Chun-Chien Shieh

Toward the development of intrafraction tumor deformation tracking using a dynamic multi-leaf collimator.

PURPOSE Intrafraction deformation limits targeting accuracy in radiotherapy. Studies show tumor deformation of over 10 mm for both single tumor deformation and system deformation (due to differential motion between primary tumors and involved lymph nodes). Such deformation cannot be adapted to with current radiotherapy methods. The objective of this study was to develop and experimentally investigate the ability of a dynamic multi-leaf collimator (DMLC) tracking system to account for tumor deformation. METHODS To compensate for tumor deformation, the DMLC tracking strategy is to warp the planned beam aperture directly to conform to the new tumor shape based on real time tumor deformation input. Two deformable phantoms that correspond to a single tumor and a tumor system were developed. The planar deformations derived from the phantom images in beams eye view were used to guide the aperture warping. An in-house deformable image registration software was developed to automatically trigger the registration once new target image was acquired and send the computed deformation to the DMLC tracking software. Because the registration speed is not fast enough to implement the experiment in real-time manner, the phantom deformation only proceeded to the next position until registration of the current deformation position was completed. The deformation tracking accuracy was evaluated by a geometric target coverage metric defined as the sum of the area incorrectly outside and inside the ideal aperture. The individual contributions from the deformable registration algorithm and the finite leaf width to the tracking uncertainty were analyzed. Clinical proof-of-principle experiment of deformation tracking using previously acquired MR images of a lung cancer patient was implemented to represent the MRI-Linac environment. Intensity-modulated radiation therapy (IMRT) treatment delivered with enabled deformation tracking was simulated and demonstrated. RESULTS The first experimental investigation of adapting to tumor deformation has been performed using simple deformable phantoms. For the single tumor deformation, the A(u)+A(o) was reduced over 56% when deformation was larger than 2 mm. Overall, the total improvement was 82%. For the tumor system deformation, the A(u)+A(o) reductions were all above 75% and the total A(u)+A(o) improvement was 86%. Similar coverage improvement was also found in simulating deformation tracking during IMRT delivery. The deformable image registration algorithm was identified as the dominant contributor to the tracking error rather than the finite leaf width. The discrepancy between the warped beam shape and the ideal beam shape due to the deformable registration was observed to be partially compensated during leaf fitting due to the finite leaf width. The clinical proof-of-principle experiment demonstrated the feasibility of intrafraction deformable tracking for clinical scenarios. CONCLUSIONS For the first time, we developed and demonstrated an experimental system that is capable of adapting the MLC aperture to account for tumor deformation. This work provides a potentially widely available management method to effectively account for intrafractional tumor deformation. This proof-of-principle study is the first experimental step toward the development of an image-guided radiotherapy system to treat deforming tumors in real-time.

Built-in electric fields and valence band offsets in InN/GaN(0001) superlattices: First-principles investigations

Based on all-electron density functional theory calculations, we systematically investigate the built-in electric fields and valence band offsets in wurtzite InN/GaN(0001) superlattices, where their correlations with biaxial strain, as well as the superlattice geometry, are determined. Both the built-in electric fields (several MV/cm) and the valence band offsets (0.16 –1.1 eV) are found to be strongly dependent on the superlattice geometry and strain growth conditions. Spontaneous polarization and strain-induced piezoelectric polarization are comparable in contribution to the macroscopic electric field. Relative to the fully relaxed superlattices, tensile (compressive) strain significantly weakens (strengthens) the magnitude of the electric field, and decreases (increases) the value of the valence band offset. The results will be valuable in relation to practical heterojunction-based device optimization and design.

Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy.

The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2-9.9 mm, which was 0.4-2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.

Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR).

Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp-Davis-Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and did not suffer from residual noise/streaking and motion blur migrated from the prior image as in PICCS. AAIR was also found to be more computationally efficient than both ASD-POCS and PICCS, with a reduction in computation time of over 50% compared to ASD-POCS. The use of anatomy segmentation was, for the first time, demonstrated to significantly improve image quality and computational efficiency for thoracic 4D CBCT reconstruction. Further developments are required to facilitate AAIR for practical use.

