bluuebunny/crossref_metadata_2025 huggingface dataset:
TU‐C‐12A‐11: Comparisons Between Cu‐ATSM PET and DCE‐CT Kinetic Parameters in Canine Sinonasal Tumors
Authors: La Fontaine M , Bradshaw T , Kubicek L , Forrest L , Jeraj R
Published: July 2014

Purpose:

Regions of poor perfusion within tumors may be associated with higher hypoxic levels. This study aimed to test this hypothesis by comparing measurements of hypoxia from Cu‐ATSM PET to vasculature kinetic parameters from DCE‐CT kinetic analysis.

Methods:

Ten canine patients with sinonasal tumors received one Cu‐ATSM PET/CT scan and three DCE‐CT scans prior to treatment. Cu‐ATSM PET/CT and DCE‐CT scans were registered and resampled to matching voxel dimensions. Kinetic analysis was performed on DCE‐CT scans and for each patient, the resulting kinetic parameter values from the three DCE‐CT scans were averaged together. Cu‐ATSM SUVs were spatially correlated (rspatial) on a voxel‐to‐voxel basis against the following DCE‐CT kinetic parameters: transit time (t1), blood flow (F), vasculature fraction (v1), and permeability (PS). In addition, whole‐tumor comparisons were performed by correlating (rROI) the mean Cu‐ATSM SUV (SUVmean) with median kinetic parameter values.

Results:

The spatial correlations (rspatial) were poor and ranged from ‐0.04 to 0.21 for all kinetic parameters. These low spatial correlations may be due to high variability in the DCE‐CT kinetic parameter voxel values between scans. In our hypothesis, t1 was expected to have a positive correlation, while F was expected to have a negative correlation to hypoxia. However, in wholetumor analysis the opposite was found for both t1 (rROI = ‐0.25) and F (rROI = 0.56). PS and v1 may depict angiogenic responses to hypoxia and found positive correlations to Cu‐ATSM SUV for PS (rROI = 0.41), and v1 (rROI = 0.57).

Conclusion:

Low spatial correlations were found between Cu‐ATSM uptake and DCE‐CT vasculature parameters, implying that poor perfusion is not associated with higher hypoxic regions. Across patients, the most hypoxic tumors tended to have higher blood flow values, which is contrary to our initial hypothesis.

Funding: R01 CA136927
TU‐C‐12A‐12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning
Authors: Perk T , Bradshaw T , Meyer E , Muzahir S , Jeraj R
Published: July 2014

Purpose:

[F‐18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F‐18]NaF scans that correspond to DJD.

Methods:

10 metastatic prostate cancer patients received whole body [F‐18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to train eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers.

Results:

The training set consisted of 37 DJD ROIs with 475 non‐DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non‐DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT.

Conclusion:

Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone.

Research supported by the Prostate Cancer Foundation
TU‐F‐BRE‐01: A High Resolution Micro Fiber Scintillator Detector Optimized for SRS and SBRT in Vivo Real Time Treatment Verification
Authors: Izaguirre E , Price S , Knewtson T , Loyalka S , Rangaraj D
Published: July 2014

Purpose:

We have built a high resolution real time scintillating fiber detector prototype to determine in real time the accuracy of stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT) treatments when only a fraction of the planned dose was delivered. The motivation of this work is to enhance dose delivery accuracy and to achieve error free radiosurgery.

Methods:

A high density array of scintillating fibers and a high speed photo detectors array were integrated to implement a high resolution real time dosimeter that can sample with high resolution pulsed SRS and SBRT beams cross sections. The high efficiency of the developed system allows to read each linac pulse in real time and to compute the accumulated dose and dose errors when only a fraction of the beam was delivered. The fibers are highly packed in a substrate that is directly coupled to two 128 pixel arrays with a pitch matching the fiber spacing to achieve accurate spatial localization. The small cross section of the fiber array allows stacking multiple fiber arrays to measure independent angular profiles that are digitally processed in parallel for real time dosimetry.

Results:

We implemented a high density array detector prototype with a pitch of 0.5 mm, readout speed of 1.2 msec, and a response time of 0.5 usec. The fast reading speed has the capability to determining the dose in flattening free filter beams. The detector can be installed in transmission mode at the output port of a micro‐MLC. Treatment deviations smaller than 3% are detected when less than 1/100 of the planned dose was delivered.

Conclusions:

We built a prototype of a high resolution fiber scintillator array detector for SRS and SBRT in vivo dosimetry. Results show that the developed detector has the potential to assure error free SRS and SBRT treatments.
TU‐F‐BRE‐02: A Practical QA Method for Respiration‐Gated SBRT Verification Using 2D Detector Array
Authors: Chen X , Fan J , Lin T , Price R , Chen L , Ma C
Published: July 2014

Purpose:

Pre‐treatment plan verification for respiration‐gated SBRT is challenging but desired since this type of treatment involve more delivery uncertainty, e.g., tumor motion effect, tissue heterogeneity and gating system QA. This study is to design and verify a QA method for respirationgated SBRT pre‐treatment verification using a 2D detector array.

