Distance Discordance Research Articles

Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images

Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios. Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3DRef. A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated. Main results. Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and −0.12/2.68 Gy for and For the IQR was considerably smaller −0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs. Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.

Simultaneous multi-slice accelerated 4D-MRI for radiotherapy guidance

4D-MRI is becoming increasingly important for daily guidance of thoracic and abdominal radiotherapy. This study exploits the simultaneous multi-slice (SMS) technique to accelerate the acquisition of a balanced turbo field echo (bTFE) and a turbo spin echo (TSE) coronal 4D-MRI sequence performed on 1.5 T MRI scanners. SMS single-shot bTFE and TSE sequences were developed to acquire a stack of 52 coronal 2D images over 30 dynamics. Simultaneously excited slices were separated by half the field of view. Slices intersecting with the liver-lung interface were used as navigator slices. For each navigator slice location, an end-exhale dynamic was automatically identified, and used to derive the self-sorting signal by rigidly registering the remaining dynamics. Navigator slices were sorted into 10 amplitude bins, and the temporal relationship of simultaneously excited slices was used to generate sorted 4D-MRIs for 12 healthy volunteers. The self-sorting signal was validated using an in vivo peak-to-peak motion analysis. The smoothness of the liver-lung interface was quantified by comparing to sagittal cine images acquired directly after the SMS-4D-MRI sequence. To ensure compatibility with the MR-linac radiotherapy workflow, the 4D-MRIs were transformed into 3D mid-position (MidP) images using deformable image registration. Consistency of the deformable vector fields was quantified in terms of the distance discordance metric (DDM) in the body. The SMS-4D-TSE sequence was additionally acquired for 3 lung cancer patients to investigate tumor visibility. SMS-4D-MRI acquisition and processing took approximately 7 min. 4D-MRI reconstruction was possible for 26 out of 27 acquired datasets. Missing data in the sorted 4D-MRIs varied from 4%–26% for the volunteers and varied from 8%–24% for the patients. Peak-to-peak (SD) amplitudes analysis agreed within 1.8 (1.1) mm and 0.9 (0.4) mm between the sorted 4D-MRIs and the self-sorting signals of the volunteers and patients, respectively. Liver-lung interface smoothness was found to be in the range of 0.6–3.1 mm for volunteers. The percentage of DDM values smaller than 2 mm was in the range of 85%–89% and 86%–92% for the volunteers and patients, respectively. Lung tumors were clearly visibility in the SMS-4D-TSE images and MidP images. Two fast SMS-accelerated 4D-MRI sequences were developed resulting in T2/T1 or T2 weighted contrast. The SMS-4D-MRIs and derived 3D MidP-MRIs yielded anatomically plausible images and good tumor visibility. SMS-4D-MRI is therefore a strong candidate to be used for treatment simulation and daily guidance of thoracic and abdominal MR-guided radiotherapy.

A multivariable study of deformable image registration evaluation metrics in 4DCT of thoracic cancer patients

Deformable image registration (DIR) accuracy is often validated using manually identified landmarks or known deformations generated using digital or physical phantoms. In daily practice, the application of these approaches is limited since they are time-consuming or require additional equipment. An alternative is the use of metrics automatically derived from the registrations, but their interpretation is not straightforward. In this work we aim to determine the suitability of DIR-derived metrics to validate the accuracy of 4 commonly used DIR algorithms. First, we investigated the DIR accuracy using a landmark-based metric (target registration error (TRE)) and a digital phantom-based metric (known deformation recovery error (KDE)). 4DCT scans of 16 thoracic cancer patients along with corresponding pairwise anatomical landmarks (AL) locations were collected from two public databases. Digital phantoms with known deformations were generated by each DIR algorithm to test all other algorithms and compute KDE. TRE and KDE were evaluated at AL. KDE was additionally quantified in coordinates randomly sampled (RS) inside the lungs. Second, we investigated the associations of 5 DIR-derived metrics (distance discordance metric (DDM), inverse consistency error (ICE), transitivity (TE), spatial (SS) and temporal smoothness (TS)) with DIR accuracy through uni- and multivariable linear regression models. TRE values were found higher compared to KDE values and these varied depending on the phantom used. The algorithm with the best accuracy achieved average values of TRE = 1.1 mm and KDE ranging from 0.3 to 0.8 mm. DDM was the best predictor of DIR accuracy, with moderate correlations (R 2 < 0.61). Poor correlations were obtained at AL for algorithms with better accuracy, which improved when evaluated at RS. Only slight correlation improvement was obtained with a multivariable analysis (R 2 < 0.64). DDM can be a useful metric to identify inaccuracies for different DIR algorithms without employing landmarks or digital phantoms.

Evaluation and Comparison of Deformable Image Registration Algorithms for 4D CT Images

Deformable image registration (DIR) is crucial in adaptive radiation therapy. However, the validation is a challenging work due to the lack of gold-standard. This study proposed an evaluation framework by using point-to-point displacement vector field (DVF). Three DIR algorithms including original flow, active Demons and symmetric force Demons were validated for ten lung 4D CT images with landmarks. DVFs derived from DIR algorithms (dDVF) and manually measured according to landmarks (mDVF) were analyzed and compared. Their target registration errors (TRE) and the relationship of lung motion and three-dimensional TRE were explored. The distance discordance metric (DDM) values were calculated. For all cases, the active Demons algorithm had the smallest TRE value and DDM values and it showed the poor correlation between 3D TRE and lung motion, which indicated that this algorithm outperformed other DIR algorithms enrolled in this study. Preliminary results demonstrated that the proposed evaluation framework had a potential ability of providing clinical guidance for the selection of appropriate algorithm in radiation therapy.

MRI-guided mid-position liver radiotherapy: Validation of image processing and registration steps

Background & purposeTo propose a novel mid-position (midP) workflow for MRI-guided liver SBRT and provide a validation of the required midP-MRI generation and registration steps. Materials & methodsThe first step of the midP workflow is the generation of a simulation midP-MRI from a 4D-MRI scan using deformable image registration. Next, a planning midP-CT is warped to the midP-MRI to enable planning in the midP-MRI anatomy. For daily MRI-guidance, three different registration methods to the simulation midP-MRI are proposed; (1) 4D rigid registration of all phases of the daily 4D-MRI, (2) 3D rigid registration of the daily midP-MRI, and (3) 3D deformable registration of the daily midP-MRI. The midP-MRI image quality was assessed with respect to 4D-MRI acquisition time, which is related to over-sampling of the data acquisition (i.e. number of dynamics). The deformable registration precision for the midP-MRI generation was validated using the distance discordance metric (DDM). The deformable CT-MRI and daily MRI-MRI registration accuracies were quantified using the ‘full circle method’. ResultsThe DDM was 1.5 mm (median) within the liver, independent of the number of dynamics. The root-mean-squared difference between midP-MRIs based on 10 and 60 dynamics was only 5.2%. The full circle CT-MRI deformable registration error had a median 3D vector length of 1.8 mm in the liver. The daily MRI-MRI registration error was submillimeter for all three evaluated methods. ConclusionThe feasibility of an MRI-guided mid-position workflow for liver SBRT is supported by the demonstrated high precision of all image processing and registration steps.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

Representing the dosimetric impact of deformable image registration errors

Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis.Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from ‘dir-labs’. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU.The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error.The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

An ELECTRE I-based multi-criteria group decision making method with interval type-2 fuzzy numbers and its application to supplier selection

As an extension of type-2 fuzzy numbers (T2 FNs), interval type-2 fuzzy numbers (IT2 FNs) are able to deal more effectively with uncertainty, have better processing abilities, and simpler computations. Because of these abilities, IT2 FNs have been widely applied indecision support systems (DSS). In this paper, to ensure more effective multi-criteria group decision-making in uncertain environments, the elimination and choice translating reality (ELECTRE) method is extended using interval type-2 fuzzy numbers. An α-based distance method is first proposed to measure the proximity between the interval type-2 fuzzy numbers. Then, an entropy measure for the IT2 FNs and an entropy weight model are developed to objectively determine the criteria weights without any weight information. By applying an α-based distance method, the concordance and discordance for each alternative are measured to determine the partial-preference outranking order. A complementary analysis is then conducted to obtain the full rank order of all alternatives. Finally, the feasibility and applicability of the proposed method are detailed using two different practical examples. A sensitivity and comparative analysis are also conducted to demonstrate the strength and practicality of the proposed method.

