bluuebunny/crossref_metadata_2025 huggingface dataset:
TU‐G‐213‐03: IEC Subcommittee 62C (Equipment for Radiotherapy, Nuclear Medicine and Radiation Dosimetry): Recent and Active Projects
Authors: Culberson W.
Published: June 2015

The International Electrotechnical Commission (IEC) writes standards that manufacturers of electrical equipment must comply with. Medical electrical equipment, such as medical imaging, radiation therapy, and radiation dosimetry devices, fall under Technical Committee 62. Of particular interest to medical physicists are the standards developed within Subcommittees (SC) 62B, which addresses diagnostic radiological imaging equipment, and 62C, which addresses equipment for radiation therapy, nuclear medicine and dosimetry. For example, a Working Group of SC 62B is responsible for safety and quality assurance standards for CT scanners and a Working Group of SC 62C is responsible for standards that set requirements for dosimetric safety and accuracy of linacs and proton accelerators. IEC standards thus have an impact on every aspect of a medical physicist's job, including equipment testing, shielding design, room layout, and workflow. Consequently, it is imperative that US medical physicists know about existing standards, as well as have input on those under development or undergoing revision.

The structure of the IEC and current standards development work will be described in detail. The presentation will explain how US medical physicists can learn about IEC standards and contribute to their development.

Learning Objectives:

Learn about the structure of the IEC and the influence that IEC standards have on the design of equipment for radiology and radiation therapy.

Learn about the mechanisms by which the US participates in the development and revision of standards.

Understand the specific requirements of several standards having direct relevance to diagnostic and radiation therapy physicists.
TU‐G‐BRD‐04: A Round Robin Dosimetry Intercomparison of Gamma Stereotactic Radiosurgery Calibration Protocols
Authors: Drzymala R , Alvarez P , Bednarz G , Bourland J , DeWerd L , Ma L , Meltsner S , Neyman G , Novotny J , Petti P , Rivard M , Shiu A , Goetsch S
Published: June 2015

Purpose:

The purpose of this multi‐institutional study was to compare two new gamma stereotactic radiosurgery (GSRS) dosimetry protocols to existing calibration methods. The ultimate goal was to guide AAPM Task Group 178 in recommending a standard GSRS dosimetry protocol.

Methods:

Nine centers (ten GSRS units) participated in the study. Each institution made eight sets of dose rate measurements: six with two different ionization chambers in three different 160mm‐diameter spherical phantoms (ABS plastic, Solid Water and liquid water), and two using the same ionization chambers with a custom in‐air positioning jig. Absolute dose rates were calculated using a newly proposed formalism by the IAEA working group for small and non‐standard radiation fields and with a new air‐kerma based protocol. The new IAEA protocol requires an in‐water ionization chamber calibration and uses previously reported Monte‐Carlo generated factors to account for the material composition of the phantom, the type of ionization chamber, and the unique GSRS beam configuration. Results obtained with the new dose calibration protocols were compared to dose rates determined by the AAPM TG‐21 and TG‐51 protocols, with TG‐21 considered as the standard.

Results:

Averaged over all institutions, ionization chambers and phantoms, the mean dose rate determined with the new IAEA protocol relative to that determined with TG‐21 in the ABS phantom was 1.000 with a standard deviation of 0.008. For TG‐51, the average ratio was 0.991 with a standard deviation of 0.013, and for the new in‐air formalism it was 1.008 with a standard deviation of 0.012.

Conclusion:

Average results with both of the new protocols agreed with TG‐21 to within one standard deviation. TG‐51, which does not take into account the unique GSRS beam configuration or phantom material, was not expected to perform as well as the new protocols. The new IAEA protocol showed remarkably good agreement with TG‐21.

Conflict of Interests: Paula Petti, Josef Novotny, Gennady Neyman and Steve Goetsch are consultants for Elekta Instrument A/B; Elekta Instrument AB, PTW Freiburg GmbH, Standard Imaging, Inc., and The Phantom Laboratory, Inc. loaned equipment for use in these experiments; The University of Wisconsin Accredited Dosimetry Calibration Laboratory provided calibration services.
TU‐G‐BRD‐05: Results From Multi‐Institutional Measurements with An Anthropomorphic Spine Phantom
Authors: Molineu A , Hernandez N , Alvarez P , Followill D
Published: June 2015

Purpose:

To analyze the results from an anthropomorphic spine phantom used for credentialing institutions for National Cancer Institute (NCI) sponsored clinical trial.

Methods:

An anthropomorphic phantom that contains left and right lungs, a heart, an esophagus, spinal cord, bony material and a PTV was sent to institutions wishing to be credentialed for NCI trials. The PTV holds 4 TLD and radiochromic film in the axial and sagittal planes. The heart holds one TLD. Institutions created IMRT plans to cover ≥90% of the PTV with 6 Gy and limit the cord dose to <0.35cc receiving 3.75 Gy and <1.2cc receiving 2.63 Gy. They were instructed to treat the phantom as they would a patient, including making plan specific IMRT/SBRT QA measurements before treatment. The TLD results in the PTV were required to be within ±7% of the plan dose. A gamma calculation was performed using the film results and the submitted DICOM plan. ≥85% of the analyzed region was required to pass a 5%/3 mm criteria.

Results:

176 institutions irradiated the spine phantom for a total of 255 results. The pass rate was 73% (187 irradiations) overall. 44 irradiations failed only the gamma criteria, 2 failed only the dose criteria and 22 failed both. The most used planning systems were Eclipse (116) and Pinnacle (52) and they had pass rates of 76% and 71%, respectively. The AAA algorithm had a pass rate of 77% while superposition type algorithms had a 71% pass rate. The average TLD measurement to institution calculation ratio was 0.99 (0.04 std dev.). The average percent pixels passing the gamma criteria for films was 89% (12% std dev.)

Conclusion:

Results show that this phantom is an important part of credentialing and that we have room for improvement in IMRT/SBRT spine treatments.

This work was supported by PHS CA180803 and CA037422 awarded by NCI, DHHS.
TU‐G‐BRD‐09: Evaluation of Patient DVH‐Based QA Metrics for Prostate VMAT: Correlation Between Accuracy of Estimated 3D Patient Dose and MLC Position Error
Authors: Kadoya N , Saito M , Fujita Y , Ogasawara M , Ito K , Sato K , Kishi K , Dobashi S , Takeda K , Jingu K
Published: June 2015

Purpose:

The purpose of this study was to evaluate the accuracy of patient DVH‐based QA metrics and test the hypothesis: measurement‐guided 3D dose reconstruction (MGDR) captures the induced dose error.

Methods:

We used 3DVH software with an ArcCHECK to estimate 3D patient dose by MGDR. Two different calculating modes of MGDR were used: “Normal Sensitivity” and “High Sensitivity”. Ten prostate cancer patients treated with hypo‐fractionated VMAT (67.6 Gy/26 Fr) were studied. For the baseline plan, we induced MLC errors (−0.75, −0.5, −0.25, 0.25, 0.5 and 0.75 mm for each single bank), generated by in‐house software. We compared the 3D patient dose estimated by 3DVH and that calculated by the treatment planning system. We evaluated the correlation between dose estimation error and MLC position error.

Results:

Slopes of linear fit to dose estimation error versus MLC position error for mean dose and D95 to the PTV were 1.76 and 1.40% mm‐1, respectively, for “Normal Sensitivity” and −0.53 and −0.88% mm‐1, respectively, for “High Sensitivity”, showing better accuracy for “High Sensitivity” than “Normal Sensitivity”. On the other hand, the slopes for mean dose to the rectum and bladder and V55 to the rectum and bladder were −1.00, −0.55, −3.53 and −1.85% mm‐1, respectively, for “Normal Sensitivity” and −2.89, −2.39, −6.24 and −4.11% mm‐1, respectively, for “High Sensitivity”, showing significant better accuracy for “Normal Sensitivity” than “High Sensitivity”.

Conclusion:

Our results showed that 3DVH had some residual error for both sensitivities, indicating the MGDR could capture a part of the induced error but not all of the induced error.Furthermore, we found that “Normal Sensitivity” might have better accuracy for the DVH metric for PTV and that “High Sensitivity” might have better accuracy for DVH metrics for the rectum and bladder. We must be willing to tolerate this residual error in clinical care.
TU‐G‐BRB‐00: Clinical Trials in Proton and Particle Therapy
Authors: Schulte Reinhard
Published: June 2015

Proton therapy, in particular, and ion therapy, just beginning, are becoming an increasing focus of attention in clinical radiation oncology and medical physics. Both modalities have been criticized of lacking convincing evidence from randomized trials proving their efficacy, justifying the higher costs involved in these therapies.

