RT Image Research Articles

Clinical Validation of Siemens’ Syngo.via Automatic Contouring System

The manual delineation of organs at risk is a process that requires a great deal of time both for the technician and for the physician. Availability of validated software tools assisted by artificial intelligence would be of great benefit, as it would significantly improve the radiation therapy workflow, reducing the time required for segmentation. The purpose of this article is to validate the deep learning-based autocontouring solution integrated in syngo.via RT Image Suite VB40 (Siemens Healthineers, Forchheim, Germany). For this purpose, we have used our own specific qualitative classification system, RANK, to evaluate more than 600 contours corresponding to 18 different automatically delineated organs at risk. Computed tomography data sets of 95 different patients were included: 30 patients with lung, 30 patients with breast, and 35male patients with pelvic cancer. The automatically generated structures were reviewed in the Eclipse Contouring module independently by 3 observers: an expert physician, an expert technician, and a junior physician. There is a statistically significant difference between the Dice coefficient associated with RANK 4 compared with the coefficient associated with RANKs 2 and 3 (P < .001). In total, 64% of the evaluated structures received the maximum score, 4. Only 1% of the structures were classified with the lowest score, 1. The time savings for breast, thorax, and pelvis were 87.6%, 93.5%, and 82.2%, respectively. Siemens' syngo.via RT Image Suite offers good autocontouring results and significant time savings.

Anal squamous cell carcinoma: Impact of radiochemotherapy evolution over years and an explorative analysis of MRI prediction of tumor response in a mono-institutional series of 131 patients.

Radiochemotherapy (RCHT) for the treatment of anal squamous cell carcinoma (ASCC) has evolved dramatically, also thanks to intensity-modulated RT (IMRT) and 3D image guidance (3D IGRT). Despite most patients presenting fair outcomes, unmet needs still exist. Predictors of poor tumor response are lacking; acute toxicity remains challenging; and local relapse remains the main pattern of failure. Between 2010 and 2020, ASCC stages I-III treated with 3D conformal radiotherapy or IMRT and CDDP-5FU or Mytomicine-5FU CHT were identified. Image guidance accepted included 2D IGRT or 3D IGRT. The study endpoints included freedom from locoregional recurrence (FFLR), colostomy free survival (CFS), freedom from distant metastasis (FFDM), overall survival (OS), and acute and late toxicity as measured by common terminology criteria for adverse events (CTCAE) version 5.0. An exploratory analysis was performed to identify possible radiomic predictors of tumor response. Feature extraction and data analysis were performed in Python™, while other statistics were performed using SPSS® v.26.0 software (IBM®). A total of 131 patients were identified. After a median FU of 52 months, 83 patients (63.4%) were alive. A total of 35 patients (26.7%) experienced locoregional failure, while 31 patients (23.7%) relapsed with distant metastasis. Five year FFLR, CFS, DMFS and PS resulted 72.3%, 80.1%, 74.5% and 64.6%. In multivariate analysis, 2D IGRT was associated with poorer FFLR, OS, and CFS (HR 4.5, 4.1, and 5.6, respectively); 3DcRT was associated with poorer OS and CFS (HR 3.1 and 6.6, respectively). IMRT reduced severe acute gastro-intestinal (GI) and severe skin acute toxicity in comparison with 3DcRT. In the exploratory analysis, the risk of relapse depended on a combination of three parameters: Total Energy, Gray Level Size Zone Matrix's Large Area High Gray Level Emphasis (GLSZM's LAHGLE), and GTV volume. Advances in radiotherapy have independently improved the prognosis of ASCC patients over years while decreasing acute GI and skin toxicity. IMRT and daily 3D image guidance may be considered standard of care in the management of ASCC. A combination of three pre-treatment MRI parameters such as low signal intensity (SI), high GLSZM's LAHGLE, and GTV volume could be integrated in risk stratification to identify candidates for RT dose-escalation to be enrolled in clinical trials.

Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models.

Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm–1, three-phase-shifted images with phase offsets of − 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation

BackgroundWe describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.MethodsThe algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation. The algorithm is commercially available in the medical products “syngo.via RT Image Suite VB50” and “AI-Rad Companion Organs RT VA20” (Siemens Healthineers). For evaluation, thoracic CT images of 237 patients and pelvic CT images of 102 patients were manually contoured following the Radiation Therapy Oncology Group (RTOG) guidelines and compared to the DI2IN results using metrics for volume, overlap and distance, e.g., Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95). The contours were also compared visually slice by slice.ResultsWe observed high correlations between automatic and manual contours. The best results were obtained for the lungs (DSC 0.97, HD95 2.7 mm/2.9 mm for left/right lung), followed by heart (DSC 0.92, HD95 4.4 mm), bladder (DSC 0.88, HD95 6.7 mm) and rectum (DSC 0.79, HD95 10.8 mm). Visual inspection showed excellent agreements with some exceptions for heart and rectum.ConclusionsThe DI2IN algorithm automatically generated contours for organs at risk close to those by a human expert, making the contouring step in radiation treatment planning simpler and faster. Few cases still required manual corrections, mainly for heart and rectum.

ARt Image Exploration Space (ARIES): A response to the image needs of art library patrons

AbstractARt Image Exploration Space (ARIES) is a free, cloud-based dynamic environment offering art historians and others an extensive array of practical tools for analysing images. It is the product of a successful collaboration between art historians, librarians, computer scientists, and engineers from the Frick Art Reference Library, New York University's Tandon School of Engineering, and Brazil's Universidade Federal Fluminense. ARIES is a powerful tool for art historians, both replicating and augmenting traditional methods they have long-used to study images.1 With the advent of the prevalent use of digital photos, art historians lacked the technology capable of replacing what they had previously been able to accomplish in the analogue world. Wood Ruby and Deutch realized that art historians needed an out-of-the-box solution that didn't require extensive knowledge of other disciplines (computer science and engineering). The result of successful collaborations and a generous donation, ARIES is now available in BETA form at www.artimageexplorationspace.com.

P03.03 Dynamics of brain metastasis cavities between resection and adjuvant stereotactic radiotherapy and impact of the delay on local control

Abstract BACKGROUND Adjuvant stereotactic radiotherapy (SRT) to the surgical cavity of brain metastases (BM) improves the local control. An early post-operative irradiation is desirable to limit the risk of recurrence while delaying it might reduce the risk of radionecrosis by reducing the size of the target volume (TV) thanks to the cavity constriction. The purpose of our study was to examine cavity dynamics after resection to determine if delaying SRT influences cavity size as well as local progression (LP). MATERIAL AND METHODS We reviewed 70 BM resection cavities treated with adjuvant SRT. A preoperative, immediate postoperative and at the time of SRT, 3D T1-weigthed MRI, were available for all patients. The volumes were retrospectively calculated with the PACS software Telemis® version 4 and the iPlan RT image software. For all cavities, the TV was obtained by expanding the cavity by a median margin of 2mm to account for microscopic spread. The surgical track and edema areas were not included in the TV. The dynamic evolution of cavities and LP according to the delay of SRT were analyzed. RESULTS The median time from surgery to SRT was 27 days (3–99). The median preoperative and SRT cavity volumes were 7.2 cc (ranges: 0.5–29.5) and 8 cc (ranges: 0.8–31), respectively. After margin expansion, the TV was increased to a median of 14.8 cc. The volumetric cavity changes were an increase by 2 cc in 35 %, whereas only 22.5 % decreased by 2 cc and 42.5% cavities were stable (defined as a change ≤2 cc). We noted that 11% of volume change were attributable to the delay (r2 = 0.11). forty-seven patients were treated within a delay of 30 days and experienced early LP in 10.6 % against 20 % beyond 30 days (p= 0.6). CONCLUSION In the interval between surgery and SRT, BM cavities dynamics are inconstant and unpredictable in function of time. Furthermore, delaying SRT may compromise local control, though the difference was not statistically significant due to the small size of our population. A preoperative SRT strategy looks more promising by alleviating the problem of time and reducing the volume of irradiated brain.