Methods:

A lung phantom was designed based on the MatriXX QA system, which includes a detector board and a MultiCube phantom. The major modification was that an air chamber (∼5cm thick) was inserted between the top portion of MultiCube phantom and detectors board. A half plastic ball (4cm diameter) was put in the air chamber and attached to the detector board surface to mimic a tumor. The whole lung phantom was placed on a moving stage with a superior‐inferior sinusoidal motion. The whole phantom was aligned to the treatment machine so that the S‐I and L‐R axes of the MatriXX were parallel to the corresponding machine axes during movement. In this way, the moving phantom is equivalent to a stationary phantom with only tumor motion. Following a typical planning procedure for respiration‐gated SBRT treatment, SBRT plans were created using the gated‐MIP as PTV. Treatment delivery was performed on Varian iX machine with respiration gating.

Results:

The measured 2D plane dose showed blurring effect induced by motion and this blurring effect was more noticeable at the peripheral region of the PTV. Nevertheless, the tumor dose was able to be identified by analyzing the detectors attached to the tumor and was well above the prescribed dose.

Conclusion:

The designed lung phantom for respiration‐gated SBRT pre‐treatment verification was tested and was able to identify the dose received by the tumor. Further tests based on large number of patients would be needed to for future routine application.
TU‐F‐BRE‐06: Flattening‐Filter‐Free Beam Quality Correction Factor Determination Using Experimental and Monte Carlo Methods
Authors: Wood M , Desai V , Simiele E , Taneja S , DeWerd L
Published: July 2014

Purpose:

To investigate beam quality correction factors for the flattening‐filter‐free (FFF) energies of the TrueBeam™ accelerator based on a dosimetry formalism for small and nonstandard fields.

Methods:

Three detectors – an Exradin W1 scintillator, Sun Nuclear EDGE diode, and LiF(Mg,Tl) TLD‐100 chips – were investigated to determine their applicability as tools to measure quality correction factors for ionization chambers in the small and nonstandard fields of the TrueBeam™. Volume‐averaging effects and energy dependence were observed in fields ranging from 1×1 to 40×40 cm2 for 6 MV and 10 MV beam energies using both FFF and flattened beam modes. Correction factors were determined for three ionization chambers: an Exradin A12 Farmer‐type chamber, an Exradin A1SL scanning chamber, and an Exradin A26 reference‐class microchamber. Beam quality corrections were also obtained using a benchmarked model of the TrueBeam™ created with the BEAMnrc user code of EGSnrc.

Results:

All three detectors demonstrated measureable energy dependence in the megavoltage range. The EDGE diode was deemed the most appropriate tool for beam quality correction factor measurements due to its low energy dependence and small size; however, alanine will be used in the future to reduce energy dependent effects even further. Measured kQmsr,Q corrections of up to 4% were found for the 6MV FFF and 10 MV FFF beams, corresponding to a discrepancy of up to 3% compared to TG‐51‐determined dose. Up to a 10% kQclin,Qmsr correction was measured for small fields referenced to a 10×10 cm2 field of the same energy. Much larger corrections were determined using the Monte Carlo model, and these discrepancies require further investigation.

Conclusion:

Progress has been made toward determining beam quality correction factors for the small and nonstandard fields of the TrueBeam™ accelerator. Further work must be done to ensure greater accuracy in patient treatments with this new modality.
TU‐F‐BRD‐01: Automatic Segmentation of Multiple Pelvic Organs On a Web‐Based Image Processing and Plan Evaluation Platform (WIPPEP)
Authors: Chai X , Liu L , Xing L
Published: July 2014

Purpose:

To present a novel automatic segmentation method for multiple pelvic organs and integrate into the web‐based image processing and plan evaluation platform (WIPPEP) to provide easy access through a web browser and leverage the power of cloud for faster computation.

Methods:

This segmentation method uses principal component analysis to model organ shapes and hierarchical clustering to model the appearance of each organ surface patch. Coarse and refined segmentation are done iteratively for each surface patch to find the best reference patch profile via a hierarchical cluster decision tree. Final segmentation is obtained using the generated global reference profile. The user chooses the CT image data to be segmented from the image server; the image volume is then visualized in the web browser and an HTTP POST request is sent to the computation server by clicking a button. When the computation server receives the segmentation request, it would then send an HTTP GET request to the image server to download the image volumes, call the segmentation program, and eventually send the segmentation results back to the web server. The segmentation results are transferred in JSON format and quickly passed to the canvas element in the web browser to display the contours.

Results:

For 5 test patients, the average DICE overlaps for prostate, bladder and rectum were 0.75, 0.78 and 0.57, respectively. With the platform running on hospital intranet, it took 20 seconds to load CT image volume to the web‐browser and 80 seconds to receive segmentation results from the computation server.