This session will provide an overview of the current status of clinical trials in proton therapy, including recent developments in ion therapy. As alluded to in the introductory talk by Dr. Schulte, opinions are diverging widely as to the usefulness and need for clinical trials in particle therapy and the challenge of equipoise. The lectures will highlight some of the challenges that surround clinical trials in particle therapy. One, presented by Dr. Choy from UT Southwestern, is that new technology and even different types of particles such as helium and carbon ions are introduced into this environment, increasing the phase space of clinical variables. The other is the issue of medical physics quality assurance with physical phantoms, presented by Mrs. Taylor from IROC Houston, which is more challenging because 3D and 4D image guidance and active delivery techniques are in relatively early stages of development. The role of digital phantoms in developing clinical treatment planning protocols and as a QA tool will also be highlighted by Dr. Lee from NCI.

The symposium will be rounded off by a panel discussion among the Symposium speakers, arguing pro or con the need and readiness for clinical trials in proton and ion therapy.

Learning Objectives:

To get an update on the current status of clinical trials allowing or mandating proton therapy.

Learn about the status of planned clinical trials in the U.S. and worldwide involving ion therapy.

Discuss the challenges in the design and QA of clinical trials in particle therapy.

Learn about existing and future physical and computational anthropomorphic phantoms for charged particle clinical trial development and support.

Research reported in this presentation is supported by the National Cancer Institute of the National; Institutes of Health under Award Number P20CA183640.
TU‐G‐BRA‐04: Changes in Regional Lung Function Measured by 4D‐CT Ventilation Imaging for Thoracic Radiotherapy
Authors: Nakajima Y , Kadoya N , Kabus S , Loo B , Keall P , Yamamoto T
Published: June 2015

Purpose:

To test the hypothesis: 4D‐CT ventilation imaging can show the known effects of radiotherapy on lung function: (1) radiation‐induced ventilation reductions, and (2) ventilation increases caused by tumor regression.

Methods:

Repeat 4D‐CT scans (pre‐, mid‐ and/or post‐treatment) were acquired prospectively for 11 thoracic cancer patients in an IRB‐approved clinical trial. A ventilation image for each time point was created using deformable image registration and the Hounsfield unit (HU)‐based or Jacobian‐based metric. The 11 patients were divided into two subgroups based on tumor volume reduction using a threshold of 5 cm3. To quantify radiation‐induced ventilation reduction, six patients who showed a small tumor volume reduction (<5 cm3) were analyzed for dose‐response relationships. To investigate ventilation increase caused by tumor regression, two of the other five patients were analyzed to compare ventilation changes in the lung lobes affected and unaffected by the tumor. The remaining three patients were excluded because there were no unaffected lobes.

Results:

Dose‐dependent reductions of HU‐based ventilation were observed in a majority of the patient‐specific dose‐response curves and in the population‐based dose‐response curve, whereas no clear relationship was seen for Jacobian‐based ventilation. The post‐treatment population‐based dose‐response curve of HU‐based ventilation demonstrated the average ventilation reductions of 20.9±7.0% at 35–40 Gy (equivalent dose in 2‐Gy fractions, EQD2), and 40.6±22.9% at 75–80 Gy EQD2. Remarkable ventilation increases in the affected lobes were observed for the two patients who showed an average tumor volume reduction of 37.1 cm3 and re‐opening airways. The mid‐treatment increase in HU‐based ventilation of patient 3 was 100.4% in the affected lobes, which was considerably greater than 7.8% in the unaffected lobes.

Conclusion:

This study has demonstrated that 4D‐CT ventilation imaging shows the known effects of radiotherapy on lung function: radiation‐induced ventilation reduction and ventilation increase caused by tumor regression, providing validation for 4D‐CT ventilation imaging.

This study was supported in part by a National Lung Cancer Partnership Young Investigator Research grant.
TU‐G‐BRA‐06: PET‐Based Treatment Response Assessement for Neoadjuvent Chemoradiation for Pancreatic Adenocarcinoma
Authors: Dalah E , Tai A , Oshima K , Hall W , Erickson B , Li X
Published: June 2015

Purpose:

To address the limitations of the conventional response evaluation criteria in solid tumors (RECIST), and validate PET response criteria in solid tumors (PERCIST1.0). We analyze the relationship between the pathological treatment response (PTR) and PERCIST1.0 for patients treated with neoadjuvent chemoradiation (nCR) for pancreatic adenocarcinoma.

Methods:

The pre‐ and post‐nCR CT and PET data for a total of 8 patients with resectable, or borderline resectable pancreatic head adenocarcinoma treated with nCR were retrospectively analyzed. These data were compared with the PTR which were graded according to tumor cell destruction (cellularity), with Grade1, 2 or 3 (G1, G2 or G3) for good, moderate, and poor responses, respectively. RECIST‐based PET (RECISTPET), and PERCIST1.0 were defined using lean body mass normalized SUV (nSUVlb). RECIST‐based CT (RECISTCT) was defined by contouring the whole pancreas head (CTPH). Pre‐ and post‐nSUVlb and SUVbw, PERCIST 1.0, were correlated with PTR using Pearson's correlation coefficient test.

