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To develop a volume-independent conformity metric called the Gaussian Weighted Conformity Index (GWCI) to evaluate stereotactic radiosurgery/radiotherapy (SRS/SRT) plans for small brain tumors.A signed bi-directional local distance (BLD) between the prescription isodose line and the target contour is determined for each point along the tumor contour (positive distance represents under-coverage). A similarity score function (SF) is derived from Gaussian function, penalizing under- and over-coverage at each point by assigning standard deviations of the Gaussian function. Each point along the dose line contour is scored with this SF. The average of the similarity scores determines the GWCI. A total of 40 targets from 18 patients who received Gamma-Knife SRS/SRT treatments were analyzed to determine appropriate penalty criteria. The resulting GWCIs for test cases already deemed clinically acceptable are presented and compared to the same cases scored with the New Conformity Index to determine the influence of tumor volumes on the two conformity indices (CIs).A total of four penalty combinations were tested based on the signed BLDs from the 40 targets. A GWCI of 0.9 is proposed as a cutoff for plan acceptability. The GWCI exhibits no target volume dependency as designed.A limitation of current CIs, volume dependency, becomes apparent when applied to SRS/SRT plans. The GWCI appears to be a more robust index, which penalizes over- and under-coverage of tumors and is not skewed by the tumor volume.

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.

PurposeTo reduce patient and procedure identification errors by human interactions in radiotherapy delivery and surgery, a Biometric Automated Patient and Procedure Identification System (BAPPIS) was developed. BAPPIS is a patient identification and treatment procedure verification system using fingerprints.MethodsThe system was developed using C++, the Microsoft Foundation Class Library, the Oracle database system, and a fingerprint scanner. To register a patient, the BAPPIS system requires three steps: capturing a photograph using a web camera for photo identification, taking at least two fingerprints, and recording other specific patient information including name, date of birth, allergies, etc. To identify a patient, the BAPPIS reads a fingerprint, identifies the patient, verifies with a second fingerprint to confirm when multiple patients have same fingerprint features, and connects to the patient’s record in electronic medical record (EMR) systems. To validate the system, 143 and 21 patients ranging from 36 to 98 years of ages were recruited from radiotherapy and breast surgery, respectively. The registration process for surgery patients includes an additional module, which has a 3D patient model. A surgeon could mark ‘O’ on the model and save a snap shot of patient in the preparation room. In the surgery room, a webcam displayed the patient’s real-time image next to the 3D model. This may prevent a possible surgical mistake.Results1,271 (96.9%) of 1,311 fingerprints were verified by BAPPIS using patients’ 2nd fingerprints from 143 patients as the system designed. A false positive recognition was not reported. The 96.9% completion ratio is because the operator did not verify with another fingerprint after identifying the first fingerprint. The reason may be due to lack of training at the beginning of the study.ConclusionWe successfully demonstrated the use of BAPPIS to correctly identify and recall patient’s record in EMR. BAPPIS may significantly reduce errors by limiting the number of non-automated steps.

PurposeTo assess stereotactic radiotherapy (SRT)/stereotactic body radiotherapy (SBRT) practices by polling clinics participating in multi-institutional clinical trials.MethodsThe NRG Oncology Medical Physics Subcommittee distributed a survey consisting of 23 questions, which covered general technologies, policies, and procedures used in the Radiation Oncology field for the delivery of SRT/SBRT (9 questions), and site-specific questions for brain SRT, lung SBRT, and prostate SBRT (14 questions). Surveys were distributed to 1,996 radiotherapy institutions included on the membership rosters of the five National Clinical Trials Network (NCTN) groups. Patient setup, motion management, target localization, prescriptions, and treatment delivery technique data were reported back by 568 institutions (28%).Results97.5% of respondents treat lung SBRT patients, 77.0% perform brain SRT, and 29.1% deliver prostate SBRT. 48.8% of clinics require a physicist present for every fraction of SBRT, 18.5% require a physicist present for the initial SBRT fraction only, and 14.9% require a physicist present for the entire first fraction, including set-up approval for all subsequent fractions. 55.3% require physician approval for all fractions, and 86.7% do not reposition without x-ray imaging. For brain SRT, most institutions (83.9%) use a planning target volume (PTV) margin of 2 mm or less. Lung SBRT PTV margins of 3 mm or more are used in 80.6% of clinics. Volumetric modulated arc therapy (VMAT) is the dominant delivery method in 62.8% of SRT treatments, 70.9% of lung SBRT, and 68.3% of prostate SBRT.ConclusionThis report characterizes SRT/SBRT practices in radiotherapy clinics participating in clinical trials. Data made available here allows the radiotherapy community to compare their practice with that of other clinics, determine what is achievable, and assess areas for improvement.