Optimizing 4DCBCT projection allocation to respiratory bins

4D cone beam computed tomography (4DCBCT) is an emerging image guidance strategy used in radiotherapy where projections acquired during a scan are sorted into respiratory bins based on the respiratory phase or displacement. 4DCBCT reduces the motion blur caused by respiratory motion but increases streaking artefacts due to projection under-sampling as a result of the irregular nature of patient breathing and the binning algorithms used. For displacement binning the streak artefacts are so severe that displacement binning is rarely used clinically. The purpose of this study is to investigate if sharing projections between respiratory bins and adjusting the location of respiratory bins in an optimal manner can reduce or eliminate streak artefacts in 4DCBCT images. We introduce a mathematical optimization framework and a heuristic solution method, which we will call the optimized projection allocation algorithm, to determine where to position the respiratory bins and which projections to source from neighbouring respiratory bins. Five 4DCBCT datasets from three patients were used to reconstruct 4DCBCT images. Projections were sorted into respiratory bins using equispaced, equal density and optimized projection allocation. The standard deviation of the angular separation between projections was used to assess streaking and the consistency of the segmented volume of a fiducial gold marker was used to assess motion blur. The standard deviation of the angular separation between projections using displacement binning and optimized projection allocation was 30%-50% smaller than conventional phase based binning and 59%-76% smaller than conventional displacement binning indicating more uniformly spaced projections and fewer streaking artefacts. The standard deviation in the marker volume was 20%-90% smaller when using optimized projection allocation than using conventional phase based binning suggesting more uniform marker segmentation and less motion blur. Images reconstructed using displacement binning and the optimized projection allocation algorithm were clearer, contained visibly fewer streak artefacts and produced more consistent marker segmentation than those reconstructed with either equispaced or equal-density binning. The optimized projection allocation algorithm significantly improves image quality in 4DCBCT images and provides, for the first time, a method to consistently generate high quality displacement binned 4DCBCT images in clinical applications.

A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy

The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with  >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude  <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

The first implementation of respiratory triggered 4DCBCT on a linear accelerator.

Four dimensional cone beam computed tomography (4DCBCT) is an image guidance strategy used for patient positioning in radiotherapy. In conventional implementations of 4DCBCT, a constant gantry speed and a constant projection pulse rate are used. Unfortunately, this leads to higher imaging doses than are necessary because a large number of redundant projections are acquired. In theoretical studies, we have previously demonstrated that by suppressing redundant projections the imaging dose can be reduced by 40-50% for a majority of patients with little reduction in image quality. The aim of this study was to experimentally realise the projection suppression technique, which we have called Respiratory Triggered 4DCBCT (RT-4DCBCT). A real-time control system was developed that takes the respiratory signal as input and computes whether to acquire, or suppress, the next projection trigger during 4DCBCT acquisition. The CIRS dynamic thorax phantom was programmed with a 2 cm peak-to-peak motion and periods ranging from 2 to 8 s. Image quality was assessed by computing the edge response width of a 3 cm imaging insert placed in the phantom as well as the signal to noise ratio of the phantoms tissue and the contrast to noise ratio between the phantoms lung and tissue. The standard deviation in the superior-inferior direction of the 3 cm imaging insert was used to assess intra-phase bin displacement variations with a higher standard deviation implying more motion blur. The 4DCBCT imaging dose was reduced by 8.6%, 41%, 54%, 70% and 77% for patients with 2, 3, 4, 6 and 8 s breathing periods respectively when compared to conventional 4DCBCT. The standard deviation of the intra-phase bin displacement variation of the 3 cm imaging insert was reduced by between 13% and 43% indicating a more consistent position for the projections within respiratory phases. For the 4 s breathing period, the edge response width was reduced by 39% (0.8 mm) with only a 6-7% decrease in the signal to noise and contrast to noise ratios. RT-4DCBCT has been experimentally realised and reduced to practice on a linear accelerator with a measurable imaging dose reductions over conventional 4DCBCT and little degradation in image quality.

