The effects of flattening filter-free beams and aperture shape controller on the complexity of conventional large-field treatment plans.

PURPOSE: The purpose of this study was to investigate the impact of using flattening filter-free (FFF) beams and the aperture shape controller (ASC) on the complexity of conventional large-field treatment plans. METHODS AND MATERIALS: A total of 24 head and neck (H&N) and 24 prostate with pelvic nodes treatment plans were used in this study. Each plan was reoptimized using the original clinical objectives with both flattened and FFF beams, as well as six different ASC settings. The dosimetric qualities of each plan cohort were evaluated using commonly used dose-volume histogram values, and plan complexities were assessed through metrics including monitor unit (MU)/Dose, change in gantry speed, multileaf collimator (MLC) speed, the edge area ratio metric (EM), and the equivalent square length. RESULTS: No significant differences in dosimetric qualities were found between plans with flattened and FFF beams. The ASC settings did not have significant effects on dosimetric qualities in the H&N plan cohort, but the "very high" ASC setting resulted in poorer dosimetric results for the prostate plans. Plans with FFF beams had significantly higher MU/Dose compared to plans with flattened beams. The use of flattening filter (FF) had significant effects on the change in gantry speed, with flattened beams producing plans that required higher change in gantry speed. However, the FF did not have significant effects on MLC speed, EM, or equivalent square length. In contrast, ASC settings had significant effects on these three metrics; increasing the ASC level resulted in plans with decreasing MLC speed, lower edge area ratio, and higher equivalent square length. CONCLUSION: This study demonstrated that using FFF beams with various ASC settings, except for the "very high" level, can produce plans with reduced complexities without compromising dosimetric qualities in conventional large-field treatment plans.

Knowledge-based quality control of organ delineations in radiation therapy.

PURPOSE: To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed. METHOD AND MATERIALS: The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10-fold cross-validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts. RESULTS: The accuracy of the classifiers was ≥ $\ge$ 99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected > $>$ 79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set. CONCLUSION: The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities.

Detection of dose delivery variations on TomoTherapy using on-board detector based verification.

A clinical case of delivery dose deviations on a TomoTherapy treatment was discovered during a patient specific treatment quality assurance (QA) verification. An in-house developed QA system, MCLogQA, for TomoTherapy has been implemented in our clinic for patient specific treatment QA. The MCLogQA system utilizes the log file and detector-based multileaf collimator (MLC) leaf opening time (LOT) to assess accuracy of treatment plan delivery. Recently, the MCLogQA system discovered >10% dose deviation for a low dose/fraction treatment plan. To verify the adequacy of the MCLogQA result, a delivery quality assurance (DQA) plan was created and performed. The treatment plan was also transferred to a second TomoTherapy unit and planning system to investigate if the plan-delivery deviation was unit dependent. Further testing was carried out in phantom plans. MCLogQA showed MLC LOT was on average 2.4% higher than the planned LOT, resulting in 3.5% increase in mean dose, and 14% increase in dose to 1 cc volume of max dose in PTV. Independent DQA verification confirmed the MCLogQA result. For the transferred treatment plan delivery, the MCLogQA also showed an average increase of 6.6% in MLC LOT, resulting in increases in mean dose by 9.3% and dose to 1 cc volume of max dose in PTV by 16%. The inaccurate MLC LOT was a result of a poor latency model at very small LOT. Phantom testing confirmed low LOT will result in relatively large dosimetric variation, and detector-based MCLogQA will detect differences in planned and measured LOT. Accuracy in TomoTherapy treatment delivery can be susceptible to LOT uncertainty. Using MCLogQA for QA verification not only validates the treatment delivery, but also provides information on LOT variation and comprehensive dose distribution. This information can help decision making when large plan-delivery deviation occurs.

A Novel Small-Animal Model of Irradiated, Implant-Based Breast Reconstruction.

BACKGROUND: There is currently a need for a clinically relevant small-animal model for irradiated, implant-based breast reconstruction. Present models are inadequate in terms of suboptimal location of expander placement and mode of radiation delivery, correlating poorly with the human clinical scenario. The authors hypothesized that by delivering fractionated radiation and placing an expander under the scalp of the animal, they would achieve soft-tissue changes histologically analogous to those seen in human irradiated, implant-based breast reconstruction. METHODS: This study consisted of 11 immunocompetent, hairless rats divided into three groups as follows: untreated control (n = 3), tissue-expanded scalps (n = 4), and fractionated irradiation plus tissue expansion of the scalp (n = 4). At the completion of the experiment for each group, skin tissue samples were analyzed histologically for vascularity, epidermal and dermal thickness, and collagen fiber alignment or scar formation. RESULTS: Expanded rat epidermis was significantly thicker and dermis was more vascular than nonexpanded skin. The authors observed a greater degree of collagen fiber alignment in the expanded group compared with nonexpanded skin. The combination of irradiation and expansion resulted in significant dermal thinning, vascular depletion, and increased scar formation compared with expanded skin alone. CONCLUSIONS: The authors describe a novel small-animal model for irradiated, implant-based breast reconstruction where histologic analysis shows structural changes in the skin consistent with known effects of radiation therapy and expansion in human skin. This model represents a significant improvement from previous ones and, as such, holds the potential to be used to test new therapeutic agents to improve clinical outcomes.

Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach.

PURPOSE: To develop a quality assurance (QA) tool that identifies inaccurate organ at risk (OAR) delineations. METHODS: The QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three-dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OARs included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool. RESULTS: In the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors (P = 0.17), and from 78% to 87% for major errors (P = 0.02). CONCLUSIONS: The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.

Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints.

PURPOSE: To determine if radiation treatment plans created based on autosegmented (AS) regions-of-interest (ROI)s are clinically equivalent to plans created based on manually segmented ROIs, where equivalence is evaluated using probabilistic dosimetric metrics and probabilistic biological endpoints for prostate IMRT. METHOD AND MATERIALS: Manually drawn contours and autosegmented ROIs were created for 167 CT image sets acquired from 19 prostate patients. Autosegmentation was performed utilizing Pinnacle's Smart Probabilistic Image Contouring Engine. For each CT set, 78 Gy/39 fraction 7-beam IMRT treatment plans with 1 cm CTV-to-PTV margins were created for each of the three contour scenarios; PMD using manually delineated (MD) ROIs, PAS using autosegmented ROIs, and PAM using autosegmented organ-at-risks (OAR)s and the manually drawn target. For each plan, 1000 virtual treatment simulations with different systematic errors for each simulation and a different random error for each fraction were performed. The statistical probability of achieving dose-volume metrics (coverage probability (CP)), expectation values for normal tissue complication probability (NTCP), and tumor control probability (TCP) metrics for all possible cross-evaluation pairs of ROI types and planning scenarios were reported. In evaluation scenarios, the root mean square loss (RMSL) and maximum absolute loss (MAL) of coverage probability of dose-volume objectives, E[TCP], and E[NTCP] were compared with respect to the base plan created and evaluated with manually drawn contours. RESULTS: Femoral head dose objectives were satisfied in all situations, as well as the maximum dose objectives for all ROIs. Bladder metrics were within the clinical coverage tolerances except D35Gy for the autosegmented plan evaluated with the manual contours. Dosimetric indices for CTV and rectum could be highly compromised when the definition of the ROIs switched from manually delineated to autosegmented. Seventy-two percent of CT image sets satisfied the worst-case CP thresholds for all dosimetric objectives in all scenarios, the percentage dropped to 50% if biological indices were taken into account. Among evaluation scenarios, (MD,PAM ) bore the highest resemblance to (MD,PMD ) where 99% and 88% of cases met all CP thresholds for bladder and rectum, respectively. CONCLUSIONS: When including daily setup variations in prostate IMRT, the dose-volume metric CP, and biological indices of ROIs were approximately equivalent for the plans created based on manually drawn targets and autosegmented OARs in 88% of cases. The accuracy of autosegmented prostates and rectums are impediment to attain statistically equivalent plans created based on manually drawn ROIs.

4D MR imaging using robust internal respiratory signal.

The purpose of this study is to investigate the feasibility of using internal respiratory (IR) surrogates to sort four-dimensional (4D) magnetic resonance (MR) images. The 4D MR images were constructed by acquiring fast 2D cine MR images sequentially, with each slice scanned for more than one breathing cycle. The 4D volume was then sorted retrospectively using the IR signal. In this study, we propose to use multiple low-frequency components in the Fourier space as well as the anterior body boundary as potential IR surrogates. From these potential IR surrogates, we used a clustering algorithm to identify those that best represented the respiratory pattern to derive the IR signal. A study with healthy volunteers was performed to assess the feasibility of the proposed IR signal. We compared this proposed IR signal with the respiratory signal obtained using respiratory bellows. Overall, 99% of the IR signals matched the bellows signals. The average difference between the end inspiration times in the IR signal and bellows signal was 0.18 s in this cohort of matching signals. For the acquired images corresponding to the other 1% of non-matching signal pairs, the respiratory motion shown in the images was coherent with the respiratory phases determined by the IR signal, but not the bellows signal. This suggested that the IR signal determined by the proposed method could potentially correct the faulty bellows signal. The sorted 4D images showed minimal mismatched artefacts and potential clinical applicability. The proposed IR signal therefore provides a feasible alternative to effectively sort MR images in 4D.

Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features.

PURPOSE: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images. METHODS: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients. RESULTS: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors' proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δtins) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δtins were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δtins was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δtins greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal. CONCLUSIONS: The authors' proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.

Fast range measurement of spot scanning proton beams using a volumetric liquid scintillator detector.

