Huchen Xie

Intra- and inter-radiation therapist reproducibility of daily isocenter verification using prostatic fiducial markers

BackgroundWe sought to determine the intra- and inter-radiation therapist reproducibility of a previously established matching technique for daily verification and correction of isocenter position relative to intraprostatic fiducial markers (FM).Materials and methodsWith the patient in the treatment position, anterior-posterior and left lateral electronic images are acquired on an amorphous silicon flat panel electronic portal imaging device. After each portal image is acquired, the therapist manually translates and aligns the fiducial markers in the image to the marker contours on the digitally reconstructed radiograph. The distances between the planned and actual isocenter location is displayed. In order to determine the reproducibility of this technique, four therapists repeated and recorded this operation two separate times on 20 previously acquired portal image datasets from two patients. The data were analyzed to obtain the mean variability in the distances measured between and within observers.ResultsThe mean and median intra-observer variability ranged from 0.4 to 0.7 mm and 0.3 to 0.6 mm respectively with a standard deviation of 0.4 to 1.0 mm. Inter-observer results were similar with a mean variability of 0.9 mm, a median of 0.6 mm, and a standard deviation of 0.7 mm. When using a 5 mm threshold, only 0.5% of treatments will undergo a table shift due to intra or inter-observer error, increasing to an error rate of 2.4% if this threshold were reduced to 3 mm.ConclusionWe have found high reproducibility with a previously established method for daily verification and correction of isocenter position relative to prostatic fiducial markers using electronic portal imaging.

Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments

Registration is critical for image‐based treatment planning and image‐guided treatment delivery. Although automatic registration is available, manual, visual‐based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3‐dimensional (3D) volumetric image representations. It is also limited to single‐pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real‐time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1°. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of 5.0±0.1mm. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: 0.02±0.09mm for CT/CT images, 0.03±0.07mm for MR/MR images, and 0.03±0.35mm for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications. PACS numbers: 87.57.nj, 87.57.nm, 87.57.‐N, 87.57.‐s

Correction of motion‐induced misalignment in co‐registered PET/CT and MRI (T1/T2/FLAIR) head images for stereotactic radiosurgery

The purpose was to evaluate and correct the co‐registration of diagnostic PET/CT and MRI/MRI images for stereotactic radiosurgery (SRS) using 3D volumetric image registration (3DVIR). The 3DVIR utilizes the homogeneity of color distribution over a volumetric anatomical landmark as the registration criterion with submillimeter accuracy. Fifty‐three PET/CT and MRI (T1, T2 and FLAIR) image sets of patients with brain lesions were acquired sequentially from a hybrid PET/CT or an MRI scanner with common diagnostic head holding devices. Twenty‐five sets of head  18F−FDG−PET/CT images were scanned over a 10‐minute interval and 14 whole‐body sets were scanned over a 30‐minute interval. Fourteen sets of MRI images were acquired, and each 3‐modal image set (T1, T2 and FLAIR) was scanned in sequence at time 0, ~5 and ~20 minutes. The misalignments in these “co‐registered” images were evaluated and corrected using the 3DVIR. Using the head immobilization devices commonly found in diagnostic PET/CT and MRI/MRI imaging, 80%–100% of these “co‐registered” images were identified as misaligned. For PET/CT, the magnitude of misalignment was 0.4°±0.5° and 0.7±0.4mm for 10‐minute scans, and 0.8°±1.2° and 2.7±1.7mm for 30‐minute scans. For MRI/MRI, the magnitude was 0.2°±0.4° and 0.3±0.2mm for 5‐minute scan intervals, and 1.1°±0.7° and 1.2±1.4mm for 20‐minute intervals. Small, but significant, misalignment is present in the co‐registered diagnostic PET/CT and MRI/MRI images and can be corrected in SRS treatment planning using the volumetric image registration for improved target localization within the clinical error tolerance. PACS numbers: 87.53.Ly, 87.57.nj, 87.57.uk, 87.57.Q‐, 87.61.jc, 87.19.xc Conflict of Interest Statement: The authors do not have any conflict of interest on this research report.