Quantifying the image quality and dose reduction of respiratory triggered 4D cone-beam computed tomography with patient-measured breathing.

Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patients respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations. Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique. A set containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 pairs of RT 4D CBCT and conventional 4D CBCT image sets from realistic simulations of a 4D CBCT system using a Rando phantom and the digital phantom, XCAT. Each of these image sets were compared to a ground truth dataset from which a mean absolute pixel difference (MAPD) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation was counted and was assumed as a surrogate for imaging dose. Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT, the average image quality was reduced by 7.6% (Rando study) and 11.1% (XCAT study). However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). The simulation studies have demonstrated that the RT 4D CBCT method can potentially offer a 53% saving in imaging dose on average compared to conventional 4D CBCT in simulation studies using a wide range of patient-measured breathing traces with a minimal impact on image quality.

Quantifying the reproducibility of lung ventilation images between 4‐Dimensional Cone Beam CT and 4‐Dimensional CT

Purpose Computed tomography ventilation imaging derived from four‐dimensional cone beam CT (CTVI4DCBCT) can complement existing 4DCT‐based methods (CTVI4DCT) to track lung function changes over a course of lung cancer radiation therapy. However, the accuracy of CTVI4DCBCT needs to be assessed since anatomic 4DCBCT has demonstrably poor image quality and small field of view (FOV) compared to treatment planning 4DCT. We perform a direct comparison between short interval CTVI4DCBCT and CTVI4DCT pairs to understand the patient specific image quality factors affecting the intermodality CTVI reproducibility in the clinic. Methods and materials We analysed 51 pairs of 4DCBCT and 4DCT scans acquired within 1 day of each other for nine lung cancer patients. To assess the impact of image quality, CTVIs were derived from 4DCBCT scans reconstructed using both standard Feldkamp‐Davis‐Kress backprojection (Symbol) and an iterative McKinnon‐Bates Simultaneous Algebraic Reconstruction Technique (Symbol). Also, the influence of FOV was assessed by deriving CTVIs from 4DCT scans that were cropped to a similar FOV as the 4DCBCT scans (Symbol), or uncropped (Symbol). All CTVIs were derived by performing deformable image registration (DIR) between the exhale and inhale phases and evaluating the Jacobian determinant of deformation. Reproducibility between corresponding CTVI4DCBCT and CTVI4DCT pairs was quantified using the voxel‐wise Spearman rank correlation and the Dice similarity coefficient (DSC) for ventilation defect regions (identified as the lower quartile of ventilation values). Mann–Whitney U‐tests were applied to determine statistical significance of each reconstruction and cropping condition. Symbol. No caption available. Symbol. No caption available. Symbol. No caption available. Symbol. No caption available. Results The (mean ± SD) Spearman correlation between Symbol and Symbol was 0.60 ± 0.23 (range −0.03–0.88) and the DSC was 0.64 ± 0.12 (0.34–0.83). By comparison, correlations between Symbol and Symbol showed a small but statistically significant improvement with = 0.64 ± 0.20 (range 0.06–0.90, P = 0.03) and DSC = 0.66 ± 0.13 (0.31–0.87, P = 0.02). Intermodal correlations were noted to decrease with an increasing fraction of lung truncation in 4DCBCT relative to 4DCT, albeit not significantly (Pearson correlation R = 0.58, P = 0.002). Symbol. No caption available. Symbol. No caption available. Symbol. No caption available. Symbol. No caption available. Conclusions This study demonstrates that DIR based CTVIs derived from 4DCBCT can exhibit reasonable to good voxel‐level agreement with CTVIs derived from 4DCT. These correlations outperform previous cross‐modality comparisons between 4DCT‐based ventilation and nuclear medicine. The use of 4DCBCT scans with iterative reconstruction and minimal lung truncation is recommended to ensure better reproducibility between 4DCBCT‐ and 4DCT‐based CTVIs.

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

PURPOSE The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. METHODS AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. RESULTS For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*104 vs. 1.4*104 ). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. CONCLUSIONS The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.