Volume 42, Issue 8, August 2015
Index of content:
42(2015); http://dx.doi.org/10.1118/1.4922709View Description Hide Description
- RADIATION THERAPY PHYSICS
Semiautomated head-and-neck IMRT planning using dose warping and scaling to robustly adapt plans in a knowledge database containing potentially suboptimal plans42(2015); http://dx.doi.org/10.1118/1.4923174View Description Hide DescriptionPurpose:
Prior work by the authors and other groups has studied the creation of automated intensity modulated radiotherapy (IMRT) plans of equivalent quality to those in a patient database of manually created clinical plans; those database plans provided guidance on the achievable sparing to organs-at-risk (OARs). However, in certain sites, such as head-and-neck, the clinical plans may not be sufficiently optimized because of anatomical complexity and clinical time constraints. This could lead to automated plans that suboptimally exploit OAR sparing. This work investigates a novel dose warping and scaling scheme that attempts to reduce effects of suboptimal sparing in clinical database plans, thus improving the quality of semiautomated head-and-neck cancer (HNC) plans.Methods:
Knowledge-based radiotherapy (KBRT) plans for each of ten “query” patients were semiautomatically generated by identifying the most similar “match” patient in a database of 103 clinical manually created patient plans. The match patient’s plans were adapted to the query case by: (1) deforming the match beam fluences to suit the query target volume and (2) warping the match primary/boost dose distribution to suit the query geometry and using the warped distribution to generate query primary/boost optimization dose-volume constraints. Item (2) included a distance scaling factor to improve query OAR dose sparing with respect to the possibly suboptimal clinical match plan. To further compensate for a component plan of the match case (primary/boost) not optimally sparing OARs, the query dose volume constraints were reduced using a dose scaling factor to be the minimum from either (a) the warped component plan (primary or boost) dose distribution or (b) the warped total plan dose distribution (primary + boost) scaled in proportion to the ratio of component prescription dose to total prescription dose. The dose-volume constraints were used to plan the query case with no human intervention to adjust constraints during plan optimization.Results:
KBRT and original clinical plans were dosimetrically equivalent for parotid glands (mean/median doses), spinal cord, and brainstem (maximum doses). KBRT plans significantly reduced larynx median doses (21.5 ± 6.6 Gy to 17.9 ± 3.9 Gy), and oral cavity mean (32.3 ± 6.2 Gy to 28.9 ± 5.4 Gy) and median (28.7 ± 5.7 Gy to 23.2 ± 5.3 Gy) doses. Doses to ipsilateral parotid gland, larynx, oral cavity, and brainstem were lower or equivalent in the KBRT plans for the majority of cases. By contrast, KBRT plans generated without the dose warping and dose scaling steps were not significantly different from the clinical plans.Conclusions:
Fast, semiautomatically generated HNC IMRT plans adapted from existing plans in a clinical database can be of equivalent or better quality than manually created plans. The reductions in OAR doses in the semiautomated plans, compared to the clinical plans, indicate that the proposed dose warping and scaling method shows promise in mitigating the impact of suboptimal clinical plans.