P03.02 Risk of leptomeningeal dissemination in patients treated with postoperative stereotactic radiotherapy of brain metastases

Abstract Background There is a body of evidence that the risk of leptomeningeal dissemination (LMD) is increased in the postoperative stereotactic radiotherapy (SRT) of brain metastases (BM) compared to adjuvant whole brain radiotherapy (WBRT). The proposed mechanism is an iatrogenic tumor dissemination into the cerebrospinal fluid at time of surgery. Including a wider volume of meningeal wall and the entire surgical track in the definition of the postoperative SRT clinical target volume (CTV) to decrease LMD is still controversial. The aim of this study was to retrospectively analyze the outcome of adjuvant SRT targeted at resection cavities of BM without previous WBRT. MATERIAL / METHODS We reviewed 70 patients treated with postoperative SRT for BM. Stereotactic planning computed tomography and planning MRI were imported into iPlan RT image software for image registration and TV delineation. The CTV consisted of any residual enhancement and all resected cavity including a safety margin of 1 to 2 mm. Only in cases of superficial initial tumor with meningeal contact was the CTV enlarged to the adjacent meningeal wall, but never included edema or the entire surgical track. Patients underwent regular follow-up MRI. The cumulative incidence rates of LMD was retrospectively calculated as well as patterns of failure. RESULTS The most common histological type was non small cell lung cancer in 61.4%. There were 38.6% infratentorial locations and 37.2 % superficial lesions. En bloc resection was achieved in 60% and compete resection in 75.7%. After a median imaging follow up time of 16.7 months, 54.3% of patients experienced distant brain failure. LMD occurred in 9 of 70 patients (12.9 %) at a median time of 10.7 months. Survival without LMD was 88% at 1 year (IC 95% 79%-97%) and 82% at 2years (IC 95% 72%-94%). In three quarter of cases, LMD interested superficial lesions. In univariate analysis, survival rates without LMD at 1 year for superficial and deep lesions were 88 % and 94 %, respectively (p=0.49). We report only one recurrence in the surgical track (1.42%). CONCLUSION The risk of LMD was comparable to the literature (11–17%). Superficial lesions were slightly more likely to relapse in the meninges, but it was non-significant. The risk of recurrence in the surgical track is negligible. Our results do not support the current guidelines recommending the systematic inclusion of the surgical track and the related meninges in the CTV.

MO‐F‐CAMPUS‐J‐02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Purpose:To investigate the method to automatically recognize the treatment site in the X‐Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup.Methods:The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down‐sampled (from 1024×768 to 128×96) by using bi‐cubic interpolation algorithm. An appearance‐based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi‐class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross‐validation experiments were performed to evaluate the performance.Results:MATLAB image processing Toolbox and scikit‐learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images.Conclusion:The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance.The work was partially supported by a research grant from Varian Medical System.

TU‐F‐CAMPUS‐T‐05: Replacement Computational Phantoms to Estimate Dose in Out‐Of‐Field Organs and Tissues

Purpose:To estimate the absorbed dose in organs and tissues at risk for radiogenic cancer for children receiving photon radiotherapy for localized brain tumors (LBTs) by supplementing their missing body anatomies with those of replacement computational phantoms. Applied beyond the extent of the RT Images collected by computed tomography simulation, these phantoms included RT Image and RT Structure Set objects that encompassed sufficient extents and contours for dosimetric calculations.Method:Nine children, aged 2 to 14 years, who received three‐dimensional conformal radiotherapy for low‐grade LBTs, were randomly selected for this study under Institutional‐Review‐Board protocol. Because the extents of their RT Images were cranial only, they were matched for size and sex with patients from a previous study with larger extents and for whom contours of organs at risk for radiogenic cancer had already been delineated. Rigid fusion was performed between the patients’ data and those of the replacement computational phantoms using commercial software. In‐field dose was calculated with a clinically‐commissioned treatment planning system, and out‐of‐field dose was estimated with an analytical model.Results:Averaged over all nine children and normalized for a therapeutic dose of 54 Gy prescribed to the PTV, where the PTV is the GTV, the highest mean organ doses were 3.27, 2.41, 1.07, 1.02, 0.24, and 0.24 Gy in the non‐tumor remainder, red bone marrow, thyroid, skin, breasts, and lungs, respectively. The mean organ doses ranged by a factor of 3 between the smallest and largest children.Conclusion:For children receiving photon radiotherapy for LBTs, we found their doses in organs at risk for second cancer to be non‐negligible, especially in the non‐tumor remainder, red bone marrow, thyroid, skin, breasts, and lungs. This study demonstrated the feasibility for patient dosimetry studies to augment missing patient anatomy by applying size‐ and sex‐matched replacement computational phantoms with pre‐contoured organs.Funding is in part by the Fogarty International Center award K01TW008409, and the Portland Chapter of the Achievement Rewards for College Scientists. The content is solely the responsibility of the authors, and does not necessarily represent the official views of the sponsors. The authors declare no conflict of interest.

