Advances in anthropomorphic thorax phantoms for radiotherapy: a review

A phantom is a highly specialized device, which mimic human body, or a part of it. There are three categories of phantoms: physical phantoms, physiological phantoms, and computational phantoms. The phantoms have been utilized in medical imaging and radiotherapy for numerous applications. In radiotherapy, the phantoms may be used for various applications such as quality assurance (QA), dosimetry, end-to-end testing, etc. In thoracic radiotherapy, unique QA problems including tumor motion, thorax deformation, and heterogeneities in the beam path have complicated the delivery of dose to both tumor and organ at risks (OARs). Also, respiratory motion is a major challenge in radiotherapy of thoracic malignancies, which can be resulted in the discrepancies between the planned and delivered doses to cancerous tissue. Hence, the overall treatment procedure needs to be verified. Anthropomorphic thorax phantoms, which are made of human tissue-mimicking materials, can be utilized to obtain the ground truth to validate these processes. Accordingly, research into new anthropomorphic thorax phantoms has accelerated. Therefore, the review is intended to summarize the current status of the commercially available and in-house-built anthropomorphic physical/physiological thorax phantoms in radiotherapy. The main focus is on anthropomorphic, deformable thorax motion phantoms. This review also discusses the applications of three-dimensional (3D) printing technology for the fabrication of thorax phantoms.

Automatic large quantity landmark pairs detection in 4DCT lung images

PURPOSE

To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy.

METHODS

A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs.

RESULTS

Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm.

CONCLUSION

A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

Basic evaluation of a novel 4D target and human body phantom

Stereotactic body radiation therapy (SBRT) is usually verified with a dynamic phantom or solid phantom, but there is a demand for phantoms that can accurately simulate tumor dynamics within an individual that would allow customized validation in every patient. We developed a new 4D dynamic target phantom (multi-cell 4D phantom) that allows simulation of tumor movement in patients. The basic quality and dynamic reproducibility of this new phantom was verified in this investigation. The newly developed multi-cell 4D phantom comprises four main components: soft tissue, bones, lungs, and tumor (target). The phantom structure was based on computed tomography (CT) data of a male. In this study, we investigated the basic performance of a multi-cell 4D phantom. All the CT numbers of the phantom were very close to those of human data. The geometric maximum amplitudes were 4.57 mm in the lateral direction, 4.59 mm in the ventrodorsal direction, and 3.68 mm in the cranio-caudal direction. Geometric errors were 0.84, 0.58, and 0.40 mm, respectively. Movements of the abdominal surface were stable for 60 s. Repeated measurements show no actual differences in target movements between multiple measurements and indicated high reproducibility (r  >  0.97). End-to-end tests using Gafchromic film revealed a gamma pass rate of 98% or above (2 mm/3%). Although our phantom performed limited reproducibility in the movement of the patient tumor at present, a satisfactory level of precision was confirmed in general. This is a very promising device for use in the verification of radiation therapy for moving targets.

A novel deformable lung phantom with programably variable external and internal correlation

PURPOSE

Lung motion phantoms used to validate radiotherapy motion management strategies have fairly simplistic designs that do not adequately capture complex phenomena observed in human respiration such as external and internal deformation, variable hysteresis and variable correlation between different parts of the thoracic anatomy. These limitations make reliable evaluation of sophisticated motion management techniques quite challenging. In this work, we present the design and implementation of a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external-internal and internal-internal correlation of embedded markers.

METHODS

An in-house-designed lung module, made from natural latex foam was inserted inside the outer shell of a commercially available lung phantom (RSD, Long Beach, CA, USA). Radiopaque markers were placed on the external surface and embedded into the lung module. Two independently programmable high-precision linear motion actuators were used to generate primarily anterior-posterior (AP) and primarily superior-inferior (SI) motion in a reproducible fashion in order to enable (a) variable correlation between the displacement of interior volume and the exterior surface, (b) independent changes in the amplitude of the AP and SI motions, and (c) variable hysteresis. The ability of the phantom to produce complex and variable motion accurately and reproducibly was evaluated by programming the two actuators with mathematical and patient-recorded lung tumor motion traces, and recording the trajectories of various markers using kV fluoroscopy. As an example application, the phantom was used to evaluate the performance of lung motion models constructed from kV fluoroscopy and 4DCT images.

RESULTS

The phantom exhibited a high degree of reproducibility and marker motion ranges were reproducible to within 0.5 mm. Variable correlation was observed between the displacements of internal-internal and internal-external markers. The SI and AP components of motion of a specific marker had a correlation parameter that varied from -11 to 17. Monitoring a region of interest on the phantom's surface to estimate internal marker motion led to considerably lower uncertainties than when a single point was monitored.

CONCLUSIONS

We successfully designed and implemented a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external-internal and internal-internal correlation of embedded markers.

A method to detect landmark pairs accurately between intra-patient volumetric medical images

PURPOSES

An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively.

METHODS

Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency.

RESULTS

The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm.

CONCLUSION

A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.

