bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐E‐213A‐03: Everything You Need to Know About the Cyberknife, But Were Afraid to Ask
Authors: Masterson‐McGary M 
Published: April 2010

The CyberKnife (Accuray, Inc. Sunnyvale, CA) is a stereotactic radiotherapy unit, designed to treat both intracranial and extracranial targets with sub‐millimeter spatial accuracy. An industrial robot has a compact 6 MV x‐band linac as its payload. The robot is able to orient the linac with six degrees of freedom, and to aim its x‐ray beam at the target volume in a non‐isocentric and non‐coplanar fashion. Typically, hundreds of beams (selected from thousands of possible beam angles) are delivered during each treatment fraction. A bi‐plane digital radiography system determines and feeds‐back target position coordinates to the robot throughout treatment, allowing the robot to continuously correct its aim to account for intra‐fraction patient and target movement. The CyberKnife uses several different tracking methodologies depending upon the type of target being treated. Both rigid body and deformable body geometries are employed. Special correlative tracking techniques have been developed to track targets that move with respiration. With nearly real‐time correction for target motion and highly precise dose delivery, the CyberKnife allows the practitioner to employ tighter margins and to deliver high doses per fraction accurately.This talk is intended to review the unique features of the CyberKnife system, and to discuss how those features require special considerations in site planning, acceptance testing, commissioning, quality assurance, treatment planning, and clinical implementation. Examples of common clinical applications and dose regimens will also be presented.Learning Objectives:1. Understand the fundamental design and functionality of the CyberKnife system.2. Understand the impact of machine design on site planning, acceptance testing, commissioning, treatment planning, as well as system and sub‐system quality assurance.3. Understand the different clinical considerations associated with robotic SRS and SBRT.4. Enable the attendee to introduce a CyberKnife system into his/her clinical practice efficiently, effectively, and safely.
WE‐A‐BRC‐02: IMRT for Prostate Cancer
Authors: Price R 
Published: April 2010

The prostate is a mobile structure compared to the surrounding bony anatomy. Daily setup, immobilization and localization uncertainties can be addressed by increasing the PTV but may result in additional dose to surrounding normal structures. At FCCC we attempt to reduce the uncertainty by employing daily localization using ultrasound, implanted fiducials or through target tracking using implanted Calypso beacons. We currently use an 8mm growth in all directions except posteriorly where 5mm is typical. Patients with fiducials and those being irradiated in the post‐prostatectomy setting undergo localization via an in‐room CT scanner or CBCT. We have recently begun target tracking on post‐prostatectomy patients as well. All patients are simulated and treated supine without a thermoplastic immobilizer to minimize respiratory related prostatic motion. Patients undergo MR and CT simulations with the rectum empty. These data are fused for target and normal structure delineation for use in treatment planning. Emphasis on soft tissue structures is important to avoid potential systematic localization errors due to the time difference between scans for patients with implanted fiducials or beacons. Dose limiting structures primarily include the rectum, bladder, and femoral heads, but may also include bowel and erectile tissues. We have developed “plan acceptance criteria” based on published data with respect to rectal complications. DVH analysis is used to ensure that the rectal volumes receiving 65Gy and 40Gy are less than 17% and 35%, respectively. Additionally, the bladder volumes receiving 65Gy and 40Gy are less than 25% and 50%, respectively. The volume of either femoral head receiving 50Gy should be less than 10%. PTV coverage should result in at least 95% of the volume receiving the prescription dose. It should be noted that the 3D dose distribution itself plays an important role in IMRT delivery and DVH analysis alone may not be sufficient. The isodose distribution should be such that the 90% and 50% lines do not traverse the half or full width of the rectum on any CT slice, respectively. Dose escalation and hypofractionation regimes continue to be of interest in the treatment of this disease site. However, most delivery techniques require PTV reductions to avoid increased normal tissue toxicity. A quantitative analysis of uncertainties contributing to PTV margins is addressed. Additionally, treatment methods that may not require margin consideration, are discussed. These include fiducial tracking using the Cyberknife and the use of high intensity focused ultrasound (HiFU) ablation.Learning Objectives:1. To understand the practical steps associated with IMRT of the prostate2. To understand the numerical values presented for plan acceptance3. To be aware of MR‐CT fusion issues for patients with implanted fiducials or beacons4. To be aware of issues related to PTV reduction
WE‐A‐303A‐01: DCE MRI for Quantitative Assessment of Therapy Response
Authors: Cao Y 
Published: April 2010