Purpose A survey was created by NRG to assess a medical physicists' percent full time equivalent (FTE) contribution to multi-institutional clinical trials. A 2012 American Society for Radiation Oncology report, "Safety Is No Accident," quantified medical physics staffing contributions in FTE factors for clinical departments. No quantification of FTE effort associated with clinical trials was included. Methods To address this lack of information, the NRG Medical Physics Subcommittee decided to obtain manpower data from the medical physics community to quantify the amount of time medical physicists spent supporting clinical trials. A survey, consisting of 16 questions, was designed to obtain information regarding physicists' time spent supporting clinical trials. The survey was distributed to medical physicists at 1996 radiation therapy institutions included on the membership rosters of the 5 National Clinical Trials Network clinical trial groups. Results Of the 451 institutions who responded, 50% (226) reported currently participating in radiation therapy trials. On average, the designated physicist at each institution spent 2.4 hours (standard deviation [SD], 5.5) per week supervising or interacting with clinical trial staff. On average, 1.2 hours (SD, 3.1), 1.8 hours (SD, 3.9), and 0.6 hours (SD, 1.1) per week were spent on trial patient simulations, treatment plan reviews, and maintaining a Digital Imaging and Communications in Medicine server, respectively. For all trial credentialing activities, physicists spent an average of 32 hours (SD, 57.2) yearly. Reading protocols and supporting dosimetrists, clinicians, and therapists took an average of 2.1 hours (SD, 3.4) per week. Physicists also attended clinical trial meetings, on average, 1.2 hours (SD, 1.9) per month. Conclusion On average, physicist spent a nontrivial total of 9 hours per week (0.21 FTE) supporting an average of 10 active clinical trials. This time commitment indicates the complexity of radiation therapy clinical trials and should be taken into account when staffing radiation therapy institutions.

Composite plans created from different image sets are generated through Deformable Image Registration (DIR) and present a challenge in accurately presenting uncertainties, which vary with anatomy. Our effort focuses on the application of Fuzzy Set theory to provide an accurate dose representation of such a composite treatment plan. The accuracy of the DIR is generally verified through geometrical visual checks, including the confirmation of the corresponding anatomies with edge features, such as bone or organ boundaries. However, the remaining volume of the image (mostly soft tissues) has few significant image features and therefore greater uncertainty. We fuzzified the deformation vector and derived a fuzzy composite dose. The fuzzification was implemented using Gaussian functions based on the varying uncertainties in the DIR. After establishing the theoretical basis for this new approach, we present two-and three-dimensional examples as proof-of-concept. Using Fuzzy Set theory, composite dose plans displaying locality-based uncertainties were successfully created, providing information previously unavailable to clinicians. Previous to Fuzzy Set dose presentations, clinicians had no measure of confidence in the accuracy of a composite dose plan. Using fuzzified composite dose presentations, clinicians can determine a safe additional dose to previously treated anatomy. This will possibly increase the treatment success rate and reduce the rate of complications.

Purpose: A quantitative and objective metric, the medical similarity index (MSI), has been developed for evaluating the accuracy of a medical image segmentation relative to a reference segmentation. The MSI uses the medical consideration function (MCF) as its basis. Methods: Currently, no indices provide quantitative evaluations of segmentation accuracy with medical considerations. Variations in segmentation can occur due to individual skill levels and medical relevance—curable or palliative intent, boundary uncertainty due to volume averaging, contrast levels, spatial resolution, and unresolved motion all affect the accuracy of a patient segmentation. Current accuracy measuring indices are not medically relevant. For example, undercontouring the tumor volume is not differentiated from overcontouring tumor. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are two similarity measures often used. However, these metrics consider only geometric difference without considering medical implications. Two segments (under- vs overcontouring tumor) with similar DSC and HD measures could produce significantly different medical treatment results. The authors are proposing a MSI involving a user-defined MCF derived from an asymmetric Gaussian function. The shape of the MCF can be determined by a user, reflecting the anatomical location and characteristics of a particular tissue, organ, or tumor type. The peak of MCF is set along the reference contour; the inner and outer slopes are selected by the user. The discrepancy between the test and reference contours is calculated at each pixel by using a bidirectional local distance measure. The MCF value corresponding to that distance is summed and averaged to produce the MSI. Synthetic segmentations and clinical data from a 15 multi-institutional trial for a head-and-neck case are scored and compared by using MSI, DSC, and Hausdorff distance. Results: The MSI was shown to reflect medical considerations through the choice of MCF penalties for under- and overcontouring. Existing similarity scores were either insensitive to medical realities or simply inaccurate. Conclusions: The medical similarity index, a segmentation evaluation metric based on medical considerations, has been proposed, developed, and tested to incorporate clinically relevant considerations beyond geometric parameters alone.

