurements in the unit density target sphere at 3%/3 mm level (composite analysis) for all profile points for the four less-modulated VMAT plans, and for 96% of the points in the highly modulated C-shape plan (from TG-119). For this latter plan, while 3DVH shows acceptable agreement with independent measurements in the unit density target, in the lung disagreement with experiment is relatively high for both the TPS calculation and 3DVH reconstruction. For the four plans excluding the C-shape, 3%/3 mm overall composite analysis passing rates for 3DVH against independent measurement ranged from 93% to 100%. The C-shape plan was deliberately chosen as a stress test of the algorithm. The dosimetric spatial alignment hidden target test demonstrated the average distance to agreement between the measured and TPS profiles in the steep dose gradient area at the edge of the 2 cm target to be 1.0 ± 0.7, 0.3 ± 0.3, and 0.3 ± 0.3 mm for the IEC X, Y, and Z directions, respectively.

Project description:PURPOSE:To validate three widely-used acceleration methods in four-dimensional (4D) flow cardiac MR; segmented 4D-spoiled-gradient-echo (4D-SPGR), 4D-echo-planar-imaging (4D-EPI), and 4D-k-t Broad-use Linear Acquisition Speed-up Technique (4D-k-t BLAST). MATERIALS AND METHODS:Acceleration methods were investigated in static/pulsatile phantoms and 25 volunteers on 1.5 Tesla MR systems. In phantoms, flow was quantified by 2D phase-contrast (PC), the three 4D flow methods and the time-beaker flow measurements. The later was used as the reference method. Peak velocity and flow assessment was done by means of all sequences. For peak velocity assessment 2D PC was used as the reference method. For flow assessment, consistency between mitral inflow and aortic outflow was investigated for all pulse-sequences. Visual grading of image quality/artifacts was performed on a four-point-scale (0?=?no artifacts; 3?=?nonevaluable). RESULTS:For the pulsatile phantom experiments, the mean error for 2D PC?=?1.0?±?1.1%, 4D-SPGR?=?4.9?±?1.3%, 4D-EPI?=?7.6?±?1.3% and 4D-k-t BLAST?=?4.4?±?1.9%. In vivo, acquisition time was shortest for 4D-EPI (4D-EPI?=?8?±?2?min versus 4D-SPGR?=?9?±?3?min, P?<?0.05 and 4D-k-t BLAST?=?9?±?3?min, P?=?0.29). 4D-EPI and 4D-k-t BLAST had minimal artifacts, while for 4D-SPGR, 40% of aortic valve/mitral valve (AV/MV) assessments scored 3 (nonevaluable). Peak velocity assessment using 4D-EPI demonstrated best correlation to 2D PC (AV:r?=?0.78, P?<?0.001; MV:r?=?0.71, P?<?0.001). Coefficient of variability (CV) for net forward flow (NFF) volume was least for 4D-EPI (7%) (2D PC:11%, 4D-SPGR: 29%, 4D-k-t BLAST: 30%, respectively). CONCLUSION:In phantom, all 4D flow techniques demonstrated mean error of less than 8%. 4D-EPI demonstrated the least susceptibility to artifacts, good image quality, modest agreement with the current reference standard for peak intra-cardiac velocities and the highest consistency of intra-cardiac flow quantifications. LEVEL OF EVIDENCE:1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:272-281.

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Four-dimensional tissue deformation reconstruction (4D TDR) validation using a real tissue phantom.

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