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At least 1,045 records · Page 58
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DOE OSTI2016

Generic and robust method for automatic segmentation of PET images using an active contour model

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions: The authors’ results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors’ study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Backscatter dose effects for high atomic number materials being irradiated in the presence of a magnetic field: A Monte Carlo study for the MRI linac

Purpose: To quantify and explain the backscatter dose effects for clinically relevant high atomic number materials being irradiated in the presence of a 1.5 T transverse magnetic field. Methods: Interface effects were investigated using Monte Carlo simulation techniques. We used GPUMCD (v5.1) and GEANT4 (v10.1) for this purpose. GPUMCD is a commercial software written for the Elekta AB, MRI linac. Dose was scored using GPUMCD in cubic voxels of side 1 and 0.5 mm, in two different virtual phantoms of dimensions 20 × 20 × 20 cm and 5 × 5 × 13.3 cm, respectively. A photon beam was generated from a point 143.5 cm away from the isocenter with energy distribution sampled from a histogram representing the true Elekta, MRI linac photon spectrum. A slab of variable thickness and position containing either bone, aluminum, titanium, stainless steel, or one of the two different dental filling materials was inserted as an inhomogeneity in the 20 × 20 × 20 cm phantom. The 5 × 5 × 13.3 cm phantom was used as a clinical test case in order to explain the dose perturbation effects for a head and neck cancer patient. The back scatter dose factor (BSDF) was defined as the ratio of the doses at a given depth with and without the presence of the inhomogeneity. Backscattered electron fluence was calculated at the inhomogeneity interface using GEANT4. A 1.5 T magnetic field was applied perpendicular to the direction of the beam in both phantoms, identical to the geometry in the Elekta MRI linac. Results: With the application of a 1.5 T magnetic field, all the BSDF’s were reduced by 12%–47%, compared to the no magnetic field case. The corresponding backscattered electron fluence at the interface was also reduced by 45%–64%. The reduction in the BSDF at the interface, due to the application of the magnetic field, is manifested in a different manner for each material. In the case of bone, the dose drops at the interface contrary to the expected increase when no magnetic field is applied. In the case of aluminum, the dose at the interface is the same with and without the presence of the aluminum. For all of the other materials the dose increases at the interface. Conclusions: The reduction in dose at the interface, in the presence of the magnetic field, is directly related to the reduction in backscattered electron fluence. This reduction occurs due to two different reasons. First, the electron spectrum hitting the interface is changed when the magnetic field is turned on, which results in changes in the electron scattering probability. Second, some electrons that have curved trajectories due to the presence of the magnetic field are absorbed by the higher density side of the interface and no longer contribute to the backscattered electron fluence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Modification and validation of an analytical source model for external beam radiotherapy Monte Carlo dose calculations

Purpose: A dose calculation tool, which combines the accuracy of the dose planning method (DPM) Monte Carlo code and the versatility of a practical analytical multisource model, which was previously reported has been improved and validated for the Varian 6 and 10 MV linear accelerators (linacs). The calculation tool can be used to calculate doses in advanced clinical application studies. One shortcoming of current clinical trials that report dose from patient plans is the lack of a standardized dose calculation methodology. Because commercial treatment planning systems (TPSs) have their own dose calculation algorithms and the clinical trial participant who uses these systems is responsible for commissioning the beam model, variation exists in the reported calculated dose distributions. Today’s modern linac is manufactured to tight specifications so that variability within a linac model is quite low. The expectation is that a single dose calculation tool for a specific linac model can be used to accurately recalculate dose from patient plans that have been submitted to the clinical trial community from any institution. The calculation tool would provide for a more meaningful outcome analysis. Methods: The analytical source model was described by a primary point source, a secondary extra-focal source, and a contaminant electron source. Off-axis energy softening and fluence effects were also included. The additions of hyperbolic functions have been incorporated into the model to correct for the changes in output and in electron contamination with field size. A multileaf collimator (MLC) model is included to facilitate phantom and patient dose calculations. An offset to the MLC leaf positions was used to correct for the rudimentary assumed primary point source. Results: Dose calculations of the depth dose and profiles for field sizes 4 × 4 to 40 × 40 cm agree with measurement within 2% of the maximum dose or 2 mm distance to agreement (DTA) for 95% of the data points tested. The model was capable of predicting the depth of the maximum dose within 1 mm. Anthropomorphic phantom benchmark testing of modulated and patterned MLCs treatment plans showed agreement to measurement within 3% in target regions using thermoluminescent dosimeters (TLD). Using radiochromic film normalized to TLD, a gamma criteria of 3% of maximum dose and 2 mm DTA was applied with a pass rate of least 85% in the high dose, high gradient, and low dose regions. Finally, recalculations of patient plans using DPM showed good agreement relative to a commercial TPS when comparing dose volume histograms and 2D dose distributions. Conclusions: A unique analytical source model coupled to the dose planning method Monte Carlo dose calculation code has been modified and validated using basic beam data and anthropomorphic phantom measurement. While this tool can be applied in general use for a particular linac model, specifically it was developed to provide a singular methodology to independently assess treatment plan dose distributions from those clinical institutions participating in National Cancer Institute trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-202-05: Pilot Study of Online Treatment Evaluation and Adaptive Re-Planning for Laryngeal SBRT