SU‐E‐J‐16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

Purpose:In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance.Methods:The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high‐pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi‐pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior‐point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result.Results:A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent.Conclusion:This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window‐level adjustment process that are currently used in clinical 2D image review software tools.

Evaluation of superficial dosimetry between treatment planning system and measurement for several breast cancer treatment techniques

Dosimetric accuracy in radiation treatment of breast cancer is critical for the evaluation of cosmetic outcomes and survival. It is often considered that treatment planning systems (TPS) may not be able to provide accurate dosimetry in the buildup region. This was investigated in various treatment techniques such as tangential wedges, field-in-field (FF), electronic compensator (eComp), and intensity-modulated radiotherapy (IMRT). Under Institutional Review Board (IRB) exemption, radiotherapy treatment plans of 111 cases were retrospectively analyzed. The distance between skin surface and 95% isodose line was measured. For measurements, Gafchromic EBT2 films were used on a humanoid unsliced phantom. Multiple layers of variable thickness of superflab bolus were placed on the breast phantom and CT scanned for planning. Treatment plans were generated using four techniques with two different grid sizes (1 × 1 and 2.5 × 2.5 mm(2)) to provide optimum dose distribution. Films were placed at different depths and exposed with the selected techniques. A calibration curve for dose versus pixel values was also generated on the same day as the phantom measurement was conducted. The DICOM RT image, dose, and plan data were imported to the in-house software. On axial plane of CT slices, curves were drawn at the position where EBT2 films were placed, and the dose profiles on the lines were acquired. The calculated and measured dose profiles were separated by check points which were marked on the films before irradiation. The segments of calculated profiles were stretched to match their resolutions to that of film dosimetry. On review of treatment plans, the distance between skin and 95% prescribed dose was up to 8 mm for plans of 27 patients. The film measurement revealed that the medial region of phantom surface received a mere 45%-50% of prescribed dose. For wedges, FF, and eComp techniques, region around the nipple received approximately 80% of prescribed dose, although only IMRT showed inhomogeneous dose profile. At deeper depths mainly (6-11 mm depths), film dosimetry showed good agreement with the TPS calculation. In contrast, the measured dose at a 3-mm depth was higher than TPS calculation by 15%-30% for all techniques. For the tangential and IMRT techniques, 1 × 1 mm(2) grid size showed a smaller difference than that with a 2.5 × 2.5 mm(2) grid size compared to the measurements. In general, TPS even with advanced algorithms do not provide accurate dosimetry in the buildup region, as verified by EBT2 film for all treatment techniques. For all cases, TPS and measured doses were in agreement from 6 mm in depth but differed at shallower depths. Grid size plays an important role in dose calculation. For accurate dosimetry small grid size should be used where differences are lower between TPS and measurements.

SU-E-T-640: DICOM-RT Data Transfer of Structure Sets Between SRS Treatment Planning Systems