Accurate confirmation and verification of the range of spot scanning proton beams is crucial for correct dose delivery. Current methods to measure proton beam range using ionization chambers are either time-consuming or result in measurements with poor spatial resolution. The large-volume liquid scintillator detector allows real-time measurements of the entire dose profile of a spot scanning proton beam. Thus, liquid scintillator detectors are an ideal tool for measuring the proton beam range for commissioning and quality assurance. However, optical artefacts may decrease the accuracy of measuring the proton beam range within the scintillator tank. The purpose of the current study was to 1) develop a geometric calibration system to accurately calculate physical distances within the liquid scintillator detector, taking into account optical artefacts; and 2) assess the accuracy, consistency, and robustness of proton beam range measurement using the liquid scintillator detector with our geometric calibration system. The range of the proton beam was measured with the calibrated liquid scintillator system and was compared to the nominal range. Measurements were made on three different days to evaluate the setup robustness from day to day, and three sets of measurements were made for each day to evaluate the consistency from delivery to delivery. All proton beam ranges measured using the liquid scintillator system were within half a millimeter of the nominal range. The delivery-to-delivery standard deviation of the range measurement was 0.04 mm, and the day-to-day standard deviation was 0.10 mm. In addition to the accuracy and robustness demonstrated by these results when our geometric calibration system was used, the liquid scintillator system allowed the range of all 94 proton beams to be measured in just two deliveries, making the liquid scintillator detector a perfect tool for range measurement of spot scanning proton beams.

3D reconstruction of scintillation light emission from proton pencil beams using limited viewing angles-a simulation study.

An accurate and high-resolution quality assurance (QA) method for proton radiotherapy beams is necessary to ensure correct dose delivery to the target. Detectors based on a large volume of liquid scintillator have shown great promise in providing fast and high-resolution measurements of proton treatment fields. However, previous work with these detectors has been limited to two-dimensional measurements, and the quantitative measurement of dose distributions was lacking. The purpose of the current study is to assess the feasibility of reconstructing three-dimensional (3D) scintillation light distributions of spot scanning proton beams using a scintillation system. The proposed system consists of a tank of liquid scintillator imaged by charge-coupled device cameras at three orthogonal viewing angles. Because of the limited number of viewing angles, we developed a profile-based technique to obtain an initial estimate that can improve the quality of the 3D reconstruction. We found that our proposed scintillator system and profile-based technique can reconstruct a single energy proton beam in 3D with a gamma passing rate (3%/3 mm local) of 100.0%. For a single energy layer of an intensity modulated proton therapy prostate treatment plan, the proposed method can reconstruct the 3D light distribution with a gamma pass rate (3%/3 mm local) of 99.7%. In addition, we also found that the proposed method is effective in detecting errors in the treatment plan, indicating that it can be a very useful tool for 3D proton beam QA.

Optical artefact characterization and correction in volumetric scintillation dosimetry.

The goals of this study were (1) to characterize the optical artefacts affecting measurement accuracy in a volumetric liquid scintillator detector, and (2) to develop methods to correct for these artefacts. The optical artefacts addressed were photon scattering, refraction, camera perspective, vignetting, lens distortion, the lens point spread function, stray radiation, and noise in the camera. These artefacts were evaluated by theoretical and experimental means, and specific correction strategies were developed for each artefact. The effectiveness of the correction methods was evaluated by comparing raw and corrected images of the scintillation light from proton pencil beams against validated Monte Carlo calculations. Blurring due to the lens and refraction at the scintillator tank-air interface were found to have the largest effect on the measured light distribution, and lens aberrations and vignetting were important primarily at the image edges. Photon scatter in the scintillator was not found to be a significant source of artefacts. The correction methods effectively mitigated the artefacts, increasing the average gamma analysis pass rate from 66% to 98% for gamma criteria of 2% dose difference and 2 mm distance to agreement. We conclude that optical artefacts cause clinically meaningful errors in the measured light distribution, and we have demonstrated effective strategies for correcting these optical artefacts.

Improved three-dimensional Look-Locker acquisition scheme and angle map filtering procedure for T1 estimation.

The three-dimensional (3D) Look-Locker (LL) acquisition is a widely used fast and efficient T1 mapping method. However, the multi-shot approach of 3D LL acquisition can introduce reconstruction artifacts that result in intensity distortions. Traditional 3D LL acquisition generally utilizes a centric encoding scheme that is limited to a single phase-encoding direction in k space. To optimize k-space segmentation, an elliptical scheme with two phase-encoding directions is implemented for the LL acquisition. This elliptical segmentation can reduce the intensity errors in the reconstructed images and improve the final T1 estimation. One of the major sources of error in LL-based T1 estimation is a lack of accurate knowledge of the actual flip angle. A multi-parameter curve-fitting procedure can account for some of the variability in the flip angle. However, curve fitting can also introduce errors in the estimated flip angle that can result in incorrect T1 values. A filtering procedure based on goodness of fit (GOF) is proposed to reduce the effect of false flip angle estimates. Filtering based on GOF weighting can remove probable incorrect angles that result in bad curve fitting. Simulation, phantom and in vivo studies have demonstrated that these techniques can improve the accuracy of 3D LL T1 estimation.

Fast algorithm for calculation of inhomogeneity gradient in magnetic resonance imaging data.

PURPOSE: To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. MATERIALS AND METHODS: The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data. RESULTS: The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3. CONCLUSION: The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals.