Sb Park

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.

SU‐FF‐T‐605: Developing a Universal Treatment Plan Review System

Purpose: To review and make a composite plan from treatment plans generated by various treatment planning systems, we developed a universal review system. Our system will read DICOM CTimage sets, and contours and dose distribution files in DICOM‐RT format. Methods and Materials: Many clinical departments have various treatment planning systems; Tomotherapy (Tomotherapy), Pinnacle (Philips Medical), XiO (CMS), Cyberknife (Cyberknife), and Eclipse (Varian). It was impossible to combine two plans generated by two different planning systems. Our system was developed in Linux operating system, and written in C++ program language. We utilized Vega DICOM‐RT library, ITK/VTK, and DCM4CHEE DICOM server. We imported a planning image set, contours, and dose distribution files in DICOM and DICOM‐RT formats from each planning system. Our system reads them in, displays the dose distribution, and generates dose volume histogram (DVH). It has capability to export DVH in tabulated form for the further analysis. We can add two plans generated with a single image set or two different image sets. We implemented image registration techniques to align the two different image sets; mutual information, and spatially weighted mutual information. If a patient was treated some time ago, then time‐dose factor (TDF) can be added when two treatment plans are added. Results and Discussion: We were able to import various plans from different planning systems and make a composite plan. Our system was very useful to determine a dose and review the dose distribution for recurrent cancer patients. Some patients treated else where, and we were able to read from their electronic data sent by the other facility. Our image registration techniques implemented were very useful and easy to use. (This work is partly supported by Susan G. Komen Breast Foundation Grant: BTCR126506)

SU‐HH‐AUD C‐08: Spatial Weighted Mutual Information for Image Registration in Image Guided Radiation Therapy

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).

SU‐FF‐J‐32: Comparison of Manual Plus MI Registration to SWMI Registration Using MVCT for Head‐And‐Neck Cancer Patients

Purpose: To measure the value of manual adjustment after MI (Mutual Information) image registration technique for head‐and‐neck cancer cases, we compared the result to those performed with Spatially Weighted Mutual Information (SWMI) measure. Methods and Materials: We chose 6 head‐and‐neck cancer patients treated with Helical Tomotherapy machine and retrieved 199 daily MVCT image sets. Each patient had three CTVs and total superior‐inferior extent of CTVs was greater than 15cm. The daily images were manually aligned after automated registration with the MI software provided by Tomotherapy. Then, it was compared to the registration result of in‐house image registrationsoftware using SWMI with a Gaussian‐shaped weight. Gaussian‐shaped weight function was set to be centered in the primary CTV. And the variance of the Gaussian for each direction was set to cover CTVs and the critical organs. Each patient had the same variance of the Gaussian setup through a whole treatment course. Our in‐house image registrationsoftware was implemented to receive DICOM and DICOM‐RT from various planning systems. Since the Tomotherapy machine without a robotic couch cannot deliver yaw and pitch corrections, only roll was considered among the rotational transformation. Results and Discussion: Manual registration is not accurate enough if there is organ deformation. Fifty nine treatments among 199 treatments for 6 head‐and‐neck cancer patients showed the difference from our automated SWMI registration by more than 3mm. Our PTVs were created from CTVs by adding a 3mm margin. The minimal dose of CTV was reduced by 52% in maximum. SWMI incorporated with the Gaussian‐shaped weight shows better target coverage for all 199 cases. The main advantage of the automated SWMI is not relying on the users expertise of visual image matching. (This work is partly supported by Susan G. Komen Breast Foundation Grant: BTCR126506)

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.

SU‐E‐J‐113: A New Quantification Measure of the Difference Between Two Organ Contours

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.

SU‐E‐J‐71: Uncertainty Representation in a Composite Radiation Dose Using Fuzzy Logic