Conclusion:

This system allows users to access image data and send segmentation requests from a web browser, perform segmentation on a computation server, and quickly visualize resulting segmentation contours on a web browser. The software prototype achieves auto‐segmentation of prostate, bladder and rectum with moderate accuracy in a minute's timeframe.
TU‐F‐BRF‐06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering
Authors: Gou S , Rapacchi S , Hu P , Sheng K
Published: July 2014

Purpose:

The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC).

Methods:

Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean‐shift clustering and K‐SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods.

Results:

DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over‐segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient.

Conclusion:

We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.
TU‐F‐BRF‐07: Accuracy of Routine Treatment Planning 4D and DIBH CT Delineation of the Left Anterior Descending Artery in Radiotherapy
Authors: White B , Vennarini S , Lin L , Freedmen G , Both S
Published: July 2014

Purpose:

To assess the feasibility of routine treatment planning 4DCT and deep inspiration breath‐hold (DIBH) to accurately contour the left anterior descending artery (LAD), a primary indicator of cardiac toxicity, for radiotherapy treatment planning of breast cancer.

Methods:

Ten subjects were imaged with a cardiac‐gated MRI protocol to determine the displacement of a ROI that included the LAD. The subjects performed a series of breath‐hold maneuvers to obtain short‐axis and radial views, which were resampled to create a 3D‐volume. Tissue motion was determined using a multi‐resolution 3D optical flow deformable image registration algorithm. The ROI motion was then used as a spatial boundary to characterize the blurring motion of the LAD in ten patients during clinical 4DCT and DIBH protocols. A radiologist contoured the LAD. Coronary motion‐induced blurring artifacts were quantified by applying an unsharp filter to accentuate the LAD despite motion‐blurring. The 4DCT maximum inhalation and exhalation respiratory phases were co‐registered to determine the LAD displacement during tidal respiration, as visualized in 4DCT.

Results:

The average 90th percentile heart motion for the ROI was 0.7±0.1mm(LR), 1.3±0.6mm(SI), 0.6±0.2mm(AP) in the cardiac‐gated MRI cohort. The average relative increase in the number of voxels comprising the LAD contour was 69.4±4.5% for the DIBH. During tidal respiration, the average relative increase in the LAD contour was 69.3±5.9% and 67.9±4.6% for inhalation and exhalation respiratory phases respectively. The average 90th percentile LAD motion was 4.8±1.1mm(LR), 0.9±0.4mm(SI), 1.9±0.6mm(AP) for the 4DCT cohort, in the absence of cardiac‐gating.

Conclusion:

Uncompensated coronary motion was the dominant form of motion blurring present in the CT images due to the high frequency of the cardiac cycle relative to the respiratory cycle. The 4D and DIBH CT contour delineation of the LAD was consistently overestimated without cardiac‐gating, which could have led to inaccurate dose volume histogram indicators in clinical practice.
TU‐F‐17A‐03: A 4D Lung Phantom for Coupled Registration/Segmentation Evaluation
Authors: Markel D , El Naqa I , Levesque I
Published: July 2014

Purpose:

Coupling the processes of segmentation and registration (regmentation) is a recent development that allows improved efficiency and accuracy for both steps and may improve the clinical feasibility of online adaptive radiotherapy. Presented is a multimodality animal tissue model designed specifically to provide a ground truth to simultaneously evaluate segmentation and registration errors during respiratory motion.

Methods:

Tumor surrogates were constructed from vacuum sealed hydrated natural sea sponges with catheters used for the injection of PET radiotracer. These contained two compartments allowing for two concentrations of radiotracer mimicking both tumor and background signals. The lungs were inflated to different volumes using an air pump and flow valve and scanned using PET/CT and MRI. Anatomical landmarks were used to evaluate the registration accuracy using an automated bifurcation tracking pipeline for reproducibility. The bifurcation tracking accuracy was assessed using virtual deformations of 2.6 cm, 5.2 cm and 7.8 cm of a CT scan of a corresponding human thorax. Bifurcations were detected in the deformed dataset and compared to known deformation coordinates for 76 points.

Results:

The bifurcation tracking accuracy was found to have a mean error of −0.94, 0.79 and −0.57 voxels in the left‐right, anterior‐posterior and inferior‐superior axes using a 1×1×5 mm3 resolution after the CT volume was deformed 7.8 cm. The tumor surrogates provided a segmentation ground truth after being registered to the phantom image.

Conclusion:

A swine lung model in conjunction with vacuum sealed sponges and a bifurcation tracking algorithm is presented that is MRI, PET and CT compatible and anatomically and kinetically realistic. Corresponding software for tracking anatomical landmarks within the phantom shows sub‐voxel accuracy. Vacuum sealed sponges provide realistic tumor surrogate with a known boundary. A ground truth with minimal uncertainty is thus realized that can be used for comparing the performance of registration and segmentation algorithms.
TU‐F‐17A‐04: Respiratory Phase‐Resolved 3D MRI with Isotropic High Spatial Resolution: Determination of the Average Breathing Motion Pattern for Abdominal Radiotherapy Planning
Authors: Deng Z , Pang J , Yang W , Yue Y , Tuli R , Fraass B , Li D , Fan Z
Published: July 2014

Purpose:

To develop a retrospective 4D‐MRI technique (respiratory phase‐resolved 3D‐MRI) for providing an accurate assessment of tumor motion secondary to respiration.