Dynamic contrast enhanced (DCE) MR imaging is emerging as a promising biomarker for therapy assessment. While initial results from research groups are promising, a significant gap exists between preliminary research studies and practical utilization. In addition to variations in data acquired from different image acquisition techniques, a large variety of pharmacokinetic models have been explored from which to derive quantitative metrics. Examples of such models include those from Patlack, Toft, and Elizabeth, with a number of others in use in various research groups. These models have various levels of complexity, which in general is inversely related to the robustness with which the models perform on imaging data. Furthermore, the selection of an optimal pharmacokinetic model may be highly dependent on the tissue or organ being investigated. Currently, a national effort is underway to help standardize the extraction of physiological parameters from DCE‐MRI and related pharmacokinetic models. This talk explores these issues, comparing various models and their performance, and provides an overview of the areas that need consideration prior to widespread adoption of DCE‐MRI derived model parameters as assessment tools.Learning Objectives:1. Understand the strengths and weaknesses of commonly used pharmacokinetic models2. Understand the influence of image acquisition parameters and image quality on derived quantitative metrics3. Provide examples of clinical applications
WE‐C‐BRB‐08: Sensitivity of Dosimetric Margin Distribution and Coverage Estimates to Sampling Parameters
Authors: Xu H , Gordon J , Siebers J 
Published: April 2010

Purpose: The dosimetric margin distribution (DMD) is a planning tool for evaluating target and normal tissue dose coverage probabilities in the presence of geometric uncertainties. This work evaluates sensitivity of coverage estimates derived from the DMD to the angular increment w at which the DMD is sampled, and the distance search increment d. Method and Materials: The study utilized three plans for localized prostate cancer, with prescribed dose of 79.2Gy and 1cm clinical target volume to planning target volume (CTV‐to‐PTV) margins, planned according to the high dose arm of RTOG0126. Dose was first recalculated with beam fluences convolved with a normal probability density function to simulate the effect of random uncertainties. Dosimetric margins were then calculated between the CTV and the 79.2Gy isodose surface at elevations f from −90° to +90°, and azimuthal angles q from −180° to +180°. In each direction (f,q) the CTV was moved relative to the dose distribution in steps of d until its minimum dose fell below 79.2Gy. Estimated coverage Qest was calculated from the DMD assuming systematic uncertainties (rigid body translations) were normally distributed with standard deviation S along each axis. Minimum and maximum deviations DQest were obtained over a range of S values for w = 5°, 10°, 20° relative to w = 2°, and for d = 0.2, 0.5, 1mm relative to d = 0.1mm. Results: Coverage uncertainty due to finite angular sampling interval w is approximately 0.5% at w = 10° and 1.5% at w = 20°. Uncertainty due to finite step size d is negligible for d £ 0.2 mm, and is approximately 0.6% for d = 1mm. Conclusion: Coverage estimates accurate to ±0.5% will be obtained if the DMD is sampled with w = 10° and d = 0.2 mm. Coverage estimates accurate to this level are desirable for target and normal tissue coverage evaluation in current treatment plans, and to support future treatment planning based on probabilistic coverage criteria.(Supported by NIH P01CA116602).
WE‐C‐BRC‐05: Use of FLT PET Imaging to Assess Tyrosine Kinase Inhibitor (TKI) Treatment Response
Authors: Jeraj R , Vanderhoek M , Simoncic U , Perlman S , Alberti D , Wilding G , Liu G 
Published: April 2010

