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

A built-up-type deformable phantom for target motion control to mimic human lung respiration

Existing human lung-mimicking requirements in various radiology application fields have led to the development of many different phantoms. However, most are static apparatus designed for equipment calibration. Although there are a few dynamic phantoms that generate predefined motions, they have complicated mechanisms that hamper even simple modifications for various lung illness simulations. As a result, existing dynamic phantoms in which a target can be embedded normally generate rectilinear target motions with limited displacement. Nevertheless, volume changes in the human lungs during normal respiration are significant, and targets inside the lungs move along various random paths depending on their location, stiffness, and the properties of the surrounding tissues. In the present work, a novel phantom design is introduced and tested. The phantom mimics the human lung motion and its deformation is initiated by a diaphragm movement. The phantom provides a fairly large deformation and the capability to adjust target motion paths. The presented device has a simple mechanism that can be easily modified to generate various pulmonary diseases. To produce a large deformation by diaphragm compressive motion, polyurethane cubic blocks constitute the deformable part of the lung phantom and a tumor made with silicone is inserted in the structure. The assembled lung part is housed within an acrylic case that is filled with water. The phantom system consists of acrylic, plastic, and low-density polyurethane to minimize artifacts when it undergoes computed tomography (CT) scans. The lung part is organized with various density polyurethane blocks, making it possible to produce nonlinear motion paths of the tumor. The lung part is deformed by a silicon film that is driven by external hydraulic pressure. A finite element method simulation and two-dimensional target motion tests were performed to verify phantom performance. The functionality of the proposed phantom system was confirmed in a series of CT images.

Performance Validation of In-House Developed Four-dimensional Dynamic Phantom

Objective:

The objective of this study was to validate the performance characteristics of in-house developed four-dimensional (4D) dynamic phantom (FDDP).

Materials and Methods:

There are three target inserts of 1.0, 1.5 and 2.0 cm diameter. The targets were driven in sinusoidal pattern in the longitudinal direction, using the combinations of amplitudes of 0.5, 1.0, and 1.5 cm with frequencies of 0.2 and 0.25 Hz. The amplitude and frequency of motion were measured manually, and by using Real-Time Position Management (RPM) system also. The static, free-breathing, and 4D computed tomography (CT) scans of the phantom were acquired with 1.0 mm slice thickness. The 4DCT scans were sorted into 0%–90% phase, and the maximum intensity projection (MIP) images were also generated. The static, free-breathing, and 4DCT data sets and MIP images were contoured to get V_Static, V_FB, V₀₀......V₉₀, and internal target volume ITV _MIP, respectively. The individual phase volumes were summed to obtain V_4D. The length of the target in the motion was measured using MIP image and compared with theoretical length (TL). The variation of 3D displacement vector of individual phase volume with respect to V₀₀ with the phase of motion was studied at amplitude and frequency of 1.0 cm and 0.25 Hz, respectively. The degree of similarity between V_FB and V_4D and V_FB and ITV_MIP was also studied for all the target sizes at amplitude and frequency of 1.0 cm and 0.2 Hz and 1.0 cm and 0.25 Hz, respectively.

Results:

The amplitude and frequency of motion agreed within the limits of uncertainty with the manually and RPM measured values. The length of target in the motion matched within 1.0 mm with TL. The 3D displacement of individual phase volume showed no target size dependence, and the degree of similarity between V_FB and V_4D and V_FB and ITV_MIP decreases with increase in the displacement between the two volumes.

Conclusions:

The mechanical and imaging performances of FDDP were found within the acceptable limits. Therefore, this phantom can be used for quality assurance of 4D imaging process in radiotherapy.

ELPHA: Dynamically deformable liver phantom for real-time motion-adaptive radiotherapy treatments

PURPOSE

Real-time motion-adaptive radiotherapy of intrahepatic tumors needs to account for motion and deformations of the liver and the target location within. Phantoms representative of anatomical deformations are required to investigate and improve dynamic treatments. A deformable phantom capable of testing motion detection and motion mitigation techniques is presented here.

