S Jani

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases.

PURPOSE To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. METHODS AND MATERIALS Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. RESULTS Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. CONCLUSIONS The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

A comparison of amplitude-based and phase-based positron emission tomography gating algorithms for segmentation of internal target volumes of tumors subject to respiratory motion.

PURPOSE To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET). METHODS AND MATERIALS List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated. RESULTS Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001). CONCLUSIONS Target volumes in images generated from amplitude-based gating are larger and more accurate, at levels that are potentially clinically significant, compared with those from temporal phase-based gating.

Pelvic nodal dosing with registration to the prostate: implications for high-risk prostate cancer patients receiving stereotactic body radiation therapy.

PURPOSE To determine whether image guidance with rigid registration (RR) to intraprostatic markers (IPMs) yields acceptable coverage of the pelvic lymph nodes in the context of a stereotactic body radiation therapy (SBRT) regimen. METHODS AND MATERIALS Four to seven kilovoltage cone-beam CTs (CBCTs) from 12 patients with high-risk prostate cancer were analyzed, allowing approximation of an SBRT regimen. The nodal clinical target volume (CTV(N)) and bladder were contoured on all kilovoltage CBCTs. The V100 CTV(N), expressed as a ratio to the same parameter on the initial plan, and the magnitude of translational shift between RR to the IPMs versus RR to the pelvic bones, were computed. The ability of a multimodality bladder filling protocol to minimize bladder height variation was assessed in a separate cohort of 4 patients. RESULTS Sixty-five CBCTs were assessed. The average V100 CTV(N) was 92.6%, but for a subset of 3 patients the average was 80.0%, compared with 97.8% for the others (P<.0001). The average overall and superior-inferior axis magnitudes of the bony-to-fiducial translations were significantly larger in the subgroup with suboptimal nodal coverage (8.1 vs 3.9 mm and 5.8 vs 2.4 mm, respectively; P<.0001). Relative bladder height changes were also significantly larger in the subgroup with suboptimal nodal coverage (42.9% vs 18.5%; P<.05). Use of a multimodality bladder-filling protocol minimized bladder height variation (P<.001). CONCLUSION A majority of patients had acceptable nodal coverage after RR to IPMs, even when approximating SBRT. However, a subset of patients had suboptimal nodal coverage. These patients had large bony-to-fiducial translations and large variations in bladder height. Nodal coverage should be excellent if the superior-inferior axis bony-to-fiducial translation and the relative bladder height change (both easily measured on CBCT) are kept to a minimum. Implementation of a strict bladder filling protocol may achieve this goal.

Automatic detection of patient identification and positioning errors in radiation therapy treatment using 3-dimensional setup images.

PURPOSE To develop an automated system that detects patient identification and positioning errors between 3-dimensional computed tomography (CT) and kilovoltage CT planning images. METHODS AND MATERIALS Planning kilovoltage CT images were collected for head and neck (H&N), pelvis, and spine treatments with corresponding 3-dimensional cone beam CT and megavoltage CT setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. For positioning errors, setup and planning images were misaligned by 1 to 5 cm in the 6 anatomical directions for H&N and pelvis patients. Spinal misalignments were simulated by misaligning to adjacent vertebral bodies. Image pairs were assessed using commonly used image similarity metrics as well as custom-designed metrics. Linear discriminant analysis classification models were trained and tested on the imaging datasets, and misclassification error (MCE), sensitivity, and specificity parameters were estimated using 10-fold cross-validation. RESULTS For patient identification, our workflow produced MCE estimates of 0.66%, 1.67%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivity and specificity ranged from 97.5% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 95.4% and 97.7%. MCEs for 1-cm H&N/pelvis misalignments were 1.3%/5.1% and 9.1%/8.6% for TomoTherapy and TrueBeam images, respectively. Two-centimeter MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. MCEs for vertebral body misalignments were 4.8% and 3.6% for TomoTherapy and TrueBeam images, respectively. CONCLUSIONS Patient identification and gross misalignment errors can be robustly and automatically detected using 3-dimensional setup images of different energies across 3 commonly treated anatomical sites.

SU‐E‐J‐179: Prediction of Pelvic Nodal Coverage Using Mutual Information Between Cone‐Beam and Planning CTs