Methods:

A 3D projection reconstruction (PR) sequence with self‐gating (SG) was developed for 4D‐MRI on a 3.0T MRI scanner. The respiration‐induced shift of the imaging target was recorded by SG signals acquired in the superior‐inferior direction every 15 radial projections (i.e. temporal resolution 98 ms). A total of 73000 radial projections obtained in 8‐min were retrospectively sorted into 10 time‐domain evenly distributed respiratory phases based on the SG information. Ten 3D image sets were then reconstructed offline. The technique was validated on a motion phantom (gadolinium‐doped water‐filled box, frequency of 10 and 18 cycles/min) and humans (4 healthy and 2 patients with liver tumors). Imaging protocol included 8‐min 4D‐MRI followed by 1‐min 2D‐realtime (498 ms/frame) MRI as a reference.

Results:

The multiphase 3D image sets with isotropic high spatial resolution (1.56 mm) permits flexible image reformatting and visualization. No intra‐phase motion‐induced blurring was observed. Comparing to 2D‐realtime, 4D‐MRI yielded similar motion range (phantom: 10.46 vs. 11.27 mm; healthy subject: 25.20 vs. 17.9 mm; patient: 11.38 vs. 9.30 mm), reasonable displacement difference averaged over the 10 phases (0.74mm; 3.63mm; 1.65mm), and excellent cross‐correlation (0.98; 0.96; 0.94) between the two displacement series.

Conclusion:

Our preliminary study has demonstrated that the 4D‐MRI technique can provide high‐quality respiratory phase‐resolved 3D images that feature: a) isotropic high spatial resolution, b) a fixed scan time of 8 minutes, c) an accurate estimate of average motion pattern, and d) minimal intra‐phase motion artifact. This approach has the potential to become a viable alternative solution to assess the impact of breathing on tumor motion and determine appropriate treatment margins. Comparison with 4D‐CT in a clinical setting is warranted to assess the value of 4D‐MRI in radiotherapy planning.

This work supported in part by grant 1R03CA173273‐01
TU‐F‐17A‐05: Calculating Tumor Trajectory and Dose‐Of‐The‐Day for Highly Mobile Tumors Using Cone‐Beam CT Projections
Authors: Jones B , Miften M
Published: July 2014

Purpose:

Cone‐beam CT (CBCT) projection images provide anatomical data in real‐time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to determine the trajectory and dose of highly mobile tumors during each fraction of treatment.

Methods:

CBCT images of a respiration phantom were acquired, where the trajectory mimicked a lung tumor with high amplitude (2.4 cm) and hysteresis. A template‐matching algorithm was used to identify the location of a steel BB in each projection. A Gaussian probability density function for tumor position was calculated which best fit the observed trajectory of the BB in the imager geometry. Two methods to improve the accuracy of tumor track reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation, and second, using the Monte Carlo method to sample the estimated Gaussian tumor position distribution. 15 clinically‐drawn abdominal/lung CTV volumes were used to evaluate the accuracy of the proposed methods by comparing the known and calculated BB trajectories.

Results:

With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root‐mean‐square (RMS) trajectory errors were lower than 5% of marker amplitude. Use of respiratory phase information decreased RMS errors by 30%, and decreased the fraction of large errors (>3 mm) by half. Mean dose to the clinical volumes was calculated with an average error of 0.1% and average absolute error of 0.3%. Dosimetric parameters D90/D95 were determined within 0.5% of maximum dose. Monte‐Carlo sampling increased RMS trajectory and dosimetric errors slightly, but prevented over‐estimation of dose in trajectories with high noise.

Conclusions:

Tumor trajectory and dose‐of‐the‐day were accurately calculated using CBCT projections. This technique provides a widely‐available method to evaluate highly‐mobile tumors, and could facilitate better strategies to mitigate or compensate for motion during SBRT.
TU‐F‐17A‐07: Real‐Time Personalized Margins
Authors: Rottmann J , Keall P , Berbeco R
Published: July 2014

Purpose:

To maximize normal tissue sparing for treatments requiring motion encompassing margins. Motion mitigation techniques including DMLC or couch tracking can freeze tumor motion within the treatment aperture potentially allowing for smaller treatment margins and thus better sparing of normal tissue. To enable for a safe application of this concept in the clinic we propose adapting margins dynamically in real‐time during radiotherapy delivery based on personalized tumor localization confidence. To demonstrate technical feasibility we present a phantom study.