Background: Sunitinib (SU) is a tyrosine kinase inhibitor (TKI) with activity against VEGFR. During VEGFR TKI withdrawal, increased pain at sites of metastasis has been observed, which we hypothesize is due to the proliferative flare. []‐fluoro‐3′‐deoxy‐3′‐L‐fluorothymidine (FLT) PET imaging was used as a marker of treatment response, to assess the proliferative flare and to investigate perfusion and vascular status of the tumor. Methods: 14 patients with advanced solid malignancies have been enrolled. SU was given at the standard dose of 50 mg for 4 weeks, followed by a 2 week break. 90‐minute dynamic FLT PET/CT scans were obtained at the baseline, week 4, and week 6. 8 patients with adequate follow‐up time were classified in two groups: with clinical benefit (CB; progressive disease (PD)>6 mo) or without clinical benefit (noCB; PD<6 mo). Changes in the peak FLT standardized uptake value (SUVpeak) were calculated. In addition, FLT kinetic analysis was performed to extract perfusion and vasculature status of the tumor. Results: The differences in FLT uptake were significant between the two groups (CB: −20%, noCB: ∼0%). Interestingly, both groups exhibited a significant increase in SUVpeak during SU withdrawal, but with a significantly higher increase in the group without clinical benefit (CB: +20%, noCB: +50%). The kinetic analysis revealed significant differences in the perfusion at week 4 between the groups (CB: ∼0%, noCB: −40%), with no significant differences at the end of the treatment cycle. Conclusions: Change in SUVpeak was associated with the degree of clinical response. Proliferative increase in the target lesions during SU withdrawal was observed, and was higher for patients with poor clinical outcome.Research sponsored in part by Pfizer.
WE‐D‐304A‐03: Quantitative Image Analysis for Prognosis in Newly Diagnosed Breast Cancer Patients with Sonographically Normal Appearing Lymph Nodes
Authors: Drukker K , Sennett C , Giger M 
Published: April 2010

Purpose: To investigate whether a computerized quantitative image analysis (QIA) system for diagnostic breast ultrasound can predict whether a breast cancer lesion has metastasized in patients with sonographically normal appearing axillary lymph nodes. Method and Materials: This study used a database of sonographic images of breast cancers in patients who presented with a single cancerous lesion and axillary lymph nodes that appeared normal on diagnostic breast ultrasound. The dataset contained images of 50 patients. In 20 patients, the breast cancer had metastasized and in 30 patients the cancer remained localized at the time of surgery. The truth regarding disease grade/pathology was obtained from the final breast surgery and lymphadectomy. Features including both lesion and parenchymal characteristics were calculated for each lesion. A leave‐one‐case‐out analysis was performed and each iteration stepwise feature selection was combined with the training of an LDA classifier for the task of distinguishing between cancerous lesions that metastasized and ones that remained localized. Results: In the leave‐one‐case‐out analysis, the most frequently selected combination consisted of 3 features. In this set, a single feature pertained to the lesion itself and the other 2 features described a difference between the lesion and the surrounding parenchyma. The features were the lesion echogenicity, the difference in echogenicity, and the difference in entropy. The LDA classifier yielded an area under the ROC curve (AUC value) of 0.80 (standard error 0.06) for the task of distinguishing between metastasized breast cancer index lesions and localized breast cancers. Conclusion: The QIA scheme obtained promising performance in this preliminary study and shows potential to diagnose metastatic disease. It is important to note that this study was performed using images only of patients for whom pre‐surgery imaging studies suggested that the breast cancer had remained localized.Conflict of Interest: Grants NIH and DOE. Stockholder, grant, royalties Hologic.
WE‐E‐304A‐01: Contrast Mechanism and Origin of Artifacts in Phase‐Sensitive X‐Ray Volumetric Imaging
Authors: Wu X , Yan A , Liu H 
Published: April 2010