METHODS

The dynamically dEformable Liver PHAntom (ELPHA) was designed to fulfill three main constraints: First, a reproducibly deformable anatomy is required. Second, the phantom should provide multimodality imaging contrast for motion detection. Third, a time-resolved dosimetry system to measure temporal effects should be provided. An artificial liver with vasculature was casted from soft silicone mixtures. The silicones allow for deformation and radiographic image contrast, while added cellulose provides ultrasonic contrast. An actuator was used for compressing the liver in the inferior direction according to a prescribed respiratory motion trace. Electromagnetic (EM) transponders integrated in ELPHA help provide ground truth motion traces. They were used to quantify the motion reproducibility of the phantom and to validate motion detection based on ultrasound imaging. A two-dimensional ultrasound probe was used to follow the position of the vessels with a template-matching algorithm. This detected vessel motion was compared to the EM transponder signal by calculating the root-mean-square error (RMSE). ELPHA was then used to investigate the dose deposition of dynamic treatment deliveries. Two dosimetry systems, radio-chromic film and plastic scintillation dosimeters (PSD), were integrated in ELPHA. The PSD allow for time-resolved measurement of the delivered dose, which was compared to a time-resolved dose of the treatment planning system. Film and PSD were used to investigate dose delivery to the deforming phantom without motion compensation and with treatment-couch tracking for motion compensation.

RESULTS

ELPHA showed densities of 66 and 45 HU in the liver and the surrounding tissues. A high motion reproducibility with a submillimeter RMSE (<0.32 mm) was measured. The motion of the vasculature detected with ultrasound agreed well with the EM transponder position (RMSE < 1 mm). A time-resolved dosimetry system with a 1 Hz time resolution was achieved with the PSD. The agreement of the planned and measured dose to the PSD decreased with increasing motion amplitude: A dosimetric RMSE of 1.2, 2.1, and 2.7 cGy/s was measured for motion amplitudes of 8, 16, and 24 mm, respectively. With couch tracking as motion compensation, these values decreased to 1.1, 1.4, and 1.4 cGy/s. This is closer to the static situation with 0.7 cGy/s. Film measurements showed that couch tracking was able to compensate for motion with a mean target dose within 5% of the static situation (-5% to +1%), which was higher than in the uncompensated cases (-41% to -1%).

CONCLUSIONS

ELPHA is a deformable liver phantom with high motion reproducibility. It was demonstrated to be suitable for the verification of motion detection and motion mitigation modalities. Based on the multimodality image contrast, a high accuracy of ultrasound based motion detection was shown. With the time-resolved dosimetry system, ELPHA is suitable for performance assessment of real-time motion-adaptive radiotherapy, as was shown exemplary with couch tracking.

ESR statement on the stepwise development of imaging biomarkers

Development of imaging biomarkers is a structured process in which new biomarkers are discovered, verified, validated and qualified against biological processes and clinical end-points. The validation process not only concerns the determination of the sensitivity and specificity but also the measurement of reproducibility. Reproducibility assessments and standardisation of the acquisition and data analysis methods are crucial when imaging biomarkers are used in multicentre trials for assessing response to treatment. Quality control in multicentre trials can be performed with the use of imaging phantoms. The cost-effectiveness of imaging biomarkers also needs to be determined. A lot of imaging biomarkers are being developed, but there are still unmet needs—for example, in the detection of tumour invasiveness.

Main Messages

• Using imaging biomarkers to streamline drug discovery and disease progression is a huge advancement in healthcare.

• The qualification and technical validation of imaging biomarkers pose unique challenges in that the accuracy, methods, standardisations and reproducibility are strictly monitored.

• The clinical value of new biomarkers is of the highest priority in terms of patient management, assessing risk factors and disease prognosis.

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

Comparison of two respiration monitoring systems for 4D imaging with a Siemens CT using a new dynamic breathing phantom

Four-dimensional computed tomography (4D-CT) requires breathing information from the patient, and for this, several systems are available. Testing of these systems, under realistic conditions, requires a phantom with a moving target and an expandable outer contour. An anthropomorphic phantom was developed to simulate patient breathing as well as lung tumor motion. Using the phantom, an optical camera system (GateCT) and a pressure sensor (AZ-733V) were simultaneously operated, and 4D-CTs were reconstructed with a Siemens CT using the provided local-amplitude-based sorting algorithm. The comparison of the tumor trajectories of both systems revealed discrepancies up to 9.7 mm. Breathing signal differences, such as baseline drift, temporal resolution and noise level were shown not to be the reason for this. Instead, the variability of the sampling interval and the accuracy of the sampling rate value written on the header of the GateCT-signal file were identified as the cause. Interpolation to regular sampling intervals and correction of the sampling rate to the actual value removed the observed discrepancies. Consistently, the introduction of sampling interval variability and inaccurate sampling rate values into the header of the AZ-733V file distorted the tumor trajectory for this system. These results underline the importance of testing new equipment thoroughly, especially if components of different manufacturers are combined.

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