Methods:

We utilize a realistic anthropomorphic dynamic thorax phantom with a lung tumor model embedded close to the spine. The tumor, a 3D‐printout of a patient's GTV, is moved 15mm peak‐to‐peak by diaphragm compression and monitored by continuous EPID imaging in real‐time. Two treatment apertures are created for each beam, one representing ITV ‐based and the other GTV‐based margin expansion. A soft tissue localization (STiL) algorithm utilizing the continuous EPID images is employed to freeze tumor motion within the treatment aperture by means of DMLC tracking. Depending on a tracking confidence measure (TCM), the treatment aperture is adjusted between the ITV and the GTV leaf.

Results:

We successfully demonstrate real‐time personalized margin adjustment in a phantom study. We measured a system latency of about 250 ms which we compensated by utilizing a respiratory motion prediction algorithm (ridge regression). With prediction in place we observe tracking accuracies better than 1mm. For TCM=0 (as during startup) an ITV‐based treatment aperture is chosen, for TCM=1 a GTV‐based aperture and for 0<TCM<1 a linear interpolation between the two apertures is chosen.

Conclusions:

We have proposed and demonstrated a concept to safely shrink treatment margins during radiotherapy and adapt to tracking confidence in real‐time. Normal tissue sparing is maximized. The worst case scenario results in delivering a plan with standard margins used in the clinic today.
TU‐F‐18A‐01: Preliminary Results of a Prototype Quality Control Process for Spectral CT
Authors: Nute J , Jacobsen M , Pennington J , Chandler A , Imai Y , Baiu C , Cody D
Published: July 2014

Purpose:

A prototype quality control (QC) phantom and analysis process has been designed specifically to monitor dual‐energy CT and address the current lack of quantitative oversight of the spectral capabilities of these scanners.

Methods:

A prototype solid water phantom was designed with multiple material inserts, and to support both head and body protocols. Inserts included tissue equivalent and material rods (iodine, iron, calcium) at various concentrations. The oval body phantom, measuring 30cm×40cm×15cm, was scanned using four dual‐energy protocols with CTDIvol ranges of 19.6–62mGy (0.516 pitch) and 10.3–32.5mGy (0.984 pitch), and rotation times ranging from 0.5‐1sec. The circular head phantom, measuring 22cm in diameter by 15cm, was scanned using three dual‐energy protocols with CTDIvol ranges of 67–132.6mGy (0.531 pitch) and 36.7–72.7mGy (0.969 pitch), and rotation times ranging from 0.5–0.9sec. All images were reconstructed at 50, 70, 110 and 140 keV, and using a water‐iodine material basis pair. The images were evaluated for iodine quantification accuracy and stability of monoenergetic reconstructions. The phantom was scanned twice on ten GE 750HD CT scanners to evaluate inter‐scanner agreement, as well as ten times on a single scanner over a oneweek period to evaluate intra‐scanner repeatability.

Results:

Preliminary analysis revealed consistent (inter‐ and intra‐scanner) iodine quantification accuracy within 10% was only achieved for protocols in the upper half of dose levels assessed when grouped by pitch. Although all scanners undergo rigorous daily single‐energy QC, iodine quantification accuracy from one scanner unexpectedly deviated from the other nine substantially. In general, inter‐scanner agreement and intra‐scanner repeatability varied with dose, rotation time and reconstructed keV.

Conclusion:

Preliminary results indicate the need for a dual‐energy QC process to ensure inter‐scanner agreement and intra‐scanner repeatability. In particular, iodine quantification accuracy within 10% may not be achievable using lower dose techniques. Future plans include longer term dual‐energy CT QC data collection.

In‐kind financial support was provided by GE Healthcare.
TU‐F‐18A‐02: Iterative Image‐Domain Decomposition for Dual‐Energy CT
Authors: Niu T , Dong X , Petrongolo M , Zhu L
Published: July 2014

Purpose:

Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Direct decomposition via matrix inversion suffers from significant degradation of image signal‐to‐noise ratios, which reduces clinical value. Existing de‐noising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. We propose an iterative image‐domain decomposition method for noise suppression in DECT, using the full variance‐covariance matrix of the decomposed images.

Methods:

The proposed algorithm is formulated in the form of least‐square estimation with smoothness regularization. It includes the inverse of the estimated variance‐covariance matrix of the decomposed images as the penalty weight in the least‐square term. Performance is evaluated using an evaluation phantom (Catphan 600) and an anthropomorphic head phantom. Results are compared to those generated using direct matrix inversion with no noise suppression, a de‐noising method applied on the decomposed images, and an existing algorithm with similar formulation but with an edge‐preserving regularization term.

Results:

On the Catphan phantom, our method retains the same spatial resolution as the CT images before decomposition while reducing the noise standard deviation of decomposed images by over 98%. The other methods either degrade spatial resolution or achieve less low‐contrast detectability. Also, our method yields lower electron density measurement error than direct matrix inversion and reduces error variation by over 97%. On the head phantom, it reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures.

Conclusion:

We propose an iterative image‐domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. The proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low‐contrast detectability.

