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

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 TDRHysteresis) 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 TDRHysteresis 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 TDRHysteresis motion paths. The average deviation of 4D TDRHysteresis 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 TDRHysteresis 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 TDRHysteresis correctly models the deformation at the endpoints and any intermediate points along the motion path.

PACS numbers: 87.55.km, 87.55.Qr, 87.57.nf, 87.85.Tu

4D CT image acquisition errors in SBRT of liver identified using correlation

In the AAPM Report 80,⁽¹⁾ the imaging modality of 4D CT and respiration‐correlated CT was declared a “promising solution for obtaining high‐quality CT data in the presence of respiratory motion”. To gather anatomically correct data over time, the existence of correlation between the internal organ movement and an external surrogate has to be assumed. For the in‐house evaluation of such correlation, we retrospectively analyzed 21 four‐dimensional computer tomography (4D CT) scans of five patients, out of which the artifacts experienced in three patients are shown here. To provide context and a baseline for the analysis of patient motion, a real‐tissue liver phantom was used with a solid water block and liver tissue. The superior–inferior motion of fiducials in phantom and patients was correlated to the recorded anterior–posterior motion of an external surrogate marker on the chest. The use of a solid water block yielded a measurable correlation coefficient of 0.98 or better using a sinusoidal animation pattern. With sinusoidally‐animated liver tissue, the minimum correlation observed was 0.96. Comparing this to retrospective patient data, we found three cases of a change in the correlation coefficient, or simply a low correlation. The source of this low correlation was investigated by careful examination of the breathing traces and the CT‐phase assignments used to reconstruct the datasets. Consequences of nonregular breathing are elaborated on. We demonstrate the impact of wrong phase assignments and missing image information in the 4D CT phase sampling processes. We also show how daily patient‐based correlation analysis can indicate changes in breathing traces, which can be significant enough to decrease, or completely eliminate, previously existing correlation.

PACS numbers: 87.57.‐s, 87.57.Q‐, 87.57.cp, 87.57.N‐, 87.55.Qr

A proto-type design of a real-tissue phantom for the validation of deformation algorithms and 4D dose calculations

The purpose of this study is to design a real-tissue phantom for use in the validation of deformation algorithms. A phantom motion controller that runs sinusoidal and non-regular patient-based breathing pattern, via a piston, was applied to porcine liver tissue. It was regulated to simulate movement ranges similar to recorded implanted liver markers from patients. 4D CT was applied to analyze deformation. The suitability of various markers in the liver and the position reproducibility of markers and of reference points were studied. The similarity of marker motion pattern in the liver phantom and in real patients was evaluated. The viability of the phantom over time and its use with electro-magnetic tracking devices were also assessed. High contrast markers, such as carbon markers, implanted in the porcine liver produced less image artifacts on CT and were well visualized compared to metallic ones. The repositionability of markers was within a measurement accuracy of +/-2 mm. Similar anatomical patient motions were reproducible up to elongations of 3 cm for a time period of at least 90 min. The phantom is compatible with electro-magnetic tracking devices and 4D CT. The phantom motion is reproducible and simulates realistic patient motion and deformation. The ability to carry out voxel-based tracking allows for the evaluation of deformation algorithms in a controlled environment with recorded patient traces. The phantom is compatible with all therapy devices clinically encountered in our department.