Purpose: We have instigated a phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for advanced-stage laryngeal cancer. We conducted this pilot dosimetric study to confirm the potential utility of online adaptive re-planning to preserve treatment quality. Methods: Ten cases of larynx cancer were evaluated. Baseline and daily SBRT treatment plans were generated per trial protocol. Daily volumetric images were acquired prior to every fraction of treatment. Reference simulation CT images were deformably registered to daily volumetric images using Eclipse. Planning contours were then deformably propagated to daily images. Reference SBRT plans were directly copied to calculate delivered dose distributions on deformed reference CT images. In-house software platform has been developed to calculate cumulative dose over a course of treatment in four steps: 1) deforming delivered dose grid to reference CT images using deformation information exported from Eclipse; 2) generating tetrahedrons using deformed dose grid as vertices; 3) resampling dose to a high resolution within every tetrahedron; 4) calculating dose-volume histograms. Our inhouse software was benchmarked with a commercial software, Mirada. Results: In all ten cases including 49 fractions of treatments, delivered daily doses were completely evaluated and treatment could be re-planned within 10 minutes. Prescription dose coverage of PTV was less than intended in 53% of fractions of treatment (mean: 94%, range: 84%–98%) while minimum coverage of CTV and GTV was 94% and 97%, respectively. Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases. Conclusion: Although GTV and CTV coverage is preserved by in-room 3D image guidance of larynx SBRT, PTV coverage can vary significantly from intended plans. Online adaptive treatment evaluation and re-planning is potentially necessary and our procedure is clinically applicable to fully preserve treatment quality. This project is supported by CPRIT Individual Investigator Research Award RP150386.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer

Purpose: Pilot study developing a CT-texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Daily CT data acquired for 24 pancreatic head cancer patients using CT-on-rails, during the routine CT-guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co-occurrence, run-length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each daily CT. Metric-trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best-fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend-response metrics. Results: We found four specific trend-metrics (Gray Level Coocurence Matrix311-1InverseDiffMomentNorm, Gray Level Coocurence Matrix311-1InverseDiffNorm, Gray Level Coocurence Matrix311-1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend-metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586). Conclusion: We have successfully created a CT-texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-35: A Multi-Institutional Plan Quality Checking Tool Built On Oncospace: A Shared Radiation Oncology Database System

Purpose: Late toxicity from radiation to critical structures limits the possible dose in Radiation Therapy. Perfectly conformal treatment of a target is not realizable, so the clinician must accept a certain level of collateral radiation to nearby OARs. But how much? General guidelines exist for healthy tissue sparing which guide RT treatment planning, but are these guidelines good enough to create the optimal plan given the individualized patient anatomy? We propose a means to evaluate the planned dose level to an OAR using a multi-institutional data-store of previously treated patients, so a clinician might reconsider planning objectives. Methods: The tool is built on Oncospace, a federated data-store system, which consists of planning data import, web based analysis tools, and a database containing:1) DVHs: dose by percent volume delivered to each ROI for each patient previously treated and included in the database.2) Overlap Volume Histograms (OVHs): Anatomical measure defined as the percent volume of an ROI within a given distance to target structures.Clinicians know what OARs are important to spare. For any ROI, Oncospace knows for which patients’ anatomy that ROI was harder to plan in the past (the OVH is less). The planned dose should be close to the least dose of previous patients. The tool displays the dose those OARs were subjected to, and the clinician can make a determination about the planning objectives used.Multiple institutions contribute to the Oncospace Consortium, and their DVH and OVH data are combined and color coded in the output. Results: The Oncospace website provides a plan quality display tool which identifies harder to treat patients, and graphically displays the dose delivered to them for comparison with the proposed plan. Conclusion: The Oncospace Consortium manages a data-store of previously treated patients which can be used for quality checking new plans. Grant funding by Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-54: Guidelines to Check Image Registration QA of a Clinical Deformation Registration Software: A Single Institution Preliminary Study

Purpose: The objective of this study is to verify and analyze the accuracy of a clinical deformable image registration (DIR) software. Methods: To test clinical DIR software qualitatively and quantitatively, we focused on lung radiotherapy and analyzed a single (Lung) patient CT scan. Artificial anatomical changes were applied to account for daily variations during the course of treatment including the planning target volume (PTV) and organs at risk (OAR). The primary CT (pCT) and the structure set (pST) was deformed with commercial tool (ImSimQA-Oncology Systems Limited) and after artificial deformation (dCT and dST) sent to another commercial tool (VelocityAI-Varian Medical Systems). In Velocity, the deformed CT and structures (dCT and dST) were inversely deformed back to original primary CT (dbpCT and dbpST). We compared the dbpST and pST structure sets using similarity metrics. Furthermore, a binary deformation field vector (BDF) was created and sent to ImSimQA software for comparison with known “ground truth” deformation vector fields (DVF). Results: An image similarity comparison was made by using “ground truth” DVF and “deformed output” BDF with an output of normalized “cross correlation (CC)” and “mutual information (MI)” in ImSimQA software. Results for the lung case were MI=0.66 and CC=0.99. The artificial structure deformation in both pST and dbpST was analyzed using DICE coefficient, mean distance to conformity (MDC) and deformation field error volume histogram (DFEVH) by comparing them before and after inverse deformation. We have noticed inadequate structure match for CTV, ITV and PTV due to close proximity of heart and overall affected by lung expansion. Conclusion: We have seen similarity between pCT and dbpCT but not so well between pST and dbpST, because of inadequate structure deformation in clinical DIR system. This system based quality assurance test will prepare us for adopting the guidelines of upcoming AAPM task group 132 protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-18: Updates to the Computational Environment for Radiological Research for Image Analysis

Purpose: To present new tools in CERR for Texture Analysis and Visualization. Method: (1) Quantitative Image Analysis: We added the ability to compute Haralick texture features based on local neighbourhood. The Texture features depend on many parameters used in their derivation. For example: (a) directionality, (b) quantization of image, (c) patch-size for the neighborhood, (d) handling of the edge voxels within the region of interest, (e) Averaging co-occurance matrix vs texture features for different directions etc. A graphical user interface was built to set these parameters and then visualize their impact on the resulting texture maps. The entire functionality was written in Matlab. Array indexing was used to speed up the texture calculation. The computation speed is very competitive with the ITK library. Moreover, our implementation works with multiple CPUs and the computation time can be further reduced by using multiple processor threads. In order to reduce the Haralick texture maps into scalar features, we propose the use of Texture Volume Histograms. This lets users make use of the entire distribution of texture values within the region of interest rather than using just the mean and the standard deviations. (2) Qualitative/Visualization tools: The derived texture maps are stored as a new scan (derived) within CERR’s planC data structure. A display that compares various scans was built to show the raw image and the derived texture maps side-by-side. These images are positionally linked and can be navigated together. CERR’s graphics handling was updated and sped-up to be compatible with the newer Matlab versions. As a result, the users can use (a) different window levels and colormaps for different viewports, (b) click-and-drag or use mouse scroll-wheel to navigate slices. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics and Outcomes modeling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-24: Identifying Prognostic Imaging Biomarkers in Early Stage Lung Cancer Using Radiomics

Purpose: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. Methods: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer. Data from 70 patients were used for training/discovery purposes, while those of remaining 40 patients were used for independent validation. We extracted 191 radiomic features, including statistical, histogram, morphological, and texture features. Cox proportional hazard regression model, coupled with the least absolute shrinkage and selection operator (LASSO), was used to predict overall survival based on the radiomic features. Results: The optimal prognostic model included three image features from the Law’s feature and wavelet texture. In the discovery cohort, this model achieved a concordance index or CI=0.67, and it separated the low-risk from high-risk groups in predicting overall survival (hazard ratio=2.72, log-rank p=0.007). In the independent validation cohort, this radiomic signature achieved a CI=0.62, and significantly stratified the low-risk and high-risk groups in terms of overall survival (hazard ratio=2.20, log-rank p=0.042). Conclusion: We identified CT imaging characteristics associated with overall survival in early stage lung cancer. If prospectively validated, this could potentially help identify high-risk patients who might benefit from adjuvant systemic therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use in specific applications such as tissue classification and changes during radiation therapy utilizing a standard imaging protocol. Authors have the following disclosures: a research agreement with Philips Medical systems (Hugo, Weiss), a license agreement with Varian Medical Systems (Hugo, Weiss), research grants from the National Institute of Health (Hugo, Weiss), UpToDate royalties (Weiss), and none(Mahon, Ford, Karki). Authors have no potential conflicts of interest to disclose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study

Purpose: Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting. Methods: Simulation of FDG-PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly-shaped lesions featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey-level co-occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map. Results: In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH-based, 0.02–100.1% for GLCOM-based, 1.11–173.8% for GLNDM-based, and 0.35–66.3% for GLZSM-based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P-value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH-based standard deviation, GLCO-based energy and entropy, GLND-based coarseness and contrast, and GLZS-based low gray-level zone emphasis, high gray-level zone emphasis, short zone low gray-level emphasis, long zone low gray-level emphasis, long zone high gray-level emphasis, and zone size nonuniformity. Conclusion: The extent to which PET imaging disturbs texture appearance is feature-dependent and could be substantial. It is thus advised that use of PET texture parameters for predictive and prognostic measurements in oncologic setting awaits further systematic and critical evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-47: Quantitative Shape Relationship Analysis of PTV Modification for Critical Anatomy Sparing and Its Impact On Pathologic Response for Neoadjuvant Stereotactic Radiotherapy for Pancreatic Cancer

Purpose: Stereotactic body radiation therapy (SBRT) may be used to increase surgery candidacy in borderline resectable (BRPC) and locally advanced (LAPC) pancreatic cancer. However, the planning target volume (PTV) may need to be limited to avoid toxicity when the gross tumor volume (GTV) is anatomically involved with surrounding critical structures. Our study aims to characterize the coverage of GTV and investigate the association between modified PTV and pathologic (pCR) or near pathologic (npCR) complete response rates determined from the surgical specimen. Methods: Patients treated with neoadjuvant pancreas SBRT followed by surgery from 2010–2015 were selected from Oncospace. Overlap volume histogram (OVH) analysis was performed to determine the extent of compromise of the PTV from both the GTV and a standard target (GTV+3mm). Subsequently, normalized overlap volume (%) was calculated for: (1) GTV-PTV, and (2) GTV+3mm expansion-PTV. A logistic regression model was used to identify the association between the overlap ratios and ≥ npCR(pCR/npCR) stratified by active breathing control (ABC) versus free-breathing status. Results: Eighty-one (BRPC: n=42, LAPC: n=39) patients were available for analysis. Nearly 40% (31/81) had ≥npCR and 75% (61/81) were able to complete ABC. Mean coverage of the GTV-PTV was 92.6% (range, 59.9%–100%, SD = 8.68) and coverage of the GTV+3mm expansion-PTV was 85. 2% (range, 59.9% −100.0%, SD= 8.67). Among the patients with ABC, every 10% increase in GTV coverage doubled the odds to have ≥npCR (OR = 1.82, p=0.06). Coverage of GTV+3mm expansion was not associated with ≥npCR regardless of ABC status. Conclusion: Preferential sparing of critical anatomy over GTV-PTV coverage with ABC management suggests worse ≥npCR rates for neoadjuvant SBRT in BRPC and LAPC. Limiting the GTV and GTV+3mm expansion in free-breathing patients was not associated with pathologic response perhaps due to larger GTV definitions as a result of motion artifacts in free-breathing CT scans. Collaboration with Toshiba, Elekta, and Phillips.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-54: CT-Texture Based Early Tumor Treatment Response Assessment During Radiation Therapy Delivery: Small Cell Versus Non-Small Cell Lung Cancers