This work is supported by a Varian MRA grant.
WE‐D‐18A‐07: Development and Evaluation of Automated Noise Measurement Technique in CT Images
Authors: Chun M , Kim J
Published: July 2014

Purpose:

To develop and evaluate an automated technique which measures the noise level in CT images.

Methods:

Candidate regions of interests (ROIs) with circular shape were selected with a criteria where the pixel intensities on Gaussian filtered image lie within a predefined range. The ROIs were further refined with the sum of absolute gradients within each candidate ROI being used as a ROI feature to distinguish homogeneous ROIs. A histogram of ROI feature was created, and the ROIs with feature values less than the mode of histogram were selected as homogeneous ROIs. Connected component analysis was then used to remove the non‐clustered ROIs to restrict the location of ROI in desired organ. Standard deviation of pixel intensities was calculated for each cluster of homogeneous ROIs to represent the local noise level, and its mode value was used to represent the noise level. Fifty abdomen CT images were collected, and five observers participated to manually measure the noise level with a guidance to draw same size ROIs. Manual results were then averaged to establish a reference value, and a correlation coefficient(r) between a reference value and our automatic result. Intra‐ and inter‐rater agreement were compared to demonstrate the performance of our proposed technique.

Results:

We confirmed successful operation of automatic homogeneous region localization and noise level measurement. Intra‐ and inter‐rater agreement(r) ranged from 0.70 to 0.95, and from 0.79 to 0.94, respectively. Agreements between reference and manual measurements by raters ranged from 0.85 to 0.95, and those between a reference and an automated result by our proposed method was 0.90.

Conclusion:

Our developed technique has successfully performed the noise measurement task in all the test images by showing an agreement of 0.90 with the reference measurement. Our proposed method enables the automated assessment of CT image noise.
WE‐D‐9A‐02: Automated Landmark‐Guided CT to Cone‐Beam CT Deformable Image Registration
Authors: Kearney V , Gu X , Chen S , Jiang L , Liu H , Chiu T , Yordy J , Nedzi L , Mao W
Published: July 2014

Purpose:

The anatomical changes that occur between the simulation CT and daily cone‐beam CT (CBCT) are investigated using an automated landmark‐guided deformable image registration (LDIR) algorithm with simultaneous intensity correction. LDIR was designed to be accurate in the presence of tissue intensity mismatch and heavy noise contamination.

Method:

An auto‐landmark generation algorithm was used in conjunction with a local small volume (LSV) gradient matching search engine to map corresponding landmarks between the CBCT and planning CT. The LSVs offsets were used to perform an initial deformation, generate landmarks, and correct local intensity mismatch. The landmarks act as stabilizing controlpoints in the Demons objective function. The accuracy of the LDIR algorithm was evaluated on one synthetic case with ground truth and data of ten head and neck cancer patients. The deformation vector field (DVF) accuracy was accessed using a synthetic case. The Root mean square error of the 3D canny edge (RMSECE), mutual information (MI), and feature similarity index metric (FSIM) were used to access the accuracy of LDIR on the patient data. The quality of the corresponding deformed contours was verified by an attending physician.

Results:

The resulting 90 percentile DVF error for the synthetic case was within 5.63mm for the original demons algorithm, 2.84mm for intensity correction alone, 2.45mm using controlpoints without intensity correction, and 1.48 mm for the LDIR algorithm. For the five patients the mean RMSECE of the original CT, Demons deformed CT, intensity corrected Demons CT, control‐point stabilized deformed CT, and LDIR CT was 0.24, 0.26, 0.20, 0.20, and 0.16 respectively.

Conclusion:

LDIR is accurate in the presence of multimodal intensity mismatch and CBCT noise contamination. Since LDIR is GPU based it can be implemented with minimal additional strain on clinical resources.

This project has been supported by a CPRIT individual investigator award RP11032.
WE‐E‐BRE‐02: BEST IN PHYSICS (THERAPY) – Stereotactic Radiotherapy for Renal Sympathetic Ablation for the Treatment of Refractory Hypertension
Authors: Maxim P , Wheeler M , Maguire P , Loo B
Published: July 2014

Purpose:

To determine the safety and efficacy of stereotactic radiotherapy as a novel treatment for patients with refractory hypertension in a swine model. Uncontrolled hypertension is a significant contributor to morbidity and mortality, substantially increasing the risk of ischemic stroke, ischemic heart disease, and kidney failure.

Methods:

High‐resolution computed tomography (CT) images of anesthetized pigs were acquired and treatment plans for each renal artery and nerve were developed using our clinically implemented treatment planning system. Stereotactic radiotherapy, 40Gy in single fraction was delivered bilaterally to the renal nerves using a state‐of‐the‐art medical linear accelerator under image guidance utilizing dynamic conformal arcs. Dose to nearby critical organs was evaluated by dosevolume histogram analysis and correlated to toxicity data obtained through follow up pathology analysis. The animals were observed for six months with serial measurements of blood pressure, urine analysis, serum laboratories, and overall clinical and behavioral status.