Development of a deformable dosimetric phantom to verify dose accumulation algorithms for adaptive radiotherapy

Adaptive radiotherapy may improve treatment outcomes for lung cancer patients. Because of the lack of an effective tool for quality assurance, this therapeutic modality is not yet accepted in clinic. The purpose of this study is to develop a deformable physical phantom for validation of dose accumulation algorithms in regions with heterogeneous mass. A three-dimensional (3D) deformable phantom was developed containing a tissue-equivalent tumor and heterogeneous sponge inserts. Thermoluminescent dosimeters (TLDs) were placed at multiple locations in the phantom each time before dose measurement. Doses were measured with the phantom in both the static and deformed cases. The deformation of the phantom was actuated by a motor driven piston. 4D computed tomography images were acquired to calculate 3D doses at each phase using Pinnacle and EGSnrc/DOSXYZnrc. These images were registered using two registration software packages: VelocityAI and Elastix. With the resultant displacement vector fields (DVFs), the calculated 3D doses were accumulated using a mass-and energy congruent mapping method and compared to those measured by the TLDs at four typical locations. In the static case, TLD measurements agreed with all the algorithms by 1.8% at the center of the tumor volume and by 4.0% in the penumbra. In the deformable case, the phantom's deformation was reproduced within 1.1 mm. For the 3D dose calculated by Pinnacle, the total dose accumulated with the Elastix DVF agreed well to the TLD measurements with their differences <2.5% at four measured locations. When the VelocityAI DVF was used, their difference increased up to 11.8%. For the 3D dose calculated by EGSnrc/DOSXYZnrc, the total doses accumulated with the two DVFs were within 5.7% of the TLD measurements which are slightly over the rate of 5% for clinical acceptance. The detector-embedded deformable phantom allows radiation dose to be measured in a dynamic environment, similar to deforming lung tissues, supporting the validation of dose mapping and accumulation operations in regions with heterogeneous mass, and dose distributions.

Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm

PURPOSE

The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping.

METHODS

The authors describe a DIR based adaptive radiotherapy workflow, using CT and cone-beam CT (CBCT) imaging. The transformations that mapped the anatomy between the two time points were obtained using four different DIR approaches available in NiftyReg. These included a standard unidirectional algorithm and more sophisticated bidirectional ones that encourage or ensure inverse consistency. The forward (CT-to-CBCT) deformation vector fields (DVFs) were used to propagate the CT Hounsfield units and structures to the daily geometry for "dose of the day" calculations, while the backward (CBCT-to-CT) DVFs were used to remap the dose of the day onto the planning CT (pCT). Data from five head and neck patients were used to evaluate the performance of each implementation based on geometrical matching, physical properties of the DVFs, and similarity between warped dose distributions. Geometrical matching was verified in terms of dice similarity coefficient (DSC), distance transform, false positives, and false negatives. The physical properties of the DVFs were assessed calculating the harmonic energy, determinant of the Jacobian, and inverse consistency error of the transformations. Dose distributions were displayed on the pCT dose space and compared using dose difference (DD), distance to dose difference, and dose volume histograms.

RESULTS

All the DIR algorithms gave similar results in terms of geometrical matching, with an average DSC of 0.85 ± 0.08, but the underlying properties of the DVFs varied in terms of smoothness and inverse consistency. When comparing the doses warped by different algorithms, we found a root mean square DD of 1.9% ± 0.8% of the prescribed dose (pD) and that an average of 9% ± 4% of voxels within the treated volume failed a 2%pD DD-test (DD2%-pp). Larger DD2%-pp was found within the high dose gradient (21% ± 6%) and regions where the CBCT quality was poorer (28% ± 9%). The differences when estimating the mean and maximum dose delivered to organs-at-risk were up to 2.0%pD and 2.8%pD, respectively.

CONCLUSIONS

The authors evaluated several DIR algorithms for CT-to-CBCT registrations. In spite of all methods resulting in comparable geometrical matching, the choice of DIR implementation leads to uncertainties in dose warped, particularly in regions of high gradient and/or poor imaging quality.

Geometric accuracy of dynamic MLC tracking with an implantable wired electromagnetic transponder

BACKGROUND

Tumor motion during radiotherapy delivery can substantially deteriorate the target dose distribution. A promising method to overcome this problem is dynamic multi-leaf collimator (DMLC) tracking. The purpose of this phantom study was to integrate a wired electromagnetic (EM) transponder localization system with DMLC tracking and to investigate the geometric accuracy of the integrated system.

MATERIAL AND METHODS

DMLC tracking experiments were performed on a Trilogy accelerator with a prototype DMLC tracking system. A wired implantable EM transponder was mounted on a motion stage with a 3 mm tungsten sphere used for target visualization in continuous portal images. The three dimensional (3D) transponder position signal was used for DMLC aperture adaption. The motion stage was programmed to reproduce eight representative patient-measured trajectories for prostate and for lung tumors. The tracking system latency was determined and prediction was used for the lung tumor trajectories to account for the latency. For each trajectory, three conformal fields with a 10 cm circular MLC aperture and 72 s treatment duration were delivered: (1) a 358° arc field; (2) an anterior static field; and (3) a lateral static field. The tracking error was measured as the difference between the marker position and the MLC aperture in the portal images.

RESULTS

The tracking system latency was 140 ms. The mean root-mean-square (rms) of the 3D transponder localization error was 0.53/0.54 mm for prostate/lung tumor trajectories. The mean rms of the two dimensional (2D) tracking error was 0.69 mm (prostate) and 0.98 mm (lung tumors) with tracking and 3.4 mm (prostate) and 5.3 mm (lung tumors) without tracking.

CONCLUSIONS

DMLC tracking was integrated with a wired EM transponder localization system and investigated for arc and static field delivery. The system provides sub-mm geometrical errors for most trajectories.