Patient-specific CT dosimetry calculation: a feasibility study

Current estimation of radiation dose from computed tomography (CT) scans on patients has relied on the measurement of Computed Tomography Dose Index (CTDI) in standard cylindrical phantoms, and calculations based on mathematical representations of “standard man”. Radiation dose to both adult and pediatric patients from a CT scan has been a concern, as noted in recent reports. The purpose of this study was to investigate the feasibility of adapting a radiation treatment planning system (RTPS) to provide patient‐specific CT dosimetry. A radiation treatment planning system was modified to calculate patient‐specific CT dose distributions, which can be represented by dose at specific points within an organ of interest, as well as organ dose‐volumes (after image segmentation) for a GE Light Speed Ultra Plus CT scanner. The RTPS calculation algorithm is based on a semi‐empirical, measured correction‐based algorithm, which has been well established in the radiotherapy community. Digital representations of the physical phantoms (virtual phantom) were acquired with the GE CT scanner in axial mode. Thermoluminescent dosimeter (TLDs) measurements in pediatric anthropomorphic phantoms were utilized to validate the dose at specific points within organs of interest relative to RTPS calculations and Monte Carlo simulations of the same virtual phantoms (digital representation). Congruence of the calculated and measured point doses for the same physical anthropomorphic phantom geometry was used to verify the feasibility of the method. The RTPS algorithm can be extended to calculate the organ dose by calculating a dose distribution point‐by‐point for a designated volume. Electron Gamma Shower (EGSnrc) codes for radiation transport calculations developed by National Research Council of Canada (NRCC) were utilized to perform the Monte Carlo (MC) simulation. In general, the RTPS and MC dose calculations are within 10% of the TLD measurements for the infant and child chest scans. With respect to the dose comparisons for the head, the RTPS dose calculations are slightly higher (10%–20%) than the TLD measurements, while the MC results were within 10% of the TLD measurements. The advantage of the algebraic dose calculation engine of the RTPS is a substantially reduced computation time (minutes vs. days) relative to Monte Carlo calculations, as well as providing patient‐specific dose estimation. It also provides the basis for a more elaborate reporting of dosimetric results, such as patient specific organ dose volumes after image segmentation. PACS numbers: 87.55.D‐, 87.57.Q‐, 87.53.Bn, 87.55.K‐

Registering Molecular Imaging Information into Anatomic Images with Improved Spatial Accuracy

To make molecular imaging useful in the clinic, accurate image registration must be done to correlate nano-scale events to macro-scale anatomy. The 3D volumetric image registration technique uses visual and quantitative measures to identify the most homogeneous color distribution on a volumetric anatomical landmark. Four phantom PET/CT images were acquired with 5.0 plusmn 0.1 mm shift interval. The image registration shift was compared with the positioning shift. An accuracy of 0.1deg and 0.1 mm was achieved. Cranial PET/CT images from 39 patients were examined. It was found that the average head motion was 0.5-1deg and 1-3 mm, even with a stringent head holder. This small but significant misalignment is beyond the capability of conventional visual-based fusion methods used clinically. The 100 mum accuracy is a step forward to register molecular activities to anatomy for high precision interventions

A feasibility study of image registration using volumetrically classified, motion-free bony landmarks in thoracic 4DCT images for image-guided patient setup

As rigid image registration becomes unreliable in the presence of involuntary organ motion, we present a novel approach to register CT images using stable bony landmarks for image-guided patient setup. Using 3D Volumetric Image Registration (3DVIR) technique, bony anatomy is volumetrically-classified as registration landmark, while soft tissues are ignored. Based on 4DCT, it was found that the spine, posterior ribs and clavicles do not move with respiration and remain registered throughout the breathing cycle. However, mutual information based registration produces an error of 1–2 mm due to moving soft tissues. It is suggested that the 3DVIR can improve image-guided setup.

SU‐E‐I‐51: Metal Artifact Reduction in CT Using Deformable Tissue‐Class Modeling

PURPOSE The purpose of this work is to present a novel approach to reduce metal artifacts in CT using deformable tissue-class modeling. The tissue-class model is generated by combining information from both original corrupted image slice (original slice) with metal artifacts, and its neighboring image slice (reference slice) in the same scan, but without metal artifacts. Missing or corrupted information in the original slice is estimated from the reference slice. METHODS The proposed method consists of four major steps. (1) Reference slice is deformed to the original slice using diffeomorphic demons registration algorithm. (2) Strong bright streak including metal objects, and dark streak artifacts are segmented respectively, by applying the basic connected threshold method on the difference image between the original and deformed reference image. (3) Pixel intensities of strong bright and dark streaks in original slice are replaced by those of corresponding pixels in deformed reference slice. The k-means clustering algorithm is then utilized to segment the original slice into four tissue classes: air, soft tissue, normal tissue, and bone. This tissue-class model is forward projected to produce a model sinogram. (4) Corrupted projection data in the sinogram of the original slice is substituted by corresponding segments in the model sinogram. The completed sinogram is then reconstructed with the filtered back-projection to produce the corrected image. RESULTS The proposed method has been tested on clincal patient data with dental fillings, prostate fiducial markers. Both qualitative and quantitative analysis indicate that image quality has been improved considerably after correction, and the proposed method outperforms the standard linear-interpolation based method, and the method using tissue-class modelling on the original slice only. CONCLUSION A novel method for metal artifact reduction in CT has been developed. The method is capable of reducing bright and dark streaks caused by metal objects in CT.