Purpose: To develop a measuring method between two contours, which can be used for validating a PTV during IGRT,organ motion and/or deformation studies. Methods: Quantifying the geometric difference between two organ/target surfaces is essential for Radiation therapy planning and delivery. Point‐to‐surface distance measures have been utilized to evaluate and visualize the local surface differences. However, previously well‐known distance measures have critical shortfalls. Normal distance (ND) measure suffers when the reference surface is strongly curved. Minimum distance (MD) measure (a.k.a. Hausdorff distance) suffers when the test surface is strongly curved. Our new distance measure named Error‐Proof Distance (EPD) can deal with both difficult cases.EPD measure calculates the maximum value between the Forward Minimum Distance (FMinD) and the Backward Maximum Distance (BMaxD) at each point. The FMinD denotes the minimum distance to the test surface from a point on the reference surface. The BMaxD means the maximum value among the minimum distances from all points of the test surface to the point on the reference surface. We tested EPD using three 2‐D contour examples including a 20mm shifted contour, and two 3‐D clinical cases. Results: In case of 2‐D contour examples, ND and MD measure failed in strongly curved areas, but EPD measure outperformed the others. The maximum distance measured between a reference and a 20mm shifted test contour should be equal to 20mm, but ND erroneously measured 24mm. Furthermore, ND reported erroneous distances where the reference surface is strongly curved in 3‐D clinical cases. Conclusions: We succeeded to prove that a new EPD is arobust and accurate distance measure to compare two 2D or 3D surfaces. EPD measure can be used to evaluate and visualize the surface difference of organ contours. It is also helpful for proving PTV margin during IGRT, and organ motion and/or deformation studies. This project is partially supported by the Agency for Healthcare Research and Quality (AHRQ) grant 1R18HS017424‐01A2.

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R'. The data set, R', T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.

Purpose: To accurately quantify the local difference between two contour surfaces in two- or three-dimensional space, a new, robust point-to-surface distance measure is developed. Methods: To evaluate and visualize the local surface differences, point-to-surface distance measures have been utilized. However, previously well-known point-to-surface distance measures have critical shortfalls. Previous distance measures termed “normal distance (ND),” “radial distance,” or “minimum distance (MD)” can report erroneous results at certain points where the surfaces under comparison meet certain conditions. These skewed results are due to the monodirectional characteristics of these methods. ComGrad distance was also proposed to overcome asymmetric characteristics of previous point-to-surface distance measures, but their critical incapability of dealing with a fold or concave contours. In this regard, a new distance measure termed the bidirectional local distance (BLD) is proposed which minimizes errors of the previous methods by taking into account the bidirectional characteristics with the forward and backward directions. BLD measure works through three steps which calculate the maximum value between the forward minimum distance (FMinD) and the backward maximum distance (BMaxD) at each point. The first step calculates the FMinD as the minimum distance to the test surface from a point,p ref on the reference surface. The second step involves calculating the minimum distances at every point on the test surface to the reference surface. During the last step, the BMaxD is calculated as the maximum distance among the minimum distances found at p ref on the reference surface. Tests are performed on two- and three-dimensional artificial contour sets in comparison to MD and ND measure techniques. Three-dimensional tests performed on actual liver and head-and-neck cancer patients. Results: The proposed BLD measure provides local distances between segmentations, even in situations where ND, MD, or ComGrad measures fail. In particular, the standard deviation measure is not distorted at certain geometries where ND, MD, and ComGrad measures report skewed results. Conclusions: The proposed measure provides more reliable statistics on contour comparisons. From the statistics, specific local and global distances can be extracted. Bidirectional local distance is a reliable distance measure in comparing two- or three-dimensional organ segmentations.

Purpose: To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy(IGRT). Methods: It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically “important” areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT(CBCT)image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRIimage sets. Results: SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ’s deformation and neighboring critical organs’ deformation. SWMI-GW was also used to successfully fuse MRI and CTimages. Conclusions: Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

A quantitative and objective metric, the medical similarity index (MSI), has been developed for evaluating the accuracy of a medical image segmentation relative to a reference segmentation. The MSI uses the medical consideration function (MCF) as its basis.Currently, no indices provide quantitative evaluations of segmentation accuracy with medical considerations. Variations in segmentation can occur due to individual skill levels and medical relevance--curable or palliative intent, boundary uncertainty due to volume averaging, contrast levels, spatial resolution, and unresolved motion all affect the accuracy of a patient segmentation. Current accuracy measuring indices are not medically relevant. For example, undercontouring the tumor volume is not differentiated from overcontouring tumor. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are two similarity measures often used. However, these metrics consider only geometric difference without considering medical implications. Two segments (under- vs overcontouring tumor) with similar DSC and HD measures could produce significantly different medical treatment results. The authors are proposing a MSI involving a user-defined MCF derived from an asymmetric Gaussian function. The shape of the MCF can be determined by a user, reflecting the anatomical location and characteristics of a particular tissue, organ, or tumor type. The peak of MCF is set along the reference contour; the inner and outer slopes are selected by the user. The discrepancy between the test and reference contours is calculated at each pixel by using a bidirectional local distance measure. The MCF value corresponding to that distance is summed and averaged to produce the MSI. Synthetic segmentations and clinical data from a 15 multi-institutional trial for a head-and-neck case are scored and compared by using MSI, DSC, and Hausdorff distance.The MSI was shown to reflect medical considerations through the choice of MCF penalties for under- and overcontouring. Existing similarity scores were either insensitive to medical realities or simply inaccurate.The medical similarity index, a segmentation evaluation metric based on medical considerations, has been proposed, developed, and tested to incorporate clinically relevant considerations beyond geometric parameters alone.