PURPOSE To investigate if pelvic nodal coverage for prostate patients undergoing intensity modulated radiotherapy (IMRT) can be predicted using mutual image information computed between planning and cone-beam CTs (CBCTs). METHODS Four patients with high-risk prostate adenocarcinoma were treated with IMRT on a Varian TrueBeam. Plans were designed such that 95% of the nodal planning target volume (PTV) received the prescription dose of 45 Gy (N=1) or 50.4 Gy (N=3). Weekly CBCTs (N=25) were acquired and the nodal clinical target volumes and organs at risk were contoured by a physician. The percent nodal volume receiving prescription dose was recorded as a ground truth. Using the recorded shifts performed by the radiation therapists at the time of image acquisition, CBCTs were aligned with the planning kVCT. Mutual image information (MI) was calculated between the CBCT and the aligned planning CT within the contour of the nodal PTV. Due to variable CBCT fields-of-view, CBCT images covering less than 90% of the nodal volume were excluded from the analysis, resulting in the removal of eight CBCTs. RESULTS A correlation coefficient of 0.40 was observed between the MI metric and the percent of the nodal target volume receiving the prescription dose. One patients CBCTs had clear outliers from the rest of the patients. Upon further investigation, we discovered image artifacts that were present only in that patients images. When those four images were excluded, the correlation improved to 0.81. CONCLUSION This pilot study shows the potential of predicting pelvic nodal dosimetry by computing the mutual image information between planning CTs and patient setup CBCTs. Importantly, this technique does not involve manual or automatic contouring of the CBCT images. Additional patients and more robust exclusion criteria will help validate our findings.

WE‐A‐134‐08: Modeling Cardiac Induced Lung Tissue Motion for a Quantitative Breathing Motion Model

PURPOSE To improve the accuracy of a quantitative breathing motion model by developing a cardiac-induced lung tissue motion model from MRI data. METHODS 10 healthy volunteers were imaged on a 1.5T MR-scanner. A total of 24 short-axis and 18 radial views were acquired during a series of 12-15s breath-holds. The planar views were combined to create a 3D view of the anatomy. Each view contained 30 equal-partitioned frames beginning with the end-diastolic cardiac phase. A single-level 3D optical flow deformable image registration algorithm was used to measure the difference in tissue position between the end-diastolic image and the remaining phases. The maximum displacement magnitude and direction obtained in this manner was defined as g(X0 ), the cardiac-induced lung tissue motion. The motion model was assumed to be linear and the motion trajectory a product of g(X0 ) and h, where h was a phase-dependent scalar that had a value of 0 at end-diastole and 1 at the maximum tissue displacement phase. The model was evaluated by comparing the cardiac-induced lung tissue motion, using a lower motion threshold of 0.3mm, with the residual model error. RESULTS The deformable image registration algorithm was found to be highly accurate. Lung tissue near the myocardium was observed to have motion as large as 5mm. The average relative error for the model was 36.5% for sub-millimeter voxel motion. The average relative error decreased for greater voxel motion to 5.6% for >3mm voxel motion. The overall average model residual error was 0.19±0.18mm. CONCLUSION The magnitude of cardiac-induced lung tissue displacement was enough to degrade the accuracy of quantitative lung tissue motion modeling. The use of a single location-independent phase dependent term provided suitable model accuracy. Introducing a cardiac motion term has the potential to reduce the error in breathing motion models caused by uncompensated cardiac-induced lung tissue motion. This work supported in part by NIH R01CA096679 and R01CA116712.

SU‐E‐T‐261: Development of An Automated System to Detect Patient Identification and Positioning Errors Prior to Radiotherapy Treatment

Purpose: To develop a system that can automatically detect patient identification and positioning errors using 3D computed tomography (CT) setup images and kilovoltage CT (kVCT) planning images. Methods: Planning kVCT images were collected for head-and-neck (H&N), pelvis, and spine treatments with corresponding 3D cone-beam CT (CBCT) and megavoltage CT (MVCT) setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. Positioning errors were simulated by misaligning the setup image by 1cm to 5cm in the six anatomical directions for H&N and pelvis patients. Misalignments for spine treatments were simulated by registering the setup image to adjacent vertebral bodies on the planning kVCT. A body contour of the setup image was used as an initial mask for image comparison. Images were pre-processed by image filtering and air voxel thresholding, and image pairs were assessed using commonly-used image similarity metrics as well as custom -designed metrics. A linear discriminant analysis classifier was trained and tested on the datasets, and misclassification error (MCE), sensitivity, and specificity estimates were generated using 10-fold cross validation. Results: Our workflow produced MCE estimates of 0.7%, 1.7%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivities and specificities ranged from 98.0% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 96.2% and 98.4%. MCEs for 1cm H&N/pelvis misalignments were 1.3/5.1% and 9.1/8.6% for TomoTherapy and TrueBeam images, respectively. 2cm MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. Vertebral misalignment MCEs were 4.8% and 4.9% for TomoTherapy and TrueBeam images, respectively. Conclusion: Patient identification and gross misalignment errors can be robustly and automatically detected using 3D setup images of two imaging modalities across three commonly-treated anatomical sites.