Geographic Discordance Between Patient Residence and Incident Location in Emergency Medical Services Responses

The location of a patient's residence is often used for emergency medical services (EMS) system planning. Our objective is to evaluate the association between patient residence and emergency incident zip codes for 911 calls. We used data from the 2013 National Emergency Medical Services Information System (NEMSIS) Public-Release Research Dataset. We studied all 911 calls with a valid complaint by dispatch, identifying zip codes for both the residence and incident locations (n=12,376,784). The primary outcomes were geographic and distance discordances between patient residence and incident zip codes. We used a multivariate logistic regression model to determine geographic discordance between residence and incident zip codes by dispatch complaint, age, and sex. We also measured distances between locations with geospatial processing. The overall proportion of geographic discordance for all 911 calls was 27.7% (95% confidence interval [CI] 27.7% to 27.8%) and the median distance discordance was 11.5 miles (95% CI 11.5 to 11.5 miles). Lower geographic discordance rates were found among patients aged 65 to 79 years (20.2%; 95% CI 20.1% to 20.2%) and 80 years and older (14.5%; 95% CI 14.5% to 14.6%). Motor vehicle crashes (63.5%; 95% CI 63.5% to 63.6%), industrial accidents (59.3%; 95% CI 58.0% to 60.6%), and mass casualty incidents (50.6%; 95% CI 49.6% to 51.5%) were more likely to occur outside a patient's residence zip code. Median network distance between home and incident zip centroid codes ranged from 8.6 to 23.5 miles. In NEMSIS, there was geographic discordance between patient residence zip code and call location zip code in slightly more than one quarter of EMS responses records. The geographic discordance rates between residence and incident zip codes were associated with dispatch complaints and age. Although a patient's residence might be a valid proxy for incident location for elderly patients, this relationship holds less true for other age groups and among different complaints. Our findings have important implications for EMS system planning, resource allocation, and injury surveillance.

A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis

Deformable image registration (DIR) is essential for adaptive radiotherapy (RT) for tumor sites subject to motion, changes in tumor volume, as well as changes in patient normal anatomy due to weight loss. Several methods have been published to evaluate DIR-related uncertainties but they are not widely adopted. The aim of this study was, therefore, to evaluate intra-patient DIR for two highly deformable organs—the bladder and the rectum—in prostate cancer RT using a quantitative metric based on multiple image registration, the distance discordance metric (DDM). Voxel-by-voxel DIR uncertainties of the bladder and rectum were evaluated using DDM on weekly CT scans of 38 subjects previously treated with RT for prostate cancer (six scans/subject). The DDM was obtained from group-wise B-spline registration of each patient’s collection of repeat CT scans. For each structure, registration uncertainties were derived from DDM-related metrics. In addition, five other quantitative measures, including inverse consistency error (ICE), transitivity error (TE), Dice similarity (DSC) and volume ratios between corresponding structures from pre- and post- registered images were computed and compared with the DDM. The DDM varied across subjects and structures; DDMmean of the bladder ranged from 2 to 13 mm and from 1 to 11 mm for the rectum. There was a high correlation between DDMmean of the bladder and the rectum (Pearson’s correlation coefficient, Rp = 0.62). The correlation between DDMmean and the volume ratios post-DIR was stronger (Rp = 0.51; 0.68) than the correlation with the TE (bladder: Rp = 0.46; rectum: Rp = 0.47), or the ICE (bladder: Rp = 0.34; rectum: Rp = 0.37). There was a negative correlation between DSC and DDMmean of both the bladder (Rp = −0.23) and the rectum (Rp = −0.63). The DDM uncertainty metric indicated considerable DIR variability across subjects and structures. Our results show a stronger correlation with volume ratios and with the DSC using DDM compared to using ICE and TE. The DDM has the potential to quantitatively identify regions of large DIR uncertainties and consequently identify anatomical/scan outliers. The DDM can, thus, be applied to improve the adaptive RT process for tumor sites subject to motion.

SU-E-J-95: A Novel Objective Approach to Identify Scan Outliers in Deformable Image Registration for Longitudinal Datasets

Purpose: The current practice for evaluating deformable image registration (DIR) is commonly subjective as it involves input from an observer due to lack of an absolute ground truth. We have therefore developed an automated objective method to evaluate DIR in order to identify scan outliers for longitudinal datasets. Methods: The imaging dataset consisted of nine repeated CT scans (CT1–9) from four prostate patients. Six “similar” CT scans (CT1–6) from one patient (PT1) were used as ground truth meanwhile three other CT scans (CT7–9) from PT2–4 served as scan outliers. Voxel-by-voxel DIR-related uncertainties (Distance Discordance Metric, DDM) were evaluated on groups of five CT scans for the following ten scenarios: Scenario 1–5: five out of six CT scans (CT1–6) from PT1 using leave-one-out technique were used; Scenario 6–8: single scan from PT1 was replaced by another CT scan from PT2–4; and Scenario 9–10: two to three CT scans from PT1 were replaced by CT scans from PT2–4. For each scenario, the DDM map was superimposed on CT-1 and two-sample t-test was performed to compare the uncertainty distributions for all 10 scenarios. Results: The mean DDM values were 3.8–4.2 mm for the first five scenarios, 5.1–6.7 mm for scenario 6–8, and 8.3 mm and 8.9 mm for scenario 9 and scenario 10, respectively. The two-sample t-test showed that the DDM distributions for scenarios 1–5 have a similar mean while the distributions for scenarios 6–10 have statistically different means compared to scenario 1–5. Conclusion: The DIR uncertainty distributions as estimated from our method on a set of images from the same patient are of similar magnitude. When images for a patient are replaced by images from other patients, the distribution of the uncertainties changes considerably, motivating the use of DDM as a metric to automatically detect scan outliers.

SU‐E‐J‐62: Estimating Plausible Treatment Course Dose Distributions by Accounting for Registration Uncertainty and Organ Motion

Purpose:Dose accumulation following deformable image registration (DIR) is challenging. In this study, we used a statistical sampling approach, which takes into account both DIR uncertainties and patient‐specific organ motion, to study the distribution of possible true dose distributions.Methods:The study included ten patients (six CT scans/patient) treated with radiotherapy for prostate cancer. For each patient, the planned dose was re‐calculated on the repeated geometries, following rigid registration based on fiducial markers. The dose re‐calculated on the first CT served as our snapshot dose distribution (D1) and the average of the first five repeat scans as our treatment course reference dose distribution (Dref). Patient‐specific motion and DIR‐uncertainties, at each voxel in CT1, were assessed using a previously developed DIR performance measure, the distance discordance metric (DDM). To sample the distribution of possible true, predicted dose distributions (Dpred), we resampled D1 by perturbing the location of each voxel with the corresponding DDM. The three dose distribution approaches are compared for the rectum and the bladder.Results:The bladder generalized equivalent uniform dose (gEUD) from the averaged Dpred was closer to the gEUDref than to the gEUD1 (difference: 0.6 vs. 1.0 Gy). For both structures, the gEUDpred was higher than the gEUDref, and significantly higher (p≤0.05) for the rectum (average: 50.8 Gy vs. 48.0 Gy).Conclusion:We have shown that the bladder gEUD values resulting from our DIR‐uncertainty inclusive dose sampling approach, Dpred, were closer to the gEUD from Dref than the gEUD values from D1. For the rectum, gEUDpred overestimated gEUDref. Theoretically however, gEUDpred values, sampled from DDM uncertainties are more representative of dose uncertainties.

SU‐E‐J‐159: Intra‐Patient Deformable Image Registration Uncertainties Quantified Using the Distance Discordance Metric

Purpose:The quantitative evaluation of deformable image registration (DIR) is currently challenging due to lack of a ground truth. In this study we test a new method proposed for quantifying multiple‐image based DIRrelated uncertainties, for DIR of pelvic images.Methods:19 patients were analyzed, each with 6 CT scans, who previously had radiotherapy for prostate cancer. Manually delineated structures for rectum and bladder, which served as ground truth structures, were delineated on the planning CT and each subsequent scan. For each patient, voxel‐by‐voxel DIR‐related uncertainties were evaluated, following B‐spline based DIR, by applying a previously developed metric, the distance discordance metric (DDM; Saleh et al., PMB (2014) 59:733). The DDM map was superimposed on the first acquired CT scan and DDM statistics were assessed, also relative to two metrics estimating the agreement between the propagated and the manually delineated structures.Results:The highest DDM values which correspond to greatest spatial uncertainties were observed near the body surface and in the bowel due to the presence of gas. The mean rectal and bladder DDM values ranged from 1.1–11.1 mm and 1.5–12.7 mm, respectively. There was a strong correlation in the DDMs between the rectum and bladder (Pearson R = 0.68 for the max DDM). For both structures, DDM was correlated with the ratio between the DIR‐propagated and manually delineated volumes (R = 0.74 for the max rectal DDM). The maximum rectal DDM was negatively correlated with the Dice Similarity Coefficient between the propagated and the manually delineated volumes (R= −0.52).Conclusion:The multipleimage based DDM map quantified considerable DIR variability across different structures and among patients. Besides using the DDM for quantifying DIR‐related uncertainties it could potentially be used to adjust for uncertainties in DIR‐based accumulated dose distributions.