Purpose: To verify the integrity of data structures created in one treatment planning system and transferred to another using DICOM‐RT for stereotactic radiosurgery. Methods: Ten radiosurgery patients were selected for this retrospective study. Initial planning images were imported into a Brainlab iPlan© RT Image 4.1 workstation. Normal structures and lesions were contoured for each patient. The structure volume as reported by the iPlan software was recorded for each volume. The images and structures were then transferred via DICOM‐RT to a Leksell GammaPlan® workstation (for Perfexion™). The volume of each structure, as reported by GammaPlan, was compared to the iPlan values. Finally, all images and structures from the iPlan workstation and the GammaPlan workstation were sent via DICOM‐RT to a Varian Eclipse™ workstation so that the respective volumes could be determined on a common platform. Results: Structure volumes ranged from less than 0.1 cc (e.g. optic tracks) to over 30 cc (brainstem). Volumes reported by GammaPlan differed from those reported by iPlan, with the former generally being smaller. Differences of up to 0.5 cc were observed. The largest absolute differences tended to be for the larger structures. The greatest percent differences were for the smaller structures, with the reported volumes differing by a factor of two or more. When the structures were sent from iPlan and GammaPlan to Eclipse (and calculated with a single algorithm), the volume differences were found to be much smaller. Conclusions: It is feasible to import DICOM‐RT structure sets into the Leksell GammaPlan from other platforms such as Brainlab's iPlan RT Image. Different planning systems (e.g. GammaPlan, iPlan and Eclipse) use different algorithms to calculate structure volumes. These differences may have significant affects (especially for small structures) when calculating dose volume histograms and conformity indices.

SU-GG-T-256: An Enhanced DICOM-RT Viewer

Purpose: To present an enhanced DICOM-RT viewer for clinical practitioners and R&D scientists and engineers in radiation therapy. Methods and Materials: DICOM-RT is a set of DICOM extensions for radiotherapy. Despite the fact that DICOM-RT was devised in 1997 and has currently been adopted by all major manufacturers, the DICOM-RT viewer is not readily available to many healthcare providers and R&D scientists and engineers in radiation therapy. Triggered by clinical needs, a DICOM/DICOM-RT viewer, DICOMan, was developed on Microsoft Windows XP platform started from scratch three years ago. It supports all kinds of DICOM-RT objects, namely RT Image, RT Structure Set, RT Plan, RT Dose and RT Record. Added to the viewer, a set of built-in tools were also implemented, such as DICOM Decompressor, DICOM Pusher, DICOM Retriever, DICOM editor, DICOM Anonymizer, DICOM Format Converter and so on. Some RT information that is not explicitly expressed in RT objects, such as incident energy fluence and isodose curves, can be reproduced and rendered to facilitate plan review. Results: DICOMan has been tested, routinely used and constantly upgraded for about three years. It has been frequently used to view DICOM CD-ROMs, push DICOM images to treatment planning systems, retrieve images from RT PACS, review treatment plans and help perform dose summation. The anonymizer, format converter and editor are handy tools for R&D scientists and engineers who need to handle DICOM/DICOM-RT objects. Conclusion: DICOM RT extension models radiation therapy practice in many modules related to clinical components of radiation therapy. Many of them are cross-referenced and coexist with the referenced ones in different objects. The regular DICOM viewer can not render RT objects appropriately. A dedicated DICOM RT viewer is needed.

SU-GG-T-258: Solutions to Transforming a Radiotherapy Clinic with Multigenerational Equipment into a Paperless and Filmless Facility

Purpose: To present solutions to transforming a radiation therapy clinic with multigenerational equipment to a paperless and filmless facility. Methods and Materials: Becoming paperless and filmless (digital) is not only more efficient and cost effective but also more environmentally friendly, however not all equipped systems are digital ready. Here we present in‐house developed digital solutions for those clinics with a similar clinical setting and the inclination of becoming digital: CT and analog simulators, Philips Pinnacle3RTP systems, BrainLAB iPlan RTP systems, conventional LINACs with IGRT capabilities, TomoTherapy units and planning systems, brachytherapy planning and delivery systems, stereotactic radiosurgery modalities and hyperthermia apparatus. A few software systems have been developed to either upgrade purchased systems or interface involved ones. System customization procedures were also performed to get them ready for a digital clinic. Results: An add‐on system, PortalSim, was developed to transform a conventional analog simulator to a digital fluoroscopy and dynamic simulation system with DICOM connectivity; a RT image pumping utility, iPump, was devised to interface BrainLAB ExacTrac IGRT system and Record and Verify (RV a DICOM screen dumping tool, ScreenBee, was created to capture computer screen of any system that lacks the effective communication channel with R&V system. Some RTP systems have been customized to meet digital requirements. As a result, all clinical data and documents from plans to quality assurance reports are consolidated in one place in digital form. Conclusion: Having a centralized data management clinical setting without using paper and radiographic film is the way to go for a digital radiation therapy clinic. Scanning everything is one of the solutions some may think of, but it is laborious and not truly paperless. There are better solutions by in‐house system development and system customization.

