J Monroe

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.

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.

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).

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‐FF‐T‐314: Initial Monte Carlo Analysis of the Dosimetric Effects of Gold Nanoparticle Radiosensitizers

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 ratios 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.

SU‐F‐T‐599: Volume‐Independent Conformity Index for Stereotactic Brain Tumors

PURPOSE To develop a volume-independent metric called the Gaussian Weighted Conformity Index (GWCI) for assessing conformality of stereotactic radiosurgery plans for small brain tumors. METHODS The GWCI calculates bi-directional distance by searching for corresponding points between the prescription isodose line and tumor contour, assigning different scoring weights to tumor coverage with a score of 1.0 being ideal assuming an idealized Gaussian distribution of dose around the tumor. (Figure 1, left) The GWCI penalizes tumor under-dosing three times more heavily than the prescription isodose falling outside the tumor. (Figure 1, middle) A user interface was created to calculate GWCI from images and RT structures (Figure 1, right). Patients receiving radiosurgery were randomly selected and images and RT structures were exported to MiM (MiMVista, Cleveland, OH) to calculate traditional conformality indices (CI). CIs were calculated for 39 tumors from patients receiving Gamma Knife radiosurgery (GKSRS) and from 10 tumors from patients receiving linac-based stereotactic radiosurgery (L-SRS). GWCIs were calculated for 14 tumors from patients receiving GKSRS and for 10 tumors from patients receiving L-SRS. RESULTS Conformality indices calculated from 39 GKSRS plans and 10 L-SRS plans are plotted in Figure 2 demonstrating that as tumour volume gets smaller, conformality index increases. GWCIs for 14 tumors were plotted against CIs and linear regression was performed (Figure 3) yielding GWCI = -.077*CI + 1.044 (R2 = .52). Utilizing this regression, the corresponding GWCI to a traditionally-acceptable CI of 1.5 was calculated as 0.927. CONCLUSION Limitations of current conformity metrics become apparent when applied to radiosurgery treatment plans. A GWCI tool was successfully developed which can be used to accurately score the quality of an individual treatment plan while eliminating small volume effects. A GWCI of 0.93 may be used as a volume-independent cutoff for plan conformality.

SU‐E‐J‐105: Applying Fuzzy Similarity Index and Ground Truth Fuzzy Contour to Evaluate the Segmentation Accuracy

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.

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.