Purpose: Phase‐sensitive x‐ray volumetric imaging has great potential for tissue contrast enhancement and radiation dose reduction, compared to conventional tissue‐attenuation based x‐ray volumetric imaging. In order to achieve quantitative and artifact‐free phase sensitive volumetric imaging, one should quantify the formed phase contrast and find the origin of artifacts formed in phase‐sensitive volumetric imaging. Method and Materials: We incorporated x‐ray Fresnel diffraction into x‐ray projection acquisitions, and reconstructed volumetric images from these phase‐sensitive projections by the filtered backprojection method. A formula for the reconstructed tissue's apparent attenuation coefficients was derived for quantitative analysis. Computer simulations of phase sensitive 3D tomography were performed, and reconstructed 3D phantom tomograms were analyzed, and the artifacts associated with phase‐contrast were identified. In order to eliminate these artifacts, we performed phase retrievals based on the phase‐attenuation duality method for each projection, and these phase‐retrieved projections were then used for new 3D tomographic reconstructions. Results: The apparent linear attenuation coefficients in 3D tomograms were found each being the sum of three parts: the tissue linear attenuation coefficient, the scaled 3D Laplacian of tissue refraction index, and the artifact depending on global distributions of tissue attenuation coefficients and refraction indices. The simulated high‐resolution 3D images of phantoms exhibit severe artifacts as the upward and downward density over‐shootings and the streaks. The phase retrieval was shown very effective for removing these artifacts and accurately reconstructing tissue attenuation coefficients in phase‐sensitive 3D tomography. Conclusion: We clarified the contrast mechanism in phase‐sensitive volumetric imaging, found the quantitative contribution of the phase contrast in reconstructed 3D images, and revealed the origin of severe artifacts. The need of the phase‐retrievals for quantitative and artifact‐free phase sensitive volumetric imaging was clearly demonstrated.Research supported in part by DoD Breast Cancer Research Program.
WE‐E‐210A‐01: Functional Cardiovascular MRI: Assessment, Visualization and Quantification of 3D Blood Flow Characteristics
Authors: Markl M 
Published: April 2010

Magnetic Resonance Imaging (MRI) techniques provide a non‐invasive method for the highly accurate anatomic depiction of the heart and vessels. Most MR‐sequences demonstrate more or less significant sensitivity to flow and motion, which can lead to artifacts in many applications. The intrinsic motion sensitivity of MRI can, however, also be used to image vessels like in phase contrast (PC) MR‐angiography or to quantify blood flow and motion of tissue. Such techniques offer the unique possibility to acquire spatially registered functional information simultaneously with the morphological data within a single experiment. Characterizations of the dynamic components of blood flow and cardiovascular function provide insight into normal and pathological physiology and have made considerable progress in recent yearsTo synchronize flow or motion sensitive measurements with periodic tissue motion or pulsatile flow, data acquisition is typically gated to the cardiac cycle and time resolved (CINE) anatomical images are collected to depict the dynamics of tissue motion and blood flow during the cardiac cycle. Visualization and quantification of blood flow and tissue motion using PC MRI has been widely used in a number of applications. In addition to analyzing tissue motion such as left ventricular function, time‐resolved 2D PC MRI techniques have proven to be useful tools for the assessment of blood flow within the cardiovascular system.Moreover, 3D spatial encoding offers the possibility of isotropic high spatial resolution and thus the ability to measure and visualize the temporal evolution of complex flow and motion patterns in a 3D‐volume. In this context, ECG synchronized and respiration controlled flow sensitive 3D MRI using 3‐directional velocity encoding (also termed ‘flow sensitive 4D MRI’) can be employed to detect and visualize global and local blood flow characteristics in targeted vascular regions (aorta, cranial arteries, carotid arteries, etc.). For the analysis and visualization of complex, three‐directional blood flow within a 3D volume, various visualization tools, including 2D vector‐fields, 3D streamlines and particle traces, have been reported. In addition more advanced data quantification strategies of directly measured (e.g. flow rates) or derived parameters (e.g. pressure difference maps, wall sheer stress, pulse wave velocity, etc.) are promising as new clinical markers for the characterization of cardiovascular disease.This lecture will provide an overview of the MR imaging principles and advanced acquisition methods, data processing, flow visualization and quantification strategies, and clinical applications of flow sensitive MRI imaging.Learning Objectives:1. Understand the basic and advanced methods for flow measurements using MRI2. Understand techniques for 3D flow visualization and quantification of blood flow and derived parameters3. Understand the issues related to clinical applications of flow‐sensitive MRI
WE‐E‐213A‐01: Practical Aspects (and Pitfalls) of External Beam Photon Calibrations Using TG‐51
Authors: Curran B 
Published: April 2010