Results:

The average mean and SD in nSUVlb for all 8 patients analyzed were lower in post‐nCR (1.35±0.34) compared to those at pre‐nCR (1.38±0.20). Using PERCIST1.0, 5/8 patients showed stable metabolic disease (SMD), 2/8 partial metabolic response (PMR), and 1/8 progressive metabolic disease (PMD). Using RECISTPET 4/8 showed stable disease (STD), 4/8 partial response (PR), whereas 8/8 showed stable disease (STD) using RECISTCT. PTR were correlated with PERCIST1.0 (R=0.3780/P=0.6071). Pathological tumor size was correlated with RECISTCT (R=0.0727/P=0.8679), and RECISTPET, R=−0.3333/P=0.3798.

Conclusion:

Chemoradiation treatment response assessment based on metabolic tumor activities using PRECIST1.0 and RECISTPET appears to provide better agreement with pathological assessment as compared to the conventional CT‐based assessment using RECISTCT. The integration of these additional radiographic metrics in assessing treatment response to nCR for pancreatic adenocarcinoma may provide a promising strategy to better select those patients most suitable for therapeutic intensification.
TU‐G‐BRA‐09: MR Elastography as a Predictor of Therapeutic Response: Assessment in Non‐Hodgkin's Lymphoma (NHL)
Authors: Pepin K , Ansell S , Ehman R , McGee K
Published: June 2015

Purpose:

To develop magnetic resonance elastography (MRE) as a noninvasive imaging biomarker of treatment response in cancer and to evaluate its prognostic potential for assessing chemotherapy response in non‐Hodgkin's lymphoma (NHL).

Methods:

A small animal cancer MRE methodology was developed which included a custom‐built 4 cm, 8‐channel receive‐only coil and an electromechanical driver capable of producing shear waves at a frequency of 800 Hz attached to an MRI‐compatible needle inserted in tissue. MRE reproducibility was evaluated in tumor phantoms and in vivo mouse experiments. Tumor shear stiffness was monitored following a single chemotherapy treatment in a murine xenograft of NHL to determine the biomechanical effect of an initial therapy response followed by tumor regrowth. The effect of an increased chemotherapy dose on the resulting decrease in tumor shear stiffness was quantified. In vivo evaluation of the feasibility of this potential biomarker was performed on three patients with NHL prior to and 2 weeks after chemotherapy treatment.

Results:

Tumor phantom models and in vivo measurements demonstrated excellent shear stiffness reproducibility with a standard deviation of ±3%. In the murine model, tumor shear stiffness and volume changes following chemotherapy treatment corresponded to periods of response and subsequent tumor regrowth. Shear stiffness alterations demonstrated a dose dependency with a decrease of 15% and 25% for the low and high dose group respectively compared to an increase of 7% in an untreated control group. In three patients with NHL, tumor shear stiffness decreased by an average of 38% within 2 weeks of chemotherapy treatment.

Conclusion:

These data demonstrate that MRE is a potentially sensitive biomarker of disease stage and response to therapy in cancer. The results demonstrate that MRE‐derived shear stiffness measurements are sensitive to chemotherapy response and disease progression in vivo, and are the first human application of MRE as a noninvasive assessment of therapy response.

Funding provided by the Mayo Clinic Center for Individualized Medicine, Imaging Biomarker Discovery Program and the Mayo Graduate School.
TU‐G‐303‐02: Robust Radiomics Methods for PET and CT Imaging
Authors: Aerts H.
Published: June 2015

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan‐omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi‐modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics‐derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low‐grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image‐based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi‐modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma.

Learning Objectives:

Understanding the basic image features that are often used in radiomic models.

Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability.

Understanding the methodology behind radiomic‐genomic (‘radiogenomics’) correlations.

Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)
TU‐G‐303‐04: Radiomics and the Coming Pan‐Omics Revolution
Authors: El Naqa I.
Published: June 2015

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan‐omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi‐modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics‐derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low‐grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image‐based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi‐modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma.

Learning Objectives:

Understanding the basic image features that are often used in radiomic models.

Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability.

Understanding the methodology behind radiomic‐genomic (‘radiogenomics’) correlations.

Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)
TU‐G‐204‐03: Dynamic CT Myocardial Perfusion Measurement Using First Pass Analysis and Maximum Slope Models
Authors: Hubbard L , Ziemer B , Sadeghi B , Javan H , Lipinski J , Molloi S
Published: June 2015

Purpose:

To evaluate the accuracy of dynamic CT myocardial perfusion measurement using first pass analysis (FPA) and maximum slope models.