Results:

All animals survived to the follow‐up point without evidence of renal dysfunction (stable serum creatinine), skin changes, or behavioral changes that might suggest animal discomfort. Plasma norepinephrine levels (ng/ml) were followed monthly for 6 months. The average reduction observed was 63%, with the median reduction at 73.5%. Microscopic evaluation 4–6 weeks after treatment showed evidence of damage to the nerves around treated renal arteries. Considerable attenuation in pan neurofilament expression by immunohistochemistry was observed with some vacuolar changes indicative of injury. There was no histological or immunohistochemical evidence of damage to nearby spinal cord or spinal nerve root structures.

Conclusion:

Our preclinical studies have shown stereotactic radiotherapy to the renal sympathetic plexus to be safe and effective in reducing blood pressure, thus this approach holds great promise as a novel treatment modality for patients with refractory hypertension.

This study was funded by the Stanford University Cardiovascular Institute. B. Loo and P. Maxim have received funding from RaySearch Laboratories.
WE‐E‐BRE‐03: Biological Validation of a Novel High‐Throughput Irradiator for Predictive Radiation Sensitivity Bioassays
Authors: Fowler TL , Martin JA , Shepard AJ , Bailey AM , Nickel KP , Kimple RJ , Bednarz BP
Published: July 2014

Purpose:

The large dose‐response variation in both tumor and normal cells between individual patients has led to the recent implementation of predictive bioassays of patient‐specific radiation sensitivity in order to personalize radiation therapy. This exciting new clinical paradigm has led us to develop a novel high‐throughput, variable dose‐rate irradiator to accompany these efforts. Here we present the biological validation of this irradiator through the use of human cells as a relative dosimeter assessed by two metrics, DNA double‐strand break repair pathway modulation and intercellular reactive oxygen species production.

Methods:

Immortalized human tonsilar epithelial cells were cultured in 96‐well micro titer plates and irradiated in groups of eight wells to absorbed doses of 0, 0.5, 1, 2, 4, and 8 Gy. High‐throughput immunofluorescent microscopy was used to detect γH2AX, a DNA double‐strand break repair mechanism recruiter. The same analysis was performed with the cells stained with CM‐H2DCFDA that produces a fluorescent adduct when exposed to reactive oxygen species during the irradiation cycle.

Results:

Irradiations of the immortalized human tonsilar epithelial cells at absorbed doses of 0, 0.5, 1, 2, 4, and 8 Gy produced excellent linearity in γH2AX and CM‐H2DCFDA with R2 values of 0.9939 and 0.9595 respectively. Single cell gel electrophoresis experimentation for the detection of physical DNA double‐strand breaks in ongoing.

Conclusions:

This work indicates significant potential for our high‐throughput variable dose rate irradiator for patient‐specific predictive radiation sensitivity bioassays. This irradiator provides a powerful tool by increasing the efficiency and number of assay techniques available to help personalize radiation therapy.
WE‐E‐BRE‐07: High‐Throughput Mapping of Proton Biologic Effect
Authors: Bronk L , Guan F , Kerr M , Dinh J , Titt U , Mirkovic D , Lin S , Mohan R , Grosshans D
Published: July 2014

Purpose:

To systematically relate the relative biological effectives (RBE) of proton therapy to beam linear energy transfer (LET) and dose.

Methods:

Using a custom irradiation apparatus previously characterized by our group, H460 NSCLCs were irradiated using a clinical 80MeV spot scanning proton beam. Utilizing this system allowed for high‐throughput clonogenic assays performed in 96‐well tissue culture plates as opposed to the traditional 6‐well technique. Each column in the 96‐well plate received a set LET‐dose combination. By altering the total number of dose repaintings, numerous dose‐LET configurations were examined to effectively generate surviving fraction (SF) data over the entire Bragg peak. The clonogenic assay was performed post‐irradiation using an INCell Analyzer for colony quantification. SF data were fit to the linear‐quadratic model for analysis.

Results:

Irradiation with increasing LETs resulted in decreased cell survival largely independent of dose. A significant correlation between LET and SF was identified by two‐way ANOVA and the extra sum‐of‐squares F test. This trend was obscured at the lower LET values in the plateau region of the Bragg peak; however, it was clear for LET values at and beyond the Bragg peak. Data fits revealed the SF at a dose of 2Gy (SF2) to be 0.48 for the lowest tested LET (1.55keV/um), 0.47 at the end of the plateau region (4.74keV/um) and 0.33 for protons at the Bragg peak (10.35keV/um). Beyond the Bragg peak we measured SF2s of 0.16 for 15.01keV/um, 0.02 for 16.79keV/um, and 0.004 for 18.06keV/um.

Conclusion:

We have shown that our methodology enables high‐content automated screening for proton irradiations over a range of LETs. The observed decrease in cellular SF in high LET regions confirms an increased RBE of the radiation and suggests further evaluation of proton RBE values is necessary to optimize clinical outcomes.