Purpose: Tumor treatment response may potentially be assessed during radiation therapy (RT) by analyzing changes in CT-textures. We investigated the different early RT-responses between small cell (SCLC) and non-small cell lung cancer (NSCLC) as assessed by CT-texture. Methods: Daily diagnostic-quality CT acquired during routine CT-guided RT using a CT-on-Rails for 13-NSCLC and 5-SCLC patients were analyzed. These patient had ages ranging from 45–78 and 38–63 years, respectively, for NSCLC and SCLC groups, and tumor-stages ranging from T2-T4, and were treated with either RT or chemotherapy and RT with 45–66Gy/ 20–34 fractions. Gross-tumor volume (GTV) contour was generated on each daily CT by populating GTV contour from simulation to daily CTs with manual editing if necessary. CT-texture parameters, such as Hounsfield Unit (HU) histogram, mean HU, skewness, kurtosis, entropy, and short-run high-gray level emphasis (SRHGLE), were calculated in GTV from each daily CT-set using an in house software tool. Difference in changes of these texture parameters during RT between NSCLC and SCLC was analyzed and compared with GTV volume changes. Results: Radiation-induced changes in CT-texture were different between SCLC and NSCLC. Average changes from first to the last fractions for NSCLC and SCLC in GTV were 28±10(12–44) and 30±15(11–47) HU (mean HU reduction), 12.7% and 18.3% (entropy), 50% and 55% (SRHGLE), 19% and 22% (kurtosis), and 5.2% and 22% (skewness), respectively. Good correlation in kurtosis changes and GTV was seen (R{sup 2}=0.8923) for SCLC, but not for NSCLC (R{sup 2}=0.4748). SCLC had better correlations between GTV volume reduction and entropy (SCLC R{sup 2}=0.847; NSCLC R{sup 2}=0.6485), skewness (SCLC R{sup 2}=0.935; NSCLC R{sup 2}=0.7666), or SRHGLE (SCLC R{sup 2}=0.9619; NSCLC R{sup 2}=0.787). Conclusion: NSCLC and SCLC exhibited different early RT-responses as assessed by CT-texture changes during RT-delivery. The observed larger changes in various CT-texture parameters for SCLC indicate that SCLC may respond to RT more rapid than NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-55: Early Detection of Treatment Induced Bone Marrow Injury During Chemoradiation Therapy Using Quantitative CT

Purpose: Acute hematologic toxicity associated with bone marrow injury is a common complication of chemoradiation therapy (CRT) for pelvic malignancies. In this work, we investigate the feasibility of using quantitative CT to detect bone marrow injury during CRT. Methods: Daily CTs were acquired during routine CT-guided radiation therapy using a CT-on-rails for 15 cervical cancer patients. All patients treated with a radiation dose of 45.0 to 50.4 Gy in 1.8 Gy/fraction along with chemotherapy. For each patient, the contours of bone marrow were generated in L4, L5 and sacrum on the first daily CT and then populated to other daily CTs by rigid registration using MIM (MIM Software Inc., Cleveland, OH) with manual editing if possible. A series of CT texture parameters, including Hunsfield Unit (HU) histogram, mean HU, entropy, energy, in bone marrow contours were calculated using MATLAB on each daily CT and were correlated with the completed blood counts (CBC) collected weekly for each patient. The correlations were analyzed with Pearson correlation tests. Results: For all patient data analyzed, mean HU in bone marrow decreased during CRT delivery. From the first to the last fraction the average mean HU reduction is 58.1 ± 13.6 HU (P<0.01). This decrease can be observed as early as after first 5 fractions and is strongly associated with the changes of most CBC quantities, such as the reductions of white and blood cell counts (r=0.97, P=0.001). The reduction of HU is spatially varied. Conclusion: Chemoradiation induced bone marrow injury can be detected during the delivery of CRT using quantitative CT. Chemoradiation results in reductions in mean HU, which are strongly associated with the change in the pretrial blood cell counts. Early detection of bone marrow injury with commonly available CT opens a door to improve bone marrow sparing, reducing risk of hematologic toxicity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-13: Choosing An IMRT Technique in the Treatment of Head and Neck Cancer with Daily Localization Uncertainties

Purpose: Head and Neck cancer treatment with IMRT/VMAT has two choices: split-filed IMRT(SFI), in which the LAN is treated with a separate anterior field and the extended whole-field IMRT(WFI) in which LAN is included with the IMRT/VMAT field. This study shows that under the same dose limit criteria, choosing the technique becomes a critical issue if daily localization and immobilization altered the dose distribution. Methods: Nine common head-and-neck cancer cases were chosen to illustrate how the daily localization and immobilization uncertainties affect to choose between SFI and WFI. Both SFI and WFI at upper target coverage were generated with VMAT. For each case, the same planning criteria were applied to the target and critical structures; therefore, similar target coverage and dose falloff can be observed in both techniques. Thirty days of kV cone beam CT(CBCT) images on each case were also delineated with contralateral and ipsilateral target as well as larynx as critical structure. About 300 CBCT images with daily delivered doses were analyzed and compared in a form of dose-volume histograms. Results: While both plans for SFI and WFI with VMAT planning utilized and meet the criteria of D95>prescription dose and for not-involved larynx with mean dose <35Gy and V55<10%, the daily localization and immobilization has a great contribution to the resulted dose delivery. With WFI, the better daily contralateral and ipsilateral neck target coverage can reflect a simpler or shorter localization; however, a much superior avoidance (WFI: mean dose a 42.5Gy; SFI: mean dose a 18.9Gy) of the non-involved larynx from the SFI is preferred. Conclusion: Dosimetrically, SFI and WFI are equally well for head and Neck cancer treatment with VMAT technique; however, if considering the contribution of daily localization(CBCT) method uncertainties, SFI is better with sparing non-involved larynx and WFI has better target coverage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-34: Automatic Target-Based Patient Positioning Framework for Image-Guided Radiotherapy in Prostate Cancer Treatment