Methods:

A swine animal model was prepared by percutaneous advancement of an angioplasty balloon into the proximal left anterior descending (LAD) coronary artery to induce varying degrees of stenosis. Maximal hyperaemia was achieved in the LAD with an intracoronary adenosine drip (240 µg/min). Serial microsphere and contrast (370 mg/mL iodine, 30 mL, 5mL/s) injections were made over a range of induced stenoses, and dynamic imaging was performed using a 320‐row CT scanner at 100 kVp and 200 mA. The FPA CT perfusion technique was used to make vessel‐specific myocardial perfusion measurements. CT perfusion measurements using the FPA and maximum slope models were validated using colored microspheres as the reference gold standard.

Results:

Perfusion measurements using the FPA technique (P_FPA) showed good correlation with minimal offset when compared to perfusion measurements using microspheres (P_ Micro) as the reference standard (P _FPA = 0.96 P_Micro + 0.05, R2 = 0.97, RMSE = 0.19 mL/min/g). In contrast, the maximum slope model technique (P_MS) was shown to underestimate perfusion when compared to microsphere perfusion measurements (P_MS = 0.42 P _Micro −0.48, R2 = 0.94, RMSE = 3.3 mL/min/g).

Conclusion:

The results indicate the potential for significant improvements in accuracy of dynamic CT myocardial perfusion measurement using the first pass analysis technique as compared with the standard maximum slope model.
TU‐G‐204‐04: A Unified Strategy for Bi‐Factorial Optimization of Radiation Dose and Contrast Dose in CT Imaging
Authors: Sahbaee P , Zhang Y , Solomon J , Becchetti M , Segars P , Samei E
Published: June 2015

Purpose:

To substantiate the interdependency of contrast dose, radiation dose, and image quality in CT towards the patient‐ specific optimization of the imaging protocols

Methods:

The study deployed two phantom platforms. A variable sized (12, 18, 23, 30, 37 cm) phantom (Mercury‐3.0) containing an iodinated insert (8.5 mgI/ml) was imaged on a representative CT scanner at multiple CTDI values (0.7–22.6 mGy). The contrast and noise were measured from the reconstructed images for each phantom diameter. Linearly related to iodine‐concentration, contrast‐to‐noise ratio (CNR), were calculated for 16 iodine‐concentration levels (0–8.5 mgI/ml). The analysis was extended to a recently developed suit of 58 virtual human models (5D XCAT) with added contrast dynamics. Emulating a contrast‐enhanced abdominal image procedure and targeting a peak‐enhancement in aorta, each XCAT phantom was “imaged” using a simulation platform (CatSim, GE). 3D surfaces for each patient/size established the relationship between iodine‐concentration, dose, and CNR. The ratios of change in iodine‐concentration versus dose (IDR) to yield a constant change in CNR were calculated for each patient size.

Results:

Mercury phantom results show the image‐quality size‐ dependence on CTDI and IC levels. For desired image‐quality values, the iso‐contour‐lines reflect the trade off between contrast‐material and radiation doses. For a fixed iodine‐concentration (4 mgI/mL), the IDR values for low (1.4 mGy) and high (11.5 mGy) dose levels were 1.02, 1.07, 1.19, 1.65, 1.54, and 3.14, 3.12, 3.52, 3.76, 4.06, respectively across five sizes. The simulation data from XCAT models confirmed the empirical results from Mercury phantom.

Conclusion:

The iodine‐concentration, image quality, and radiation dose are interdependent. The understanding of the relationships between iodine‐concentration, image quality, and radiation dose will allow for a more comprehensive optimization of CT imaging devices and techniques, providing the methodology to balance iodine‐concentration and dose based on patient's attributes.
TU‐G‐204‐05: The Effects of CT Acquisition and Reconstruction Conditions On Computed Texture Feature Values of Lung Lesions
Authors: Lo P , Young S , Kim G , Hoffman J , Brown M , McNitt‐Gray M
Published: June 2015

Purpose:

Texture features have been investigated as a biomarker of response and malignancy. Because these features reflect local differences in density, they may be influenced by acquisition and reconstruction parameters. The purpose of this study was to investigate the effects of radiation dose level and reconstruction method on features derived from lung lesions.