Purpose: To present dose uncertainty in composite radiation plans generated through Deformable Image Registration (DIR). We introduce a Fuzzy Composite Dose (FCD) with fuzzified deformation vectors. We compared the Fuzzy method to the statistical approach. Methods: The accuracy of the DIR is generally verified through visual checks, including confirmation of matching corresponding anatomies. However, most of the soft tissues have few significant image features and therefore greater uncertainty in registration. We fuzzified the deformation vectors and derived a FCD. We utilized a Gaussian function for uncertainty allocation. A Gaussian variance (two standard deviation; 2sigma) between 2mm to 10mm was assigned, which is based on previous publications analyzing the difference between various DIR methods. The least uncertainty (2sigma=2mm) was assigned to the areas having significant image features. The most uncertainty (2sigma=10mm) was assigned to the areas with few features or a lot of noise. A Fuzzy union operator was utilized to derive the FCD from the Fuzzy deformation vector. The Alpha‐cut method was employed to present the uncertainty range of FCD. A patient example is presented. After radiation treatment to the right lung, they returned to the hospital later to have another radiation treatment to the right rib cage near the liver. B‐Spline DIR was utilized to create the composite dose. Results: In the liver, the statistical analysis shows the uncertainty up to 2sigma=17Gy. With the fuzzy approach, the alpha‐cut showed the uncertainty range up to 35Gy. The dose uncertainty range using the alpha‐cut is shown to be asymmetric, a representation not possible using the standard statistical approach. Conclusions: FCD showed the better presentation including the range and direction of uncertainty in the composite dose. This will enable the clinician to deliver effective dose to a recurrent tumor or new tumor near the previously treated site while minimizing complications.

SU‐GG‐J‐51: A New Image Guided Radiation Therapy Scheme Using Spatially Weighted Mutual Information Image Registration and a Semi‐Automatic PET Segmentation Tool

Purpose:_To enhance the automatic image registration process for Image Guided Radiation Therapy(IGRT) using biological information acquired by PETimaging.Methods and Materials: We developed an image registration tool which is incorporated with a three‐dimensional semi‐automatic segmentation tool for Positron Emission Tomography(PET)image set. Firstly we registered PETimage set to the planning CTimage set. Contours for the high uptake volumes were generated by a semi‐automatic segmentation tool. User can choose an intensity profile with a pointing device in Graphical User Interface. Then, region growing algorithm was utilized. Threshold for segmentation can be adjusted in GUI. We exported the segmented volumes and fused into a DICOM‐RT structure set generated in a planning CT. We formulated Spatially Weighted Mutual Information (SWMI) image registration with Structure‐Of‐Interest (SOI) based weight function (SWMI‐SOI) because assigning various importance weight values to geometric locations is not possible with mutual informationimage registration. SWMI method allows the user can assign the importance weights through the geometric space. A user can choose how much importance weight will be assigned to each SOI. We expanded the organ contours so that each SOI included enough image features such as organ edges. We assigned the higher importance value to Clinical Target Volumes (CTV) and the Organs‐At‐Risks (OAR). The highest importance value was assigned to the volumes which were delineated using PET segmentation tool. Lower weight values were assigned to other structures so that they would not dominate the registration. This insures that PET defined volumes will get a full treatment dose in every fraction. Results and Discussion: We successfully used biological information in IGRT process and SWMI‐SOI registration algorithm. Our method showed consistency between users and was very useful when multiple lesions were considered during image registration process. Further clinical research is necessary to determine optimal weight assignment.

SU‐GG‐J‐146: Piecewise Rigid‐Body Image Registration for Adaptive Radiation Therapy

Purpose: To accelerate image deformation and registration for Adaptive Radiation Therapy(ART), we propose a piecewise rigid‐body image registration (PRIR). This algorithm is faster than full deformable registration by up to a factor of ten without sacrificing accuracy. Method and Materials: Deformable Image Registration (DIR) may not reflect the biomechanicalcharacteristics of the anatomy. In short, DIR may try to deform the body in ways that are not physically possible. Fluid dynamic models were adopted to address this problem. However, DIR consumes too much time to be utilized for routine clinical ART. PRIR considers the biomechanicalcharacteristics of the anatomy while retaining the speed of rigid‐body transformations. First, we import contours from a Radiation Therapy Plan (RTP) through DICOM‐RT. We assume that the contour delineates an organ (or tumor), which is modeled as an independent rigid‐body. Two image sets (planning CT and Conebeam CT) were aligned with our rigid‐body registration using Spatial Weighted Mutual Information (SWMI). Then, rigid‐body registration was performed with each organ independently from the rest of image using Mutual Information or SWMI. Users can select a specific type of registration: full translation and rotation, 3‐D scaling, or 2‐D scaling by considering biomechanicalcharacteristics. One prostate case (9 image sets) and one head‐and‐neck case (7 image sets) were tested. Results and Discussion: Our image registration for 6 contoured organs took 95seconds on average. We succeeded in transforming deformable contours from RTPimages to daily cone beam CTimages. Our method can be utilized to re‐optimize plans for Adaptive Radiation Therapy. The speed can be further improved with parallel processing. Accelerating deformable registrations is critical for implementing real‐time ART. (This work is partly supported by Susan G. Komen Breast Foundation Grant: BTCR126506)

SU‐FF‐T‐139: Designing a Patient Identification System Using Bioinformatics — A Fingerprint Approach

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 patients 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.