Purpose: A survey was taken by NRG Oncology to assess Full Time Equivalent (FTE) contributions to multi institutional clinical trials by medical physicists.No current quantification of physicists’ efforts in FTE units associated with clinical trials is available. The complexity of multi-institutional trials increases with new technologies and techniques. Proper staffing may directly impact the quality of trial data and outcomes. The demands on physics time supporting clinical trials needs to be assessed. Methods: The NRG Oncology Medical Physicist Subcommittee created a sixteen question survey to obtain this FTE data. IROC Houston distributed the survey to their list of 1802 contact physicists. Results: After three weeks, 363 responded (20.1% response). 187 (51.5%) institutions reporting external beam participation were processed. There was a wide range in number of protocols active and supported at each institution. Of the 187 clinics, 134 (71.7%) participate in 0 to 10 trials, 28 (15%) in 11 to 20 trials, 10 (5.3%) in 21 to 30 trials, 9 (4.8%) had 40 to 75 trials. On average, physicist spent 2.7 hours (SD: 6.0) per week supervising or interacting with clinical trial staff. 1.25 hours (SD: 3.37), 1.83 hours (SD: 4.13), and 0.64 hours(SD: 1.13) per week were spent on patient simulation, reviewing treatment plans, and maintaining a DICOM server, respectively. For all protocol credentialing activities, physicist spent an average of 37.05 hours (SD: 96.94) yearly. To support dosimetrists, clinicians, and therapists, physicist spend on average 2.07 hours (SD: 3.52) per week just reading protocols. Physicist attended clinical trial meetings for on average 1.13 hours (SD: 1.85) per month. Conclusion: Responding physicists spend a nontrivial amount of time: 8.8 hours per week (0.22 FTE) supporting, on average, 9 active multi-institutional clinical trials.

This paper compares experimentally measured and calculated dose-rate distributions for a novel 125 I liquid-filled brachytherapy balloon applicator (the GliaSite RTS), designed for the treatment of malignant brain-tumor resection-cavity margins. This work is intended to comply with the American Association of Physicists in Medicine (AAPM) Radiation Therapy Committee’s recommendations [Med. Phys. 25, 2269–2270 (1998)] for dosimetric characterization of low-energy photon interstitial brachytherapy sources. Absolute low dose-rate radiochromic film (RCF) dosimetry measurements were performed in coronal planes about the applicator. The applicator was placed in a solid water phantom, machined to conform to the inflated applicator’s surface. The results were used to validate the accuracy of Monte Carlophoton transport (MCPT) simulations and a point-source dose-kernel algorithm in predicting dose to water. The absolute activity of the 125 I solution was determined by intercomparing a National Institute of Standards and Technology (NIST) 125 I standard with a known mass of radiotherapy solution (Iotrex™) in an identical vial and geometry. For the two films not in contact with applicator, the average agreement between RCF and MCPT (specified as the mean absolute deviation in successive 4 mm rings) was found to be within ±5% at distances 0.2–25 mm from the film centers. For the two films touching the catheter, the mean agreement was ±14.5% and 7.5% near the balloon surface but improving to 7.5% and 6% by 3.5 mm from the surface. These errors, as large as 20% in isolated pixels, are likely due to trim damage, 125 I contamination, and poor conformance with the balloon. At larger distances where the radiationdoses were very low, the observed discrepancies were significantly larger than expected. We hypothesize that they are due to a dose-rate dependence of the RCF response. A 1%–10% average difference between a simple one-dimensional path-length semiempiricaldose-kernel model and the MCPT calculations was observed over clinically relevant distances.