Volumetric analysis of thoracic and lumbar vertebral bodies

Background context During the last decade, vertebral augmentation techniques with cement (kyphoplasty and vertebroplasty) have revealed that 75% to 100% of individuals with osteoporotic vertebral compression fractures have good to moderate pain relief postoperatively, as well as adequate restoration of the vertebral body height. The volume of cement injected into a vertebra varied in different reports. To our knowledge, there are no studies that report on the physiological distribution of thoracic and lumbar vertebrae body volumes. Purpose The purpose of this study was to quantitatively evaluate thoracic and lumbar vertebral body volumes. Study design/setting Forty computed tomography (CT) scans of the thoracic and lumbar spines were used to measure a volume of each vertebral body. Methods Forty CT scans with standard 2.5-mm bone window cuts of the thoracic and lumbar vertebrae (20 men and 20 women) were analyzed to measure the volumetric capacity of vertebral bodies using a BrainLAB Software (iPlan RT image 4.0) program. The software was tested for validity and reliability in two pilot studies. Mean and standard deviations for each vertebral body volume were calculated and recorded. The vertebral body working distance and the so-called “safe zone” were also measured to determine a size of a balloon to be used in kyphoplasty. Results Vertebral body volume increased gradually from T1 to L4 with the exception of L5, which measured to be smaller than L4. The mean thoracic vertebrae volume was 15.0 cm 3 (ranged in the absolute values from 5.2 to 39.5 cm 3), and the mean lumbar vertebrae volume was 35 cm 3 (ranged in the absolute values from 19.7 to 61.5 cm 3). Men had larger volume vertebral bodies only in the lumbar spine compared with women. The average vertebral body working distances from T1 to T4 was 23.4±2.7 mm, from T5 to T9 was 30.3±3.6 mm, and from T10 to L5 was 35.5±3.9 mm. Conclusions Knowing the physiological variability of vertebral body volumes may help prevent complications as a result of underaugmentation or overaugmentation with excessive amount of cement during kyphoplasty or vertebroplasty in osteoporotic compression fractures. We recommend using 10 mm balloons from T1 to T4, 15 mm balloons from T5 to T9, and 20 mm balloons from T10 to L5 for kyphoplasty.

DICOM-RT and Its Utilization in Radiation Therapy

The Digital Imaging and Communications in Medicine (DICOM) standard is now widely implemented in radiology as the standard for diagnostic imaging. It has also been extended for use in various sub-specialties. One of the first extensions was applied to radiation therapy and is known as DICOM-RT. In addition to the protocol used in the DICOM standard, seven DICOM-RT objects--namely, RT Image, RT Structure Set, RT Plan, RT Dose, RT Beams Treatment Record, RT Brachy Treatment Record, and RT Treatment Summary Record--have been created, each with a well-defined data model. The data models set the standard for integration of radiation therapy information for an electronic patient record and would facilitate the interoperability of different radiation therapy systems, thus making possible the sharing of information from different systems.

Local Anatomic Changes in Parotid and Submandibular Glands During Radiotherapy for Oropharynx Cancer and Correlation With Dose, Studied in Detail With Nonrigid Registration