Purpose: Since its publication in September, 1999 (Medical Physics 26(9), pp 1847–90), the TG‐51 calibration protocol has been implemented in most US institutions. This presentation, one part of a workshop session on practical aspects of treatment unit calibrations, will review the basics of TG‐51 calibrations for conventional linear accelerator beams. Methods and Materials: Using the TG‐51 publication as a guide, practical aspects of performing a TG‐51 calibration of a treatment unit will be described. Relevant definitions of the key parameters to be acquired, either through measurement or calculation, will be reviewed. Starting from the acquisition of valid absorbed‐dose to water calibration factor, methods for determining each of the parameters used in the TG‐51 calculation will be reviewed. Common techniques will be illustrated and some common mistakes will be discussed. Publicly available resources will be described. Methods for verifying the results of calibrations, particularly when spreadsheets or other calculations aids are utilized, will also be discussed.Learning Objectives:1. Review the basic implementation of TG‐51 for conventional external beam measurements.2. Discuss practical aspects of the selection of ion chamber, electrometer and measurement conditions for TG‐51 calibrations.3. Demonstrate some techniques for determining TG‐51 parameters such as Pion, Ppol, and kQ4. Discuss some common mistakes and how to avoid them.
WE‐E‐213A‐02: Practical Aspects of Cyberknife Small Field Dosimetry
Authors: Dieterich S , Wang L 
Published: April 2010

Small field dosimetry for Cyberknife is the most challenging aspects of machine commissioning and annual QA. Accurate reference dosimetry, relative output factors, TPR and OCR data form the baseline for accurate dose delivery. This presentation will give practical guidance on ways to achieve an accurate dosimetric measurement in a time‐efficient way.First, a new reference dosimetry concept based on a recent publication by the IAEA Working group on “Small Field Dosimetry” will be introduced. This new concept explains how to approach reference dosimetry for radiation therapy machines which do not have a 10 cm × 10 cm field as required by TG51 or IAEA Report 398. Using an example, it will be explained how to calculate kQ for the 60 mm collimator. Suitable detectors for the reference dosimetry with the 60 mm collimator will be discussed. This will include a review of the measured dose dependency on chamber length for the non‐flattened field of Cyberknife based on recently published literature. A discussion on reference dosimetry for the IRIS collimator will conclude reference dosimetry.Second, relative output factor measurements, focusing on collimators ⩽ 10 mm, will be introduced. The importance of checking the detector for correct inverse‐square law will be shown with a clinical example. Suitable detectors for output measurements are different from detectors used for reference dosimetry. Special consideration will be given to IRIS collimator output factors and their dependency on the accuracy of the mechanical field size tolerance.This presentation will end with a section on measurements of the TPR and OCR data. Especially measuring OCR data for the IRIS collimator, which require 15 degree and 105 degree scans in addition to the in‐plane and cross‐plane scans, will be discussed. Data processing as well as tips & tricks to streamline the process will be given. Quality assurance and secondary dose verification processes as applicable to the dosimetry will be covered.Learning Objectives:1. Understanding the new IAEA “Small Field Dosimetry” Paradigm on reference field dosimetry.2. Understanding which detector types are suitable for Cyberknife small field dosimetry, and why.3. Understanding Cyberknife‐specific challenges.