A volume-independent conformity index for stereotactic radiosurgery.

PURPOSE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Clinical Study of Using Biometrics to Identify Patient and Procedure.

PURPOSE: To 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. METHODS: The 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. RESULTS: 1,271 (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. CONCLUSION: We 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.

NRG Oncology medical physicists' manpower survey quantifying support demands for multi-institutional clinical trials.

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.

Quantitative evaluation of image segmentation incorporating medical consideration functions.

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.

Quantitative Analysis Tools and Digital Phantoms for Deformable Image Registration Quality Assurance.

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.

Bidirectional local distance measure for comparing segmentations.

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.

Composite Radiation Dose Representation Using Fuzzy Set Theory.

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.

Spatially weighted mutual information image registration for image guided radiation therapy.

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 MRI image 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 CT images. 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.

Monte Carlo-aided dosimetry of the theragenics TheraSeed model 200 103Pd interstitial brachytherapy seed.

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.