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.

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.

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.