A 4DRT simulation study using a synthetic 3.5D CT image with motion-free target of lung cancer based on 4DCT

Four-Dimensional Radiotherapy (4DRT) planning based on 4D Computed Tomography (4DCT) is limited by drastically increased planning workload and unavoidably needed new software. We introduced a new concept of 3.5DCT for a simplified 4DRT planning. This 3.5DCT is synthesised from 4DCT by aligning the tumour centres in each phase, simulating a motion-compensated tumour from the beam eye’s view. Four patients’ 4DCTs were used for the 4DRT planning, which were compared with motion-trajectory-based and respiratory-gated plans. This simplified 4DRT plan shows dosimetric advantage of significant reduction of V95 and V20. This suggests that the static 3.5DCT image is sufficient for 4DRT planning.

SU‐E‐T‐90: Determination of Orthovoltage Beam‐Hardening Filters Generating the NIST‐Traceable Beam Quality

Purpose: We performed Monte Carlo simulation using EGSnrc for simulating an X‐RAD 320 Biological Irradiator (Precision X‐Ray Inc., North Branford, CT) which generates orthovoltage x‐rays. Monte‐Carlo simulation was used to determine an appropriate filter to generate the NIST‐traceable beam quality so that a calibration factor necessary for a secondary chamber can be obtained by comparing to a NIST primary standard. Methods: The dimensions and positions of various components including a target within the X‐RAD 320 irradiator were implemented in BEAMnrc. The Monte Carlo simulation was validated by comparing with measurement of x‐ray transmission with a tube potential of 200, 250, and 300 kVp. Using the validated Monte‐Carlo codes, appropriate beam‐hardening filters were investigated to produce the NIST‐traceable beam qualities such as M200, M250, and M300. Results: The simulated transmission of x‐rays with a tube potential of 200, 250, and 300 kVp agreed with the measured ones with a difference of −0.1 ± 2.0%, 1.7 ± 2.1%, and 1.6 ± 2.3%, respectively. The statistical uncertainty in the Monte Carlo simulations was less than 0.5%. The Monte Carlo simulations verified that the NIST‐listed filters for M200 (4.35 mmAl + 1.12 mmCu), M250 (5.25 mmAl + 3.2 mmCu), and M300 (4.25 mmAl + 6.5 mmSn) were adequate for our X‐RAD 320 irradiator. The simulated half‐value layers and homogeneity coefficients using the above filters were 1.64 mmCu and 67.5% for 200 kVp, 3.20 mmCu and 86.1% for 250 kVp, and 5.3 mmCu and 97.1% for 300 kVp, which are in good agreement with the NIST‐traceable beam quality. Conclusions: Monte Carlo simulations of an X‐RAD 320 irradiator were performed to determined beam‐hardening filters for producing the NIST‐traceable beam quality. The NIST‐listed filters were satisfactory for the X‐RAD 320 irradiator to generate the NIST‐traceable beam quality. This research was supported by the Intramural Research Program of the NIH, NCI.

SU‐E‐J‐59: GPU‐Based Interactive Multi‐Volume Visualization in Radiotherapy Treatment Planning

Purpose: To develop a GPU‐based interactive multi‐volume visualization program in radiotherapytreatment planning(RTP) which is able to show the spatial relationships between patient anatomical data and radiationdose distribution. Methods: The radiationdose matrix is extracted from commercial RTP systems such as the Eclipse from Varian Oncology Systems (Palo Alto, CA, USA) and the Hi‐Art from the TomoTherapy Incorporated (Madison, WI, USA), and is then co‐registered with the CT volume such that all three volumes of CT data, dose distribution, and segmented radiotherapy structures share the same geometry, resolution, and position, and show no rotation against each other. A GPU‐based multi‐volume ray casting technique is developed by using NVIDIAs CUDA framework for simultaneously volume renderings of patient anatomy data and radiationdose distribution. The program is executed on an NVIDIA Tesla C1060 computing processor. Each ray emitted from view point is independently processed by a thread on GPU. The ray transverses both volumes and the visual contributions are mixed for every sample points. Results: The program has been tested on braintumor patient data and lungtumor patient data. High quality volume rendering of patient anatomy and dose distribution has been generated interactively. The performance of 8 FPS has been achieved for patient data size of 512×512×188, and view window size of 512×512. Conclusions: A multi‐volume visualization program in RTP has been developed on GPU. The program offers visualization through interactive volume rendering of patient anatomical data and radiationdose distribution. The program can be used to improve the understanding of the spatial relationships between patient anatomical data and radiationdose distribution.