The distance discordance metric—a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration

Previous methods to estimate the inherent accuracy of deformable image registration (DIR) have typically been performed relative to a known ground truth, such as tracking of anatomic landmarks or known deformations in a physical or virtual phantom. In this study, we propose a new approach to estimate the spatial geometric uncertainty of DIR using statistical sampling techniques that can be applied to the resulting deformation vector fields (DVFs) for a given registration. The proposed DIR performance metric, the distance discordance metric (DDM), is based on the variability in the distance between corresponding voxels from different images, which are co-registered to the same voxel at location (X) in an arbitrarily chosen ‘reference’ image. The DDM value, at location (X) in the reference image, represents the mean dispersion between voxels, when these images are registered to other images in the image set. The method requires at least four registered images to estimate the uncertainty of the DIRs, both for inter- and intra-patient DIR. To validate the proposed method, we generated an image set by deforming a software phantom with known DVFs. The registration error was computed at each voxel in the ‘reference’ phantom and then compared to DDM, inverse consistency error (ICE), and transitivity error (TE) over the entire phantom. The DDM showed a higher Pearson correlation (Rp) with the actual error (Rp ranged from 0.6 to 0.9) in comparison with ICE and TE (Rp ranged from 0.2 to 0.8). In the resulting spatial DDM map, regions with distinct intensity gradients had a lower discordance and therefore, less variability relative to regions with uniform intensity. Subsequently, we applied DDM for intra-patient DIR in an image set of ten longitudinal computed tomography (CT) scans of one prostate cancer patient and for inter-patient DIR in an image set of ten planning CT scans of different head and neck cancer patients. For both intra- and inter-patient DIR, the spatial DDM map showed large variation over the volume of interest (the pelvis for the prostate patient and the head for the head and neck patients). The highest discordance was observed in the soft tissues, such as the brain, bladder, and rectum, due to higher variability in the registration. The smallest DDM values were observed in the bony structures in the pelvis and the base of the skull. The proposed metric, DDM, provides a quantitative tool to evaluate the performance of DIR when a set of images is available. Therefore, DDM can be used to estimate and visualize the uncertainty of intra- and/or inter-patient DIR based on the variability of the registration rather than the absolute registration error.

SU‐C‐WAB‐07: Normalized Distance Discordance (DD): A Quantitative and Position‐Specific Metric to Evaluate the Uncertainty of Inter‐and Intra‐Patient Deformable Registrations

Purpose: Quantitative tools to estimate the uncertainty in deformable image registration are lacking. Distance discordance(DD) is a quantity that we have defined, and is an estimate of the mean error (measured in terms of distance) due to deformable image registration. This abstract presents a refined method of estimating distance discordance that improves accuracy. Methods: Starting from an arbitrary reference image [i] in a set of images [1…n], voxels are traced from any two moving images [j] and [k] that happened to be registered at the same voxel on [i] to other reference images in the image set. The difference between each pair of points is recorded in the Cartesian coordinate system (dx,dy,dz). The mean of the difference (&lt;dx&gt;, &lt;dy&gt;, &lt;dz&gt;) is subtracted from the original difference in order to calculate the unbiased distance between these pair of points and which corresponded to distance discordance metric. Experiments using this new approach were evaluated in a set of 8 different head and neck patients. Results: The 3D spatial map of the mean DD metric showed a variation among anatomical sites in the head and neck region. Regions of high contrast (bones) showed a lower mean DD value (2—13 mm) while regions of low contrast (soft tissue) had a larger mean DD value (6—19 mm). The shape of the dose discordance histogram (DDH) for some anatomical structures followed a log normal distribution with a long tail while others had irregular shape. Conclusion: The updated DD metric gives an unbiased measure of the spatial uncertainty in image registration in the absence of a ground truth. For instance, the DD maps can be used to set a level of confidence for dose accumulation in adaptive radiotherapy. However, the DD metric provides a necessary but not a sufficient condition to ensure good registration. Partially supported by NIH grant R01 CA85181; href=http://soundfrost.org/&gt;download free&lt;