Rosalie B. Hite Graduate Fellowship in Cancer Research, NIH Program Project Grant P01CA021239
WE‐E‐BRE‐08: Impact of IUdR in Rat 9L Glioma Cell Survival for 25–35 KeV Photo‐Activated Auger Electron Therapy
Authors: Alvarez D , Hogstrom K , Brown T , Dugas J , Matthews K , Varnes M
Published: July 2014

Purpose:

To determine the biological effect from Auger electrons with 9% and 18% iododeoxyuridine (IUdR) incorporated into the DNA of rat 9L glioma cells at photon energies above and below the K‐edge of iodine (33.2 keV).

Methods:

Rat 9L glioma cell survival versus dose curves with 0%, 9%, and 18% thymidine replacement with IUdR were measured using four irradiation energies (4 MV x‐rays; monochromatic 35, 30, and 25 keV synchrotron photons). For each of 11 conditions (Energy, %IUdR) survival curves were fit to the data (826 cell cultures) using the linear‐quadratic model. The ratio of doses resulting in 10% survival gave sensitization enhancement ratios (SER10) from which contributions due to linear‐energy transfer (LET), radiosensitization (RS), and Auger effect (AE) were extracted.

Results:

At 35, 30, and 25 keV, SER10,LET values were 1.08±0.03, 1.22±0.02, and 1.37±0.02, respectively. At 4 MV SER10,RS values for 9% and 18% IUdR were 1.28±0.02 and 1.40±0.02, respectively. Assuming LET effects are independent of %IUdR and radiosensitization effects are independent of energy, SER10,AE values for 18% IUdR at 35, 30, and 25 keV were 1.35±0.05, 1.06±0.03, and 0.98±0.03, respectively; values for 9% IUdR at 35 and 25 keV were 1.01±0.04 and 0.82±0.02, respectively.

Conclusion:

For 18% IUdR the radiosensitization effect of 1.40 and the Auger effect of 1.35 at 35 keV are equally important to the combined effect of 1.90. No measureable Auger effect was observed for energies below the K‐edge at 20 and 25 keV, as expected. The insignificant Auger effect at 9% IUdR was not expected. Additional data (40–70 keV) and radiobiological modeling are being acquired to better understand the energy dependence of Auger electron therapy with IUdR.

Funding support in part by the National Science Foundation Graduate Research Fellowship Program and in part by Contract No. W81XWH‐10‐1‐0005 awarded by the U.S. Army Research Acquisition Activity. This paper does not necessarily reflect the position or policy of the Government, and no official endorsement should be inferred.
WE‐E‐BRE‐09: Investigation of the Association Between Radiation‐Induced Pain and Radiation Dose in Head and Neck Cancer Patients
Authors: Gay H , Oh J , Apte A , Dyk P , Mullen D , Eschen L , Fergus S , Chin R , Thorstad W , Deasy J
Published: July 2014

Purpose:

Patients with head and neck cancer who undergo radiotherapy often experience several undesirable side‐effects, including xerostomia, trismus, and pain in the head and neck area, but little is know about the dose‐volume predictors of such pain. We investigated the association between radiation dose and both throat and esophagus pain during radiotherapy.

Methods:

We analyzed 124 head and neck patients who received radiotherapy at the Washington University School of Medicine in Saint Louis. For these patients, weekly PROs were recorded, including 16 pain and anatomical location questions. In addition, 17 observational symptoms were recorded. Patients were asked to describe their pain at each site according to a four‐level scale: none (0), mild (1), moderate (2), and severe (3). We explored the association between throat pain and the mean dose received in oral cavity and between esophageal pain and the mean dose received in the esophagus. The severity of pain was determined by the difference between the baseline (week 1) pain score and the maximum pain score during treatment. The baseline pain score was defined as the first available pain score before receiving 10 Gy because radiotherapy pain originates later during treatment. Dose‐volume metrics were extracted from treatment plans using CERR. To evaluate the correlation between pain and radiation dose, Spearman's correlation coefficient (Rs) was used.

Results:

The associations between throat pain and the mean dose to the oral cavity, and between esophagus pain and the mean dose to the esophagus, were both statistically significant, with Rs=0.320 (p=0.003) and Rs=0.424 (p<0.0001), respectively. Mean dose, for each structure, was a better predictor of pain than total integral dose.

Conclusion:

We demonstrated that pain during radiotherapy in head and neck patients highly correlates with the dose delivered. We will further investigate the association between other pain locations and relevant normal tissue dose characteristics.
WE‐E‐17A‐02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG‐PET Images
Authors: Li R , Aguilera T , Shultz D , Rubin D , Diehn M , Loo B
Published: July 2014

Purpose:

This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG‐PET images.

Methods:

We acquired pre‐treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity‐volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayes (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10‐fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC).

Results:

Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity.

Conclusion:

Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.