Methods:

With IRB approval, 33 lung tumor cases were identified from clinically indicated thoracic CT scans in which the raw projection (sinogram) data were available. Based on a previously‐published technique, noise was added to the raw data to simulate reduced‐dose versions of each case at 25%, 10% and 3% of the original dose. Original and simulated reduced dose projection data were reconstructed with conventional and two iterative‐reconstruction settings, yielding 12 combinations of dose/recon conditions. One lesion from each case was contoured. At the reference condition (full dose, conventional recon), 17 lesions were randomly selected for repeat contouring (repeatability). For each lesion at each dose/recon condition, 151 texture measures were calculated. A paired differences approach was employed to compare feature variation from repeat contours at the reference condition to the variation observed in other dose/recon conditions (reproducibility). The ratio of standard deviation of the reproducibility to repeatability was used as the variation measure for each feature.

Results:

The mean variation (standard deviation) across dose levels and kernel was significantly different with a ratio of 2.24 (±5.85) across texture features (p=0.01). The mean variation (standard deviation) across dose levels with conventional recon was also significantly different with 2.30 (7.11) (p=0.025). The mean variation across reconstruction settings of original dose has a trend in showing difference with 1.35 (2.60) among all features (p=0.09).

Conclusion:

Texture features varied considerably with variations in dose and reconstruction condition. Care should be taken to standardize these conditions when using texture as a quantitative feature.

This effort supported in part by a grant from the National Cancer Institute's Quantitative Imaging Network (QIN): U01 CA181156; The UCLA Department of Radiology has a Master Research Agreement with Siemens Healthcare; Dr. McNitt‐Gray has previously received research support from Siemens Healthcare.
TU‐G‐204‐06: Correlation Between Texture Analysis‐Based Model Observer and Human Observer in Diagnosis of Ischemic Infarct in Non‐Contrast Head CT of Adults
Authors: Li B , Fujita A , Buch K , Sakai O
Published: June 2015

Purpose:

To investigate the correlation between texture analysis‐based model observer and human observer in the task of diagnosis of ischemic infarct in non‐contrast head CT of adults.

Methods:

Non‐contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro‐reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro ‐radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9‐point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro‐radiologist placed multiple ROIs over the infarcts. These ROIs were entered into a texture analysis software package. Forty‐two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2‐D features, were computed and averaged over the images per dataset. The Fisher‐coefficient (ratio of between‐class variance to in‐class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher ‐coefficient were entered into linear multivariate regression for iterative modeling.

Results:

Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p‐value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R‐sq=0.77). The root‐mean‐square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased.

Conclusion:

For diagnosis of ischemic infarct in non‐contrast head CT in adults, the predicted image quality scores from texture analysis‐based model observer was highly correlated with that of human observers for various noise levels. Texture‐based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.
TU‐G‐207‐02: Clinical Applications of X‐Ray Phase Contrast Imaging: Where Do We Stand?
Authors: Li K.
Published: June 2015

Last few years has witnessed the development of novel of X‐ray imaging modalities, such as spectral CT, phase contrast CT, and X‐ray acoustic/fluorescence/luminescence imaging. This symposium will present the recent advances of these emerging X‐ray imaging modalities and update the attendees with knowledge in various related topics, including X‐ray photon‐counting detectors, X‐ray physics underlying the emerging applications beyond the traditional X‐ray imaging, image reconstruction for the novel modalities, characterization and evaluation of the systems, and their practical implications. In addition, the concept and practical aspects of X‐ray activatable targeted nanoparticles for molecular X‐ray imaging will be discussed in the context of X‐ray fluorescence and luminescence CT.

Learning Objectives:

Present background knowledge of various emerging X‐ray imaging techniques, such as spectral CT, phase contrast CT and X‐ray fluorescence/luminescence CT.

Discuss the practical need, technical aspects and current status of the emerging X‐ray imaging modalities.

Describe utility and future impact of the new generation of X‐ray imaging applications.
TU‐G‐201‐02: An MRI Simulator From Proposal to Operation
Authors: Cao Y.
Published: June 2015

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment.

The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, and potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts.

The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4‐zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed.

In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment.

Learning Objectives:

Learn the latest innovations of major imaging systems relevant to radiation therapy

Be able to describe important technical specifications of CT, MRI, and PET/CT

Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development
TU‐G‐210‐02: TRANS‐FUSIMO ‐ An Integrative Approach to Model‐Based Treatment Planning of Liver FUS
Authors: Preusser T.
Published: June 2015

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney.

Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning

Tobias Preusser – TRANS‐FUSIMO – An Integrative Approach to Model‐Based Treatment Planning of Liver FUS

Tobias Preusser – TRANS‐FUSIMO – An Integrative Approach to Model‐Based Treatment Planning of Liver FUS

Learning Objectives:

Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments.

Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies.

Determine how to obtain appropriate acoustic modeling parameters from clinical images.

Understand the separate role of absorption and scattering in energy delivery to tissues.

See how organ motion can be compensated for in ultrasound therapies.

Compare simulated data with clinical temperature measurements in transcranial applications.

Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007‐2013) under 270186 (FUSIMO) and 611889 (TRANS‐FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV).
TU‐F‐CAMPUS‐T‐01: Dose and Energy Spectra From Neutron Induced Radioactivity in Medical Linear Accelerators Following High Energy Total Body Irradiation
Authors: Keehan S , Taylor M , Smith R , Dunn L , Kron T , Franich R
Published: June 2015

Purpose:

To assess the risk posed by neutron induced activation of components in medical linear accelerators (linacs) following the delivery of high monitor unit 18 MV photon beams such as used in TBI.

Methods:

Gamma spectroscopy was used to identify radioisotopes produced in components of a Varian 21EX and an Elekta Synergy following delivery of photon beams. Dose and risk estimates for TBI were assessed using dose deliveries from an actual patient treatment. A 1 litre spherical ion chamber (PTW, Germany) has been used to measure the dose at the beam exit window and at the total body irradiation (TBI) treatment couch following large and small field beams with long beam‐on times. Measurements were also made outside of the closed jaws to quantify the benefit of the attenuation provided by the jaws.

Results:

The radioisotopes produced in the linac head have been identified as 18⁷W, ⁵⁶Mn, 2⁴Na and 2⁸Al, which have half‐lives from between 2.3 min to 24 hours. The dose at the beam exit window following an 18 MV 2197 MU TBI beam delivery was 12.6 µSv in ten minutes. The dose rate at the TBI treatment couch 4.8 m away is a factor of ten lower. For a typical TBI delivered in six fractions each consisting of four beams and an annual patient load of 24, the annual dose estimate for a staff member at the treatment couch for ten minutes is 750 µSv. This can be further reduced by a factor of about twelve if the jaws are closed before entering the room, resulting in a dose estimate of 65 µSv.

Conclusion:

The dose resulting from the activation products for a representative TBI workload at our clinic of 24 patients per year is 750 µSv, which can be further reduced to 65 µSv by closing the jaws.
TU‐F‐CAMPUS‐T‐04: An Evaluation of Out‐Of‐Field Doses for Electron Beams From Modern Varian and Elekta Linear Accelerators
Authors: Cardenas C , Nitsch P , Kudchadker R , Howell R , Kry S
Published: June 2015

Purpose:

Accurately determining out‐of‐field doses when using electron beam radiotherapy is of importance when treating pregnant patients or patients with implanted electronic devices. Scattered doses outside of the applicator field in electron beams have not been broadly investigated, especially since manufacturers have taken different approaches in applicator designs.

Methods:

In this study, doses outside of the applicator field were measured for electron beams produced by a 10×10 applicator on two Varian 21iXs operating at 6, 9, 12, 16, and 20 MeV, a Varian TrueBeam operating at 6, 9, 12, 16, and 20 MeV, and an Elekta Versa HD operating at 6, 9, 12 and 15 MeV. Peripheral dose profiles and percent depth doses were measured in a Wellhofer water phantom at 100 cm SSD with a Farmer ion chamber. Doses were compared to peripheral photon doses from AAPM's Task Group #36 report.

Results:

Doses were highest for the highest electron energies. Doses typically decreased with increasing distance from the field edge but showed substantial increases over some distance ranges. Substantial dose differences were observed between different accelerators; the Elekta accelerator had much higher doses than any Varian unit examined. Surprisingly, doses were often similar to, and could be much higher than, doses from photon therapy. Doses decreased sharply with depth before becoming nearly constant; the dose was found to decrease to a depth of approximately E(MeV)/4 in cm.

Conclusion:

The results of this study indicate that proper shielding may be very important when utilizing electron beams, particularly on a Versa HD, while treating pregnant patients or those with implanted electronic devices. Applying a water equivalent bolus of Emax(MeV)/4 thickness (cm) on the patient would reduce fetal dose drastically for all clinical energies and is a practical solution to manage the potentially high peripheral doses seen from modern electron beams.

Funding from NIH Grant number: #CA180803
TU‐F‐CAMPUS‐J‐03: Prediction of Respiratory Motion Using State Space Models
Authors: Kotoku J , Kumagai S , Haga A , Nakabayashi S , Arai N , Kobayashi T
Published: June 2015

Purpose:

The purpose of this study is to predict respiratory motion for a few seconds ahead by use of dynamic linear models. The models describe trends and periodic components of time series of respiratory curves obtained on patient's body.