To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT).It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRI image sets.SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ's deformation and neighboring critical organs' deformation. SWMI-GW was also used to successfully fuse MRI and CT images.Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

A dosimetric study of a 103Pd seed for permanent interstitial brachytherapy, the Theragenics Corporation Model 200 (TheraSeed), has been undertaken utilizing Monte Carlo photon-transport (MCPT) simulations. All dosimetric quantities recommended by the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) [Med. Phys. 22, 209-234 (1995)] report have been calculated. This source contains graphite pellets coated with palladium metal, within which the radioactive 103Pd is distributed. Due to the significant influence of this metal coating thickness on the dose distribution, two coating thicknesses, 2.2 microm (light seed, representing currently available seeds) and 10.5 microm (heavy seed, representing reactor-produced seeds available before 1994), were analyzed. Quantities determined are the following: dose rate constant, radial dose function, anisotropy function, anisotropy factor, anisotropy constant, and "along and away" dose tables. The National Institute of Standards and Technology (NIST) Wide Angle Free Air Chamber (WAFAC) standard for air-kerma strength (SK,N99) was simulated, allowing a comparison to measured dosimetry data normalized to SKN99. The calculated dose-rate constants are 0.691 (light seed) and 0.694 cGy h(-1) U(-1) (heavy seed), where 1 U= 1 microGy x m2 x h(-1), in contrast to the recommended TG-43 value of 0.74 cGy h(-1) U(-1) and the value, 0.665 cGy h(-1) U(-1), recommended by AAPM report 69 [Med. Phys. 27, 634-642 (2000)]. Anisotropy constants (1/r2 weighted average, r > or = 1 cm) are 0.862 and 0.884 for the light seed and heavy seed, respectively. A generalization of the AAPM formalism [Med. Phys. 27, 634-642] for evaluating the time-dependent ratio of an administered-to-prescribed dose is presented. The findings of this study, in combination with 5% corrections applied to WAFAC measurements performed in 1999, imply that changes in the AAPM's recommended ratios as large as 6%, are indicated.

The plastic scintillator (PS) is a promising dosimeter for brachytherapy and other low-energy photon applications because of its high sensitivity and approximate tissue equivalence. As part of our project to develop a new PS material which maximizes sensitivity and radiological equivalence to water, we have measured the response, epsilon (light output/unit air kerma), of PS to low-energy bremsstrahlung (20 to 57 keV average energies) x-rays as well as photons emitted by 99mTc, 192Ir, and 137Cs sources, all of which were calibrated in terms of air kerma. The PS systems studied were a standard commercial PS, BC400 (Bicron Corporation, Newbury, OH), and our new sensitive and quench-resistant scintillator (polyvinyltoluene base and binary dye system) with and without 4% Cl loading intended to match the effective atomic number of water. For low-energy x-rays, epsilon was 20-57% relative to epsilon for 192Ir photons. Chlorine loading clearly reduced the energy dependence of epsilon, which ranged from 46% to 85% relative to 192Ir. However, even after using Monte Carlo photon-transport simulation to correct for the non-air equivalence of the PS, inherent dosimetric sensitivity still varied by 30% over the 20-400 keV energy range. Our work, one of the few measurements of PS response to low-energy photons, appears to confirm Birks' 1955 finding that ionization quenching reduces sensitivity to electrons below 125 keV. However, our results cannot be explained by Birks' widely used unimolecular quenching model.

Purpose:Monte Carlo simulations have been undertaken to quantify the dosimetry of tumors that have absorbed nanoparticlegold as radiosensitizers and irradiated with various radiotherapy modalities. Method and Materials: EGSnrcMP and BEAMnrcMP, well known and documented Monte CarloPhotonTransport simulation codes are used for therapy simulations. Previous measurements of uptake ratio's provide a basis for generating new radiation interaction cross sections for use in radiation transport simulations. Cross sections generated by the simulation software are checked against cross sections generated using the NIST (National Institute of Standards and Technology) Standard Reference Database 8 (XGAM) and found to agree to within about 0.5%. 250 kVp, and 6 MV and Ir‐192 HDR spectra provided with the EGS package where used as sources. Tumors containing 0.5% to 5% Au by weight (the balance made up by water) and sized from 0.5 to 3 cm were simulated at various depths and compared to pure water. The effects of normal tissue absorption of gold in media surrounding the tumor were also investigated. Results: As expected, 250 kVp orthovoltage units showed the largest benefit from nanoparticles. A 50% absorption increase in tumors absorbing 1.5% Au by weight. Ir‐192 therapy beams available from High Dose Rate Brachytherapy units treating 1.5% and 5% nanoparticlegold by weight showed a 13% and 38% increase in dose absorption rate for tumors 1cm from the source. 6MV photon beams treating a 3cm tumor at 10cm depth showed a modest 5% improvement. Surrounding media absorption of goldnanoparticles shifts both target and surrounding tissue absorption rates higher, but does not significantly change their relative absorption rates. Conclusion: These simulations suggest goldnanoparticles in some modalities are worth investigating as radiosensitizers. Initial research should focus on HDR modalities first.