Methods, software and datasets to verify DVH calculations against analytical values: Twenty years late(r)42(2015); http://dx.doi.org/10.1118/1.4923175View Description Hide DescriptionPurpose:
The authors designed data, methods, and metrics that can serve as a standard, independent of any software package, to evaluate dose-volume histogram (DVH) calculation accuracy and detect limitations. The authors use simple geometrical objects at different orientations combined with dose grids of varying spatial resolution with linear 1D dose gradients; when combined, ground truth DVH curves can be calculated analytically in closed form to serve as the absolute standards.Methods:
dicom RT structure sets containing a small sphere, cylinder, and cone were created programmatically with axial plane spacing varying from 0.2 to 3 mm. Cylinders and cones were modeled in two different orientations with respect to the IEC 1217 Y axis. The contours were designed to stringently but methodically test voxelation methods required for DVH. Synthetic RT dose files were generated with 1D linear dose gradient and with grid resolution varying from 0.4 to 3 mm. Two commercial DVH algorithms—pinnacle (Philips Radiation Oncology Systems) and PlanIQ (Sun Nuclear Corp.)—were tested against analytical values using custom, noncommercial analysis software. In Test 1, axial contour spacing was constant at 0.2 mm while dose grid resolution varied. In Tests 2 and 3, the dose grid resolution was matched to varying subsampled axial contours with spacing of 1, 2, and 3 mm, and difference analysis and metrics were employed: (1) histograms of the accuracy of various DVH parameters (total volume, D max, D min, and doses to % volume: D99, D95, D5, D1, D0.03 cm3) and (2) volume errors extracted along the DVH curves were generated and summarized in tabular and graphical forms.Results:
In Test 1, pinnacle produced 52 deviations (15%) while PlanIQ produced 5 (1.5%). In Test 2, pinnacle and PlanIQ differed from analytical by >3% in 93 (36%) and 18 (7%) times, respectively. Excluding D min and D max as least clinically relevant would result in 32 (15%) vs 5 (2%) scored deviations for pinnacle vs PlanIQ in Test 1, while Test 2 would yield 53 (25%) vs 17 (8%). In Test 3, statistical analyses of volume errors extracted continuously along the curves show pinnacle to have more errors and higher variability (relative to PlanIQ), primarily due to pinnacle’s lack of sufficient 3D grid supersampling. Another major driver for pinnacle errors is an inconsistency in implementation of the “end-capping”; the additional volume resulting from expanding superior and inferior contours halfway to the next slice is included in the total volume calculation, but dose voxels in this expanded volume are excluded from the DVH. PlanIQ had fewer deviations, and most were associated with a rotated cylinder modeled by rectangular axial contours; for coarser axial spacing, the limited number of cross-sectional rectangles hinders the ability to render the true structure volume.Conclusions:
The method is applicable to any DVH-calculating software capable of importing dicom RT structure set and dose objects (the authors’ examples are available for download). It includes a collection of tests that probe the design of the DVH algorithm, measure its accuracy, and identify failure modes. Merits and applicability of each test are discussed.
Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy42(2015); http://dx.doi.org/10.1118/1.4923179View Description Hide DescriptionPurpose:
To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy.Methods:
CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCT us) and a priori CT-based scatter correction (CBCT ap). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCT us, while no HU change was applied to the CBCT ap. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CT ref) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCT ap was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images.Results:
The uncorrected CBCT and CBCT us images demonstrated substantial WEPL discrepancies (7.3 ± 5.3 mm and 11.1 ± 6.6 mm, respectively) with respect to the CT ref, while the CBCT ap images showed substantially reduced WEPL errors (2.4 ± 2.0 mm). Similarly, the CBCT ap-based treatment plans demonstrated a high pass rate (96.0% ± 2.5% in 2 mm/2% criteria) in a 3D gamma analysis.Conclusions:
A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms.
- RADIATION IMAGING PHYSICS
Technical Note: Impact on detective quantum efficiency of edge angle determination method by International Electrotechnical Commission methodology for cardiac x-ray image detectors42(2015); http://dx.doi.org/10.1118/1.4923178View Description Hide DescriptionPurpose:
Cardiac x-ray detectors are used to acquire moving images in real-time for angiography and interventional procedures. Detective quantum efficiency (DQE) is not generally measured on these dynamic detectors; the required “for processing” image data and control of x-ray settings have not been accessible. By 2016, USA hospital physicists will have the ability to measure DQE and will likely utilize the International Electrotechnical Commission (IEC) standard for measuring DQE of dynamic x-ray imaging devices. The current IEC standard requires an image of a tilted tungsten edge test object to obtain modulation transfer function (MTF) for DQE calculation. It specifies the range of edge angles to use; however, it does not specify a preferred method to determine this angle for image analysis. The study aimed to answer the question “will my choice in method impact my results?” Four different established edge angle determination methods were compared to investigate the impact on DQE.Methods:
Following the IEC standard, edge and flat field images were acquired on a cardiac flat-panel detector to calculate MTF and noise power spectrum, respectively, to determine DQE. Accuracy of the methods in determining the correct angle was ascertained using a simulated edge image with known angulations. Precision of the methods was ascertained using variability of MTF and DQE, calculated via bootstrapping.Results:
Three methods provided near equal angles and the same MTF while the fourth, with an angular difference of 6%, had a MTF lower by 3% at 1.5 mm−1 spatial frequency and 8% at 2.5 mm−1; corresponding DQE differences were 6% at 1.5 mm−1 and 17% at 2.5 mm−1; differences were greater than standard deviations in the measurements.Conclusions:
DQE measurements may vary by a significant amount, depending on the method used to determine the edge angle when following the IEC standard methodology for a cardiac x-ray detector. The most accurate and precise methods are recommended for absolute assessments and reproducible measurements, respectively.