SU‐E‐J‐83: Evaluation of Landmark‐Assisted Deformable Image Registration Technique On Inter‐and Intra‐Patient Head and Neck CT Imaging

Purpose: Accurate image registration in patients with Head and Neck (HN) cancer is critical for assessment of treatment response, and must be rigorously validated. To evaluate deformable image registration (DIR) with our previously developed DIR techniques on HN PET‐CT scans, we incorporated distance discordance metric (DDM), a novel method for quantification of local registration uncertainty, as well as traditional quantitative and qualitative measurements. Methods: For each CT image series, six landmark (LM) points relevant for patients with HN cancer were placed, including the inferior‐most extent of the styloid processes, the lateral‐and inferior‐most aspects of the hyoid bone, and the superior central face of the sternum. These points were used either to assist or assess registration.Inter‐patient HN CT image registration for 10 patients was performed using standard intensity‐based B‐Spline and LM‐assisted B‐Spline DIR techniques. Intra‐patient DIR of the same patients was performed using pre‐and post‐radiation therapy image sets of the same patient.DDM metrics were obtained for inter‐patient DIR results and compared to traditional qualitative and quantitative methods including whole image mean square error (MSE), relative landmark distances between scans, and visual judgment by physicians. Results: Registration results were improved with the addition of landmarks on bony structures relevant to HN cancer. While MSE only provides a general estimate of the overall image registration error, the novel DDM metric shows the registration uncertainties for each pixel on the fixed image, allowing for localization of error. Based on DDM, DIR uncertainty was significantly reduced (&gt;50% in region of interest) by LM‐assisted DIR techniques on inter‐patient registration. Relative LM distance was reduced as well for both intra‐and inter‐patient DIR (&lt;1mm). Conclusion: Evaluation of DIR results with novel and traditional methods demonstrates the LM‐assisted DIR approach achieves better registration results on both inter‐and intra‐patient DIR.

WE-E-213CD-11: A New Automatically Generated Metric for Evaluating the Spatial Precision of Deformable Image Registrations: The Distance Discordance Metric.

We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures. Partially supported by NIH grant R01 CA85181.

WE-E-217A-01: CAD: Quality Assurance of CAD Systems Implemented in Clinical Use.

We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures.

WE-E-217BCD-02: Update on the Upcoming ACR Digital Mammography QC Manual.

We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures.

SU-E-J-92: CERR: New Tools to Analyze Image Registration Precision.

To present new tools in CERR (The Computational Environment for Radiotherapy Research) to analyze image registration and other software updates/additions. CERR continues to be a key environment (cited more than 129 times to date) for numerous RT-research studies involving outcomes modeling, prototyping algorithms for segmentation, and registration, experiments with phantom dosimetry, IMRT research, etc. Image registration is one of the key technologies required in many research studies. CERR has been interfaced with popular image registration frameworks like Plastimatch and ITK. Once the images have been autoregistered, CERR provides tools to analyze the accuracy of registration using the following innovative approaches (1)Distance Discordance Histograms (DDH), described in detail in a separate paper and (2)'MirrorScope', explained as follows: for any view plane the 2-d image is broken up into a 2d grid of medium-sized squares. Each square contains a right-half, which is the reference image, and a left-half, which is the mirror flipped version of the overlay image. The user can increase or decrease the size of this grid to control the resolution of the analysis. Other updates to CERR include tools to extract image and dosimetric features programmatically and storage in a central database and tools to interface with Statistical analysis software like SPSS and Matlab Statistics toolbox. MirrorScope was compared on various examples, including 'perfect' registration examples and 'artificially translated' registrations. for 'perfect' registration, the patterns obtained within each circles are symmetric, and are easily, visually recognized as aligned. For registrations that are off, the patterns obtained in the circles located in the regions of imperfections show unsymmetrical patterns that are easily recognized. The new updates to CERR further increase its utility for RT-research. Mirrorscope is a visually intuitive method of monitoring the accuracy of image registration that improves on the visual confusion of standard methods.