Purpose: To evaluate the segmentation accuracy by using our novel Fuzzy Similarity Index (FSI) and Ground Truth Fuzzy Contour (GTFC) with the consideration of inter‐and intra‐observer variation Methods: We developed GTFC to build consensus truth segmentation(contour) and FSI to score segmentation for an objective and quantitative evaluation of in‐vivo medical image segmentation. GTFC is built by applying Fuzzy theory to consider with inter‐and intra‐observer variation. GTFC has the Fuzzy Membership Function(FMFn) which can assign a weight to each expert depending on their experience, unlike STAPLE. By using GTFC, we formulate a quantitative scoring index to evaluate the segmentation accuracy.When a test segmentation is evaluated, we calculate the membership value of FMFn at every point in the test segmentation(contour). Then, we can make a distribution of membership value as Membership Score Histogram(MSH). We enhanced FSI to make more responsive. While generating a single value index(FSI) from MSH, we adopt the strategy of penalizing lower membership values. The resultant FSI equation is formulated by combining MSH with a penalty constant. We tested the FSI by applying to a brain case. Ten experts segmented a region in the brain and six non‐experts independently delineated the same place. GTFC was created from segmentations of expert to evaluate the accuracy of segmentations of non‐experts. Then, we calculated FSI after making a MSH per test segmentation. Results: The order in higher similarity to GTFC is non‐expert 6, 1, 5, 2, 3, and 4. Non‐expert 2, 3, and 4 are significantly deviated from GTFC. Their FSIs are 0.845, 0.476, 0.125, 0.085, 0.078, and 0.005, respectively. Conclusions:FSI can sensitively reflect the accuracy of test segmentation. It can be used to develop unbiased educational tools or credential process for clinicians. It can be also used to evaluate the performance of automated segmentation tools.

Purpose: To introduce an indicator which can show the deformable image registration (DIR) accuracy, and be used for commissioning DIR systems. Methods: Inaccurate DIR can produce inaccurate composite dose when we add doses from multiple treatment plans. Deformation Error Histogram(DEH) and QA procedure were proposed to overcome the shortfall of landmark approach. Our method conducted a DVM comparison approach instead of using the significant visible feature points of the landmark approach. To show the feasibility of using DEH, we used a Lung and a Liver cancer patient. Each patient has two CT image sets (Reference and Test image sets). We generated a truth DVM by performing DIR using R and T sets, and by applying the DVM to T, R' is generated. To quantifies the systematic error of our in‐house DIR system, we calculated a test DVM by performing DIR using R' and T image sets. Then, we calculated a difference vector in millimeter between the test DVM and the truth DVM. Finally, a DEH was made from the difference vector for showing the cumulative distribution of error per Structure‐Of‐Interest (SOI). Results: Both clinical cases, we visualized errors by displaying the calculated difference vectors. Relatively larger systematic errors are observed where large deformation occurs, such as skin, mobile joints, shoulders, diaphragm, and inferior Lung region. The DEH can show the measured confidence range in DIR for the selected SOI. For Liver patient, [Average error, Standard deviation, 95% Confidence] for SOIs were GTV [6.69,2.07, 2.94], R Kidney [4.13,1.65,1.76], Spinal Cord [3.77,0.30,3.27], and Liver [3.03,1.67,0.78] in millimeter. For Lung patient, the Result were GTV [3.24,1.37,1.12], Heart [3.16,1.63,1.02], Lung [4.93,1.95,2.00], and Spinal Cord [4.39,1.67,2.39]. Conclusion: DEH provides quantitative systematic error during DIR. DEH and our data can be a tool for estimating magnitude of errors per anatomical sites and commissioning DIR systems.

Purpose: To validate the stability and accuracy of a new image registration assistance tool UTRS (Universal Treatment Plan Review System) [Case Western Reserve University, Cleveland, OH]) for CT and PET images. UTRS was developed to review and manage the DICOM and DICOM‐RT data sets, performing both manual and assisted image registration. Methods: We tested two groups of patients with existing PET/CT fusions performed by a clinical fusion expert. The first group consisted of 17 Head and Neck cancer (HN) cases and the second of 20 Lungcancer (LC) cases. In both groups, each patient's PET image set was aligned to the patient's CTimage set using the assisted registration method in UTRS. For the HN cases, Spatially Weighted Mutual Information with Gaussian Weighting (SWMI‐ GW) was selected as the metric. Mutual Information was used to register the LC cases. The existing MIM (MIM software Inc., Cleveland, OH) fusions are used for comparisons. Results: Visual checks confirmed the assisted registration of UTRS was comparable to the expert manual registration using MIM. Computational comparisons showed the Mean Absolute Differences (MADs) between the two registrations were less than 3.1mm in translation and 1.2 degree in rotation for HN cases. For LC cases, the MADs between MIM and UTRS were less than 2.9mm in translation and 1.5degree in rotation. Conclusions: Since a MIM's validation study showed the manual registration of MIM had a comparable variation; up to 2.93mm MADs from their average registration, we concluded that the assisted registration of UTRS is also valid for clinical use. We also found that rotational corrections were necessary for many cases between the image sets from the CT simulator and the diagnostic PET(/CT), even with the same immobilizer. The assisted registration dealt with rotations better since a 3‐ dimensional manual rotation on 2‐dimensional screen is obtuse.