- MAGNETIC RESONANCE PHYSICS
42(2015); http://dx.doi.org/10.1118/1.4923168View Description Hide DescriptionPurpose:
T2-weighted MRI provides excellent tumor-to-tissue contrast for target volume delineation in radiation therapy treatment planning. This study aims at developing a novel T2-weighted retrospective four dimensional magnetic resonance imaging (4D-MRI) phase sorting technique for imaging organ/tumor respiratory motion.Methods:
A 2D fast T2-weighted half-Fourier acquisition single-shot turbo spin-echo MR sequence was used for image acquisition of 4D-MRI, with a frame rate of 2–3 frames/s. Respiratory motion was measured using an external breathing monitoring device. A phase sorting method was developed to sort the images by their corresponding respiratory phases. Besides, a result-driven strategy was applied to effectively utilize redundant images in the case when multiple images were allocated to a bin. This strategy, selecting the image with minimal amplitude error, will generate the most representative 4D-MRI. Since we are using a different image acquisition mode for 4D imaging (the sequential image acquisition scheme) with the conventionally used cine or helical image acquisition scheme, the 4D dataset sufficient condition was not obviously and directly predictable. An important challenge of the proposed technique was to determine the number of repeated scans (NR ) required to obtain sufficient phase information at each slice position. To tackle this challenge, the authors first conducted computer simulations using real-time position management respiratory signals of the 29 cancer patients under an IRB-approved retrospective study to derive the relationships between NR and the following factors: number of slices (NS ), number of 4D-MRI respiratory bins (NB ), and starting phase at image acquisition (P 0). To validate the authors’ technique, 4D-MRI acquisition and reconstruction were simulated on a 4D digital extended cardiac-torso (XCAT) human phantom using simulation derived parameters. Twelve healthy volunteers were involved in an IRB-approved study to investigate the feasibility of this technique.Results:
4D data acquisition completeness (Cp ) increases as NR increases in an inverse-exponential fashion (Cp = 100 − 99 × exp(−0.18 × NR ), when NB = 6, fitted using 29 patients’ data). The NR required for 4D-MRI reconstruction (defined as achieving 95% completeness, Cp = 95%, NR = N R,95) is proportional to NB (N R,95 ∼ 2.86 × NB , r = 1.0), but independent of NS and P 0. Simulated XCAT 4D-MRI showed a clear pattern of respiratory motion. Tumor motion trajectories measured on 4D-MRI were comparable to the average input signal, with a mean relative amplitude error of 2.7% ± 2.9%. Reconstructed 4D-MRI for healthy volunteers illustrated clear respiratory motion on three orthogonal planes, with minimal image artifacts. The artifacts were presumably caused by breathing irregularity and incompleteness of data acquisition (95% acquired only). The mean relative amplitude error between critical structure trajectory and average breathing curve for 12 healthy volunteers is 2.5 ± 0.3 mm in superior–inferior direction.Conclusions:
A novel T2-weighted retrospective phase sorting 4D-MRI technique has been developed and successfully applied on digital phantom and healthy volunteers.