Purpose: Current image-guided radiotherapy (IGRT) procedure is bonebased patient positioning, followed by subjective manual correction using cone beam computed tomography (CBCT). This procedure might cause the misalignment of the patient positioning. Automatic target-based patient positioning systems achieve the better reproducibility of patient setup. Our aim of this study was to develop an automatic target-based patient positioning framework for IGRT with CBCT images in prostate cancer treatment. Methods: Seventy-three CBCT images of 10 patients and 24 planning CT images with digital imaging and communications in medicine for radiotherapy (DICOM-RT) structures were used for this study. Our proposed framework started from the generation of probabilistic atlases of bone and prostate from 24 planning CT images and prostate contours, which were made in the treatment planning. Next, the gray-scale histograms of CBCT values within CTV regions in the planning CT images were obtained as the occurrence probability of the CBCT values. Then, CBCT images were registered to the atlases using a rigid registration with mutual information. Finally, prostate regions were estimated by applying the Bayesian inference to CBCT images with the probabilistic atlases and CBCT value occurrence probability. The proposed framework was evaluated by calculating the Euclidean distance of errors between two centroids of prostate regions determined by our method and ground truths of manual delineations by a radiation oncologist and a medical physicist on CBCT images for 10 patients. Results: The average Euclidean distance between the centroids of extracted prostate regions determined by our proposed method and ground truths was 4.4 mm. The average errors for each direction were 1.8 mm in anteroposterior direction, 0.6 mm in lateral direction and 2.1 mm in craniocaudal direction. Conclusion: Our proposed framework based on probabilistic atlases and Bayesian inference might be feasible to automatically determine prostate regions on CBCT images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-104: Weekly MRI for Dose Assessment of Organs at Risk During Treatment of HN Cancer of the Oropharynx

Purpose: Investigate the feasibility of using weekly MRI to assess dose to organs at risk utilizing deformable image registration. Methods: Sixteen H&N patients with oropharyngeal cancer were imaged on a 3T MR scanner using T2W and mDIXON sequence. Patients were imaged on a weekly basis in treatment position. Parotids (LP & RP), submandibular glands (LS, RS), and oral cavity (OC) were delineated on the weekly MR and reviewed by a board certified radiation oncologist. The original planning CT (pCT), RT-Dose, and RT-Structures were deformed and registered to each weekly MRIs. The deformed CTs and RT-Structures were imported to the treatment planning system (TPS) and rigidly registered to the pCT. Forward dose calculation of the original RT-Plan was used to estimate the delivered dose on the deformed CT. The dose volume histograms (DVH) statistics were performed to compare planned dose, deformed dose, and forward calculated dose. In addition, Dice similarity metric (DSM) was used to compare deformed and reference structures. Results: The average (min,max) DSM between deformed and reference structures was 0.71 (0.69,0.93); 0.70 (0.64,0.89); 0.65 (0.48,0.86); 0.63 (0.37,0.89); and 0.63 (0.58,0.87); for LP, RP, LS, RS, and OC respectively. The respective average relative structures volumes changed at a weekly rate of −4.99%; −4.40%; +3.45%; +1.46%; −1.39%, respectively. The percentage difference %(min,max) between estimated delivered dose and planned dose was +3.94 (−51.3,+30.5); +6.33 (−58.6,+82.7); +2.46 (−38.9,+37.6,); +2.38(−49.0,+28.9); +3.55(−17.0,+43.1). Conclusion: The recalculated dose based on weekly MRI deviated from planned dose for all OARs. Meanwhile, the deformed dose did not reflect the subtle changes in OARs as compared to the recalculated dose. This study demonstrates the feasibility of using weekly MRI to monitor volumetric changes which has important implications on actual delivered dose.

60 APPLIED LIFE SCIENCES↗