Purpose: To develop a robust registration algorithm for medical images, emphasizing the registration of plan images with cone beam computed tomography(CBCT)images for IGRT. Our Spatial Weighted Mutual Information (SWMI) technique assigns greater importance to user‐selected volume, allowing medically ‘important’ areas to dominate the registration process. Method and Materials: Mutual Information (MI) is the most popular measure of image registration due to its robustness and multi‐modal capability. However, MI does have difficulty where organ deformation is present. We reformulated the MI algorithm by incorporating an adaptable weight function to user selected spatial locations. Since MI is defined in probabilistic space, we proposed a spatial‐weighted joint probability, and composed a Spatial Weighted Mutual Information measure. Our image registrationsoftware and Graphical User Interface (GUI) was programmed in C++ to import DICOM images and DICOM‐RT information, and to perform image registration. For this study, we used Gaussian‐shaped spatial weights applied to a user‐defined volume. Our software allows a user to adjust the Gaussian parameters via the GUI. Convergence and robustness of our registration method was first tested with a head‐and‐neck plan and seven CBCTimage sets. Then, a prostate plan with nine CBCTimage sets was analyzed. Speed of convergence was tested by arbitrarily miss‐aligning two image sets ±15mm over 41 trials. We also applied our algorithm to fuse CT and MRI image sets. Results and Discussion:Image registration using our measure converges 10% faster than using Mutual Information. Our study showed image registration using a uniform weight over an entire volume lead to compromised target coverage. SWMI showed better alignment near target areas and neighboring critical organs even with organ deformation. Our method worked well in fusing MRI to CTimages as well. (This work is partly supported by Susan G. Komen Breast Foundation Grant: BTCR126506).

Purpose: We developed a computerized patient identification system employing fingerprint scanners and pattern recognitionsoftware to reduce identification errors in radiation therapy. Our customized system was developed to interact with the IMPAC record and verification system. Methods and Materials: The patient identification system was programmed with C++ language and equipped with an optical fingerprint scanner SDK (Suprema Inc®) to accept a scan, recognize the patient, and open the correct patient record in the record and verify system. A new patient initially has two fingers scanned (about 1 second each) and is photographed with web camera. During actual use, the patient has one finger scanned, the print is identified, (about 1 sec / 1000 records to search), a second finger is scanned for a second verification, and then the patient photograph is displayed. The error probability is one out of 1 billion. Once a patient is identified, our system calls IMPAC and opens the patient's file. We recruited 10 volunteers to create 50 virtual patients using 5 pairs of fingerprints per volunteer. This yields a database with 100 fingerprints for accuracy testing. Since the relative size of fingers on a hand varies, pairs of corresponding digits (e.g. right and left index fingers) from each volunteer were used as a virtual patient. This provided a test of scanning viability between digits (e.g. thumb vs. pinkie). A total of 115 tests were performed, each pair of fingers were tested 23 times. Results: We had no false positive results for identifying patients out of 111 accepted scans. The smallest finger failed to scan four times and was judged inadequate for identification. Conclusion: Optically scanned fingerprints can be used to accurately identify patients and open their IMPAC patient file. This system has great promise to ensure proper patient identification with minimal costs and effort.

Purpose: A customized gating system was developed for a large bore CT scanner to produce gated images for use with an active breathing control (ABC) system. Operator triggering following a breath hold is replaced by an electronic gating signal from a bellows. A breathing phantom was constructed and imaged to insure accurate gating. Methods and Materials: A bellows strap wraps around the patient to monitor breathing during CT scans. A universal serial port (USB) cable provides power to the bellows sensor via the circuit box. Bellows signals route to the computer from the circuit box via another USB cable. Operators can set a threshold point in the breathing cycle that corresponds to an ABC breath‐hold to trigger the CT scanner. Softwaregenerated signals gate the CT through the circuit box. Unique to this system is an option to scan continuously while the patient is holding a breath, in contrast to commercial gated‐CT systems which take one image per breathing cycle. A breathing phantom was created using a computer programmed actuator, step‐motor, and off‐set ball. A sine wave modeled on human breathing was fed to the actuator. The moving range was 2.10 cm anterior‐posterior and 1.05 cm in lateral direction. The diameter and motion period was 6.4 cm and 5.54 sec. Phantom images taken were: stationary non‐gated, moving gated, and three un‐gated while moving. All image sets were analyzed with the Pinnacle planning system. Volumes generated from contours where used as a metric to determine imaging accuracy. Results: Target volumes of stationary and gated imaging were 146.8 and 145.5cm3, respectively (1% agreement). However, the discrepancies between stationary and all three un‐gated scans ranged from 4.84 to 5.45% with significant shape distortion. Conclusions: We successfully created a custom gating system for a large‐bore CT simulator.