SU‐C‐BRD‐04: Automatic Detection of Patient Identification and Patient Positioning Errors Using 3D Setup Images

PURPOSE To develop an automated system to detect patient identification and patient positioning errors using algorithmic comparison between megavoltage CT (MVCT) and kilovoltage CT (kVCT) planning images. METHODS MVCT images from 35 head and neck (H&N) patients and 19 pelvis patients were collected from a Tomotherapy machine, along with the corresponding planning kVCTs. MVCTs and kVCTs were manually aligned according to clinical protocols at our institution. Patient identification errors were simulated by aligning MVCTs and kVCTs from different patients. Positioning errors were simulated by misaligning MVCTs and kVCTs by 1cm to 5cm in the each of the six anatomical directions. For each image pair, a pixel-by-pixel cross-correlation metric was computed within the MVCT image body contour. To eliminate the effect of daily variations in bowel gas, the metric was limited to voxels with HU>-700. The kVCT voxel intensities were remapped to the MVCT scale using a third-order polynomial from a publicly available software package. RESULTS A threshold pixel-by-pixel cross-correlation value was found that distinguished between correct and incorrect patient setup with a high degree of accuracy. A stratified 10-fold cross-validation analysis yielded average misclassification probabilities of 0.0030 for H&N and 0.00 for pelvis. For misaligned image pairs, cross-validation analysis yielded average misclassification probabilities of 0.00 and 0.0013 for H&N shifts ≥20mm and ≥10mm across all six anatomical directions, respectively. Misclassification probabilities were 0.00, 0.011, and 0.10 for pelvic shifts ≥30mm, ≥20mm, and ≥10mm, respectively. Receiver operator characteristic analysis for misaligned patients yielded areas under the curve ranging from 0.99 to 1.0 for H&N and 0.86 to 1.0 for pelvis. CONCLUSION This proof-ofconcept study shows that pixel-by-pixel cross-correlation of MVCT setup images with their corresponding planning CT images can be used to detect wrong-patient errors as well as incorrect patient shifts in the pelvis and H&N regions.

SU-F-BRD-11: Prediction of Dosimetric Endpoints From Patient Geometry Using Neural Nets

PURPOSE The previously-published overlap volume histogram (OVH) technique lends itself naturally to prediction of the dose received by a given volume of tissue (e.g. D90) in intensity-modulated radiotherapy (IMRT) treatment plans. Here we extend the OVH technique using artificial neural networks in order to predict the volume of tissue receiving a given dose (e.g. V90) in both prostate IMRT and conventional breast radiotherapy. METHODS Twenty-nine prostate treatment plans and forty-three breast treatment plans were analyzed. The spatial relationships between the prostate and rectum and between the breast and ipsilateral lung were characterized using OVHs. The OVH is a cumulative histogram representing the fractional volume of the risk organ overlapped by a series of isotropic expansions of the planning target volume (PTV). Seven cases were identified as outliers and replanned. OVH points were used as inputs to a one hidden layer feed forward artificial neural network with quality parameters of the corresponding plan, such as the rectum V50, as targets. A 3-fold cross-validation was used to estimate the prediction error. RESULTS The root mean square (RMS) error between the predicted rectum V50s and the planned values was 2.3, which was 35% of the standard deviation of V50 for the twenty-nine plans. The RMS error of prediction of V20 of the ipsilateral lung in breast cases was 3.9, which was 90% of the standard deviation of the V20 values in the breast plan database. CONCLUSION This study demonstrates that artificial neural nets can be used to extend the OVH technique to predict dosimetric endpoints taking the form of a volume receiving a given dose, rather than the minimum dose received by a given volume. Prediction of ipsilateral lung dose in breast radiotherapy using the OVH technique remains a work in progress.

TH-C-18A-11: Investigating the Minimum Scan Parameters Required to Generate Free-Breathing Fast-Helical CT Scans Without Motion-Artifacts

PURPOSE A recently proposed 4D-CT protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artifacts, which arise when tissue motion is greater than scan speed. This work identifies the minimum scanner parameters required to successfully generate free-breathing fast-helical scans without doubling-artifacts. METHODS 10 patients were imaged under free breathing conditions 25 times in alternating directions with a 64-slice CT scanner using a low dose fast helical protocol. A high temporal resolution (0.1s) 4D-CT was generated using a patient specific motion model and patient breathing waveforms, and used as the input for a scanner simulation. Forward projections were calculated using helical cone-beam geometry (800 projections per rotation) and a GPU accelerated reconstruction algorithm was implemented. Various CT scanner detector widths and rotation times were simulated, and verified using a motion phantom. Doubling-artifacts were quantified in patient images using structural similarity maps to determine the similarity between axial slices. RESULTS Increasing amounts of doubling-artifacts were observed with increasing rotation times > 0.2s for 16×1mm slice scan geometry. No significant increase in doubling artifacts was observed for 64×1mm slice scan geometry up to 1.0s rotation time although blurring artifacts were observed >0.6s. Using a 16×1mm slice scan geometry, a rotation time of less than 0.3s (53mm/s scan speed) would be required to produce images of similar quality to a 64×1mm slice scan geometry. CONCLUSION The current generation of 16 slice CT scanners, which are present in most Radiation Oncology departments, are not capable of generating free-breathing sorting-artifact-free images in the majority of patients. The next generation of CT scanners should be capable of at least 53mm/s scan speed in order to use a fast-helical 4D-CT protocol to generate a motion-artifact free 4D-CT. NIH R01CA096679.