To quantify the anatomic changes caused by external beam radiotherapy in head-and-neck cancer patients in full three dimensions and to relate the local anatomic changes to the planned mean dose. A nonrigid registration method was adapted for RT image registration. The method was applied in 10 head-and-neck cancer patients, who each underwent a planning and a repeat computed tomography scan. Contoured structures (parotid, submandibular glands, and tumor) were registered in a nonrigid manner. The accuracy of the transformation was determined. The transformation results were used to summarize the anatomic changes on a local scale for the irradiated and spared glands. The volume reduction of the glands was related to the planned mean dose. Transformation was accurate with a mean error of 0.6 +/- 0.5 mm. The volume of all glands and the primary tumor decreased. The lateral regions of the irradiated parotid glands moved inward (average, 3 mm), and the medial regions tended to remain in the same position. The irradiated submandibular glands shrank and moved upward. The spared glands showed only a small deformation ( approximately 1 mm in most regions). Overall, the primary tumors shrank. The volume loss of the parotid glands correlated significantly with the planned mean dose (p <0.001). General shrinkage and deformation of irradiated glands was seen. The spared glands showed few changes. These changes were assessed by a nonrigid registration method, which effectively described the local changes occurring in the head-and-neck region after external beam radiotherapy.

1011: A Review of the Activities of the ITC in Support of RTOG Advanced Technology Clinical Trials

1011: A Review of the Activities of the ITC in Support of RTOG Advanced Technology Clinical Trials

SU‐FF‐T‐308: ITC Assists Developers of ATC Compliant DICOM Export for Clinical Trials

Purpose: The Image‐guided Therapy QA Center (ITC) as part of the Advanced Technology QA Consortium (ATC) has played a key role in assisting treatment planning system (TPS) developers in verifying that their DICOM implementations (CT, RT Structure Set, RT Dose, RT Plan, and RT Image) match ATC's conformance statement. This presentation will review lessons learned in this important effort. Methods and Materials: ITC hosted a series of DICOM Implementers' Workshops to assist TPS vendors in implementing RT objects needed for clinical trials. A system of software (“ATC Method 1”) developed at ITC to receive, process, and review volumetric treatment planning data for advanced technology clinical trials was used to assist vendors in their implementation of DICOM export. ATC's DICOM conformance statement specifies requirements for using DICOM RT objects in these clinical trials. ITC's DICOM fileset reader converts incoming data to an internal format for efficient display and review using the ITC web‐based Remote Review Tool (RRT). The RRT was used by TPS developers to visualize/compare submitted images, structure sets, and dose distributions, thus greatly facilitating their DICOM implementations. Results: Interactions with developers have exposed several problems in interpretation and implementation of the DICOM standard resulting from the complexity of DICOM RT objects and differences in design/capabilities of TPSs. Examples of problems seen include CT/Structure/Dose miss‐registration and DVH‐calculation discrepancies. To date, 6 TPSs have released ATC‐compliant DICOM export software. ITC has received DICOM data matching the ATC conformance statement from a total of 15 TPSs. ITC has worked with 8 additional TPS developers. Conclusions: The ITC web‐based Remote Review Tool has proven to be of great help to vendors in developing and verifying implementations. More effort is needed by vendors to make digital data submission for clinical trials a simpler process. Support: NIH U24 CA81647.

Differential Regulation of Pulmonary Endothelial Monolayer Integrity by Varying Degrees of Cyclic Stretch

Ventilator-induced lung injury is a life-threatening complication of mechanical ventilation at high-tidal volumes. Besides activation of proinflammatory cytokine production, excessive lung distension directly affects blood-gas barrier and lung vascular permeability. To investigate whether restoration of pulmonary endothelial cell (EC) monolayer integrity after agonist challenge is dependent on the magnitude of applied cyclic stretch (CS) and how these effects are linked to differential activation of small GTPases Rac and Rho, pulmonary ECs were subjected to physiologically (5% elongation) or pathologically (18% elongation) relevant levels of CS. Pathological CS enhanced thrombin-induced gap formation and delayed monolayer recovery, whereas physiological CS induced nearly complete EC recovery accompanied by peripheral redistribution of focal adhesions and cortactin after 50 minutes of thrombin. Consistent with differential effects on monolayer integrity, 18% CS enhanced thrombin-induced Rho activation, whereas 5% CS promoted Rac activation during the EC recovery phase. Rac inhibition dramatically attenuated restoration of monolayer integrity after thrombin challenge. Physiological CS preconditioning (5% CS, 24 hours) enhanced EC paracellular gap resolution after step-wise increase to 18% CS (30 minutes) and thrombin challenge. These results suggest a critical role for the CS amplitude and the balance between Rac and Rho in mechanochemical regulation of lung EC barrier.