We compared prostate position corrections using ultrasound and megavoltage CTimage guided systems following initial patient positioning with lasers. Internal prostate movements between the two patient cohorts were assumed to be equivalent given a sufficient number of measurements, so we hypothesize the image guided shifts should average out to the same magnitude. Therapists adjusted patient positions with physician supervision based on daily imaging prior to each treatment. A total of 17 prostate patients were studied. Ten patients (Group I) were scanned in 3‐D mode using an ultrasound system. Contours (prostate/bladder/rectum) from the treatment planning system were transposed on the image set to register the ultrasoundimage. Therapists shift the contours to match the ultrasoundimage set and then adjust the patient by these same shifts. The number of shifts recorded from Group I was 432. Group II patients (seven subjects yielding 315 recorded shifts) were treated using Tomotherapy. Patients were scanned over the prostate region and the megavoltage CTimage was registered with the treatment planningCT and contours. Shifts were produced for each axes and in roll. The patient table is adjusted to match the registration shifts. Roll corrections were minor and deemed insignificant for this study. The average prostate size from Group I was 104.9cc with a standard deviation of 42.1cc while Group II was 104.2cc and 54.8cc. A panel of four was formed to evaluate the quality of images to check possible operator bias due to image quality. Group I showed an average shift magnitude of 6.1mm with a standard deviation of 3.4mm. The average shift from Group II was 10.6mm with a standard deviation of 6.1mm. Tomotherapy imaging averaged 43% larger shifts compared to ultrasound. Possible explanations for this difference include operator laxity in initial tomotherapy positioning while depending on imaging and auto‐adjustment to compensate.

Purpose: The final goal of this research is to develop a patient repositioning system using a 3D optical laser scanner. We investigated the magnitude of human and system errors, which determines the accuracy of the patient repositioning system. We also scanned four breast patients to test the efficacy of this system.Method and Materials: A handheld 3D surface scanning system acquires patient surface images, which are registered to a reference image using a rigid body transformation. The resultant image registration generates the patient position adjustment to match the images. We evaluated human and system error associated with this positioning technique. To measure human error, ten operators performed ten experiments to acquire the same reference point. To evaluate the system error, we attached the receiver (serving as the origin of the surface image data) to the X‐Y‐Z positioning system and measured the discrepancy between the measured physical position and the scanner reported distance in three axes. We also scanned four breast patients and analyzed the repositioning errors. Results: The mean human error and standard deviation were evaluated to be 1.86mm and 0.66mm, respectively. The system mean error and standard deviation were found to be 0.4mm and 0.32mm (n=45), respectively. Image translation and rotational shifts from the patient study ranged from 5mm to 15mm and −6° to 11°, respectively. Conclusion: We have experimentally evaluated human and system error associated with a new 3D hand‐held laser scanner positioning system. As shown in the result, human error was much more significant than system error. This handheld 3D optical scanning system has proved its capability as a tool for patient repositioning between fractions. Future investigations will examine the relation between surface images and internal target localization.

A new detector for imaging annihilation radiation is designed as an auxiliary unit for single photoncomputed tomography(SPECT) systems. Increased interest in higher energymedical imaging creates a demand for a low cost device that can be used with existing SPECT units. Key design features are: a custom high-resolution collimator, a scintillator formed from plastic optical fibers, and a position-sensitive photomultiplier. The optical fiber core is a ternary scintillator: Polystyrene (solvent) with solutes PPO and BS–MSB. High-energy resolution (7.5 mm full-width at half max (FWHM) at a source-to-collimator distance of 10 cm), improved total efficiency (2.7×10 −5 at a SCD of 10 cm), and low cost are hallmarks of the new detector. The FWHM is more than twice that available imaging 511 keV gammas with conventional SPECT cameras. Images are formed using Compton electrons, which are limited to one interaction inside the detector as opposed to detection by photoelectric interactions in conventional cameras. Computer simulations with the radiation transport code EGS4 verified the basic imaging concept and were then used to optimize the detector and collimator designs. A working prototype detector was manufactured, tested, and characterized. Positron emitting Germanium-68 (Ge-68) was used as a laboratory point source of annihilation radiation for testing. A larger area detector (4×4 array of 16 identical modules, each 5 cm ×5 cm ×20 cm with an expected planar sensitivity of 4×10 −4 ) specifically for augmented breast cancer diagnosis was then proposed and simulated as a possible application of this technology.