Methods:

To measure spatial coordinates of multiple points on patient's body during respiratory motion, we used a consumer depth camera (Microsoft Kinect) and obtained depth data via triangulation from infrared random dots patterns.To describe a dynamics of respiratory motion, we selected a symplectic form of a harmonic oscillator. As a filter, we selected a particle filter. Particle filter is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations. The key idea is to represent the required posterior density function by a set of random samples and to compute estimates based on these samples.

Results:

Filtered values were calculated as a mean of a filtered distribution. The prediction values were well correlated with the values to be observed.To validate accuracy of our model, predicted depth values were compared with measured ones. The accuracy of our model was roughly 20 % for 4 seconds ahead in average, while the measurement depth accuracy is within 1 mm.

Conclusion:

Time‐series modeling using Bayesian inference technique is useful for prediction of respiratory motion.Although prediction accuracy became worse along with the length of forecasting time, we can conclude this method is a promising tool for prediction of patient's motion.

This work was partly supported by the JSPS Core‐to‐Core Program (No. 23003).
TU‐F‐CAMPUS‐I‐03: Enhancement of 4D CBCT Image Quality Using An Adaptive Prior Image Constrained Compressed Sensing
Authors: Lee H , Yoon J , Lee E , Cho S , Park K , Choi W , Keum K
Published: June 2015

Purpose:

To develop an iterative reconstruction algorithm using a compressed sensing with adaptive prior image constraints to solve 4D CBCT reconstruction problem.

Methods:

The images reconstructed by the FDK algorithm with a full set of unsorted projections are served as prior images for partial projections in each phase group and are utilized as an initial guess. Additionally, the prior images are clustered into several regions by applying intensity‐based thresholding, which is referred to as the segmented prior images. The segmented prior images are employed to detect any possible mismatched areas compared with the target images generated by partial projection data. With these two prior images, our algorithm alternately performs the simultaneous algebraic reconstruction technique and anisotropic total variation regularization while adjusting a weighted relaxation map during the iterative reconstruction process. The weighted relaxation map depends on binary images created by the voxel‐dependent comparison between the segmented prior and segmented target images. For the segmented target images, the k‐means clustering with a geometric weighting is applied on the reconstruction images generated in each iteration step. The inverse values of the distance map converted from binary images are assigned to be the values of the relaxation map. Evaluations using Catphan504 phantom with a motion platform were carried out.

Results:

Qualitative and quantitative analyses showed that the method provides high‐quality CBCT reconstruction images when compared with those generated by the FDK, CS, and PICCS algorithms, with higher contrast‐to‐noise ratio and faster convergence caused by minimizing data fidelity. Especially, the proposed method was superior to PICCS in the aspect of updating locally‐mismatched region.

Conclusion:

The proposed method not only improves the image quality of 4D CBCT by adaptive updates during the reconstruction process, but also leads to a lower imaging dose and faster acquisition time by using a regular 3D CBCT scan.
WE‐E‐BRB‐00: Knowledge‐Based Treatment Planning Automation
Authors: Zhang Xiaodong
Published: June 2015

The knowledge‐based treatment planning automation has evolved from academic research from the clinical application with the involvement of the major industry TPS vendors. Previous work proved the concept that IMRT or VMAT plans can be designed without or with minimal human intervention. Recently, the major industry vendors start releasing the automatic planning product such as Philips automatic treatment planning, Varian's Rapidplan, and RaySearch's autoplan. Many centers start to evaluate and use the automatic planning products from TPS vendors. It is more and more becoming evident that automatic planning will be routinely used in the clinic.

The first presentation will focus on the knowledge modeling for clinical treatment planning. The presenter will introduce the algorithm and theory for developing the dose volume histogram (DVH) model, summarize the results on the validation of the DVH model, and discuss the application of DVH for the automatic treatment planning. The second presenter will focus on the clinical use of the automatic treatment planning. The presenter will give an overview of the several automatic treatment planning tools including the tools developed commercially and in‐house, present the early experience of using those tools, and discuss the application of the tools in the routine clinical use and in the quality control of the treatment planning.

Learning Objectives:

Describe the algorithm and theory behind the knowledge based treatment planning automation.

Clinical experience of using automatic planning in clinical trial and patient treatment

Early clinical experience of using commercial automatic planning approach

The application of automatic planning approach to perform quality assurance of treatment plan.

XD obtain the research funding from Varian Medical System, Philips Medical System and Funding support form Sister Institution Network Fund of MD Anderson, XD is also a founder of Global Oncology One