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Title: SU-F-J-114: On-Treatment Imagereconstruction Using Transit Images of Treatment Beams Through Patient and Thosethrough Planning CT Images

Abstract

Purpose: To reconstruct patient images at the time of radiation delivery using measured transit images of treatment beams through patient and calculated transit images through planning CT images. Methods: We hypothesize that the ratio of the measured transit images to the calculated images may provide changed amounts of the patient image between times of planning CT and treatment. To test, we have devised lung phantoms with a tumor object (3-cm diameter) placed at iso-center (simulating planning CT) and off-center by 1 cm (simulating treatment). CT images of the two phantoms were acquired; the image of the off-centered phantom, unavailable clinically, represents the reference on-treatment image in the image quality of planning CT. Cine-transit images through the two phantoms were also acquired in EPID from a non-modulated 6 MV beam when the gantry was rotated 360 degrees; the image through the centered phantom simulates calculated image. While the current study is a feasibility study, in reality our computational EPID model can be applicable in providing accurate transit image from MC simulation. Changed MV HU values were reconstructed from the ratio between two EPID projection data, converted to KV HU values, and added to the planning CT, thereby reconstructing the on-treatment imagemore » of the patient limited to the irradiated region of the phantom. Results: The reconstructed image was compared with the reference image. Except for local HU differences>200 as a maximum, excellent agreement was found. The average difference across the entire image was 16.2 HU. Conclusion: We have demonstrated the feasibility of a method of reconstructing on-treatment images of a patient using EPID image and planning CT images. Further studies will include resolving the local HU differences and investigation on the dosimetry impact of the reconstructed image.« less

Authors:
;  [1];  [2];  [3];  [4];  [5];  [6]
  1. KAIST, Yuseong-gu, Daejeon (Korea, Republic of)
  2. Hallym University Sacred Heart Hospital, Anyang (Korea, Republic of)
  3. East Carolina University Greenville, NC (United States)
  4. Samsung Medical Cener, Gangnam-gu, Seoul (Korea, Republic of)
  5. Yonsei Cancer Center, Seoul (Korea, Republic of)
  6. Loma Linda University Medical Center, Loma Linda, CA (United States)
Publication Date:
OSTI Identifier:
22634721
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; COMPUTERIZED TOMOGRAPHY; FEASIBILITY STUDIES; IMAGE PROCESSING; IMAGES; LUNGS; NEOPLASMS; PATIENTS; PHANTOMS; PLANNING

Citation Formats

Lee, H, Cho, S, Cheong, K, Jung, J, Jung, S, Kim, J, and Yeo, I. SU-F-J-114: On-Treatment Imagereconstruction Using Transit Images of Treatment Beams Through Patient and Thosethrough Planning CT Images. United States: N. p., 2016. Web. doi:10.1118/1.4956022.
Lee, H, Cho, S, Cheong, K, Jung, J, Jung, S, Kim, J, & Yeo, I. SU-F-J-114: On-Treatment Imagereconstruction Using Transit Images of Treatment Beams Through Patient and Thosethrough Planning CT Images. United States. doi:10.1118/1.4956022.
Lee, H, Cho, S, Cheong, K, Jung, J, Jung, S, Kim, J, and Yeo, I. 2016. "SU-F-J-114: On-Treatment Imagereconstruction Using Transit Images of Treatment Beams Through Patient and Thosethrough Planning CT Images". United States. doi:10.1118/1.4956022.
@article{osti_22634721,
title = {SU-F-J-114: On-Treatment Imagereconstruction Using Transit Images of Treatment Beams Through Patient and Thosethrough Planning CT Images},
author = {Lee, H and Cho, S and Cheong, K and Jung, J and Jung, S and Kim, J and Yeo, I},
abstractNote = {Purpose: To reconstruct patient images at the time of radiation delivery using measured transit images of treatment beams through patient and calculated transit images through planning CT images. Methods: We hypothesize that the ratio of the measured transit images to the calculated images may provide changed amounts of the patient image between times of planning CT and treatment. To test, we have devised lung phantoms with a tumor object (3-cm diameter) placed at iso-center (simulating planning CT) and off-center by 1 cm (simulating treatment). CT images of the two phantoms were acquired; the image of the off-centered phantom, unavailable clinically, represents the reference on-treatment image in the image quality of planning CT. Cine-transit images through the two phantoms were also acquired in EPID from a non-modulated 6 MV beam when the gantry was rotated 360 degrees; the image through the centered phantom simulates calculated image. While the current study is a feasibility study, in reality our computational EPID model can be applicable in providing accurate transit image from MC simulation. Changed MV HU values were reconstructed from the ratio between two EPID projection data, converted to KV HU values, and added to the planning CT, thereby reconstructing the on-treatment image of the patient limited to the irradiated region of the phantom. Results: The reconstructed image was compared with the reference image. Except for local HU differences>200 as a maximum, excellent agreement was found. The average difference across the entire image was 16.2 HU. Conclusion: We have demonstrated the feasibility of a method of reconstructing on-treatment images of a patient using EPID image and planning CT images. Further studies will include resolving the local HU differences and investigation on the dosimetry impact of the reconstructed image.},
doi = {10.1118/1.4956022},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was usedmore » to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.« less
  • Purpose: This research investigates the use of Mult-ileaf Collimator (MLC) dynalog files to modify a Volumetric Arc Therapy (VMAT) DICOM Radiotherapy Treatment file from the Treatment Planning System (TPS) for quality assurance and treatment plan verification. Methods: Actual MLC positions and gantry angles where retrieved from the MLC Dynalog files of an approved and treated VMAT plan. The treatment machine used was a Novalis TX linac equipped with high definition MLC. The DICOM RT file of the plan was exported from the TPS (Eclipse, Varian Medical Systems) and the actual MLC leaf positions and gantry angles were inserted in placemore » of the planned positions for each control point. The modified DICOM RT file was then imported back into the TPS where dose calculations were performed. The resulting dose distributions were then exported to VeriSoft (PTW) where a 3D gamma was calculated using 3mm-3% and 2mm-2% criteria. A 2D gamma was also calculated using dose measurements on the Delta4 (Sandidose) phantom. Results: A 3D gamma was calculated in Verisoft at 3mm-3% of 99.5% and at 2mm-2% of 99.2%. The pretreatment verification on the Delta4 yielded a 2D gamma at 3mm-3% of 97.9% and at 2mm-2% of 88.5%. The dose volume histograms of the approved plan and the dynalog plan are virtually identical. Conclusion: Initial results show good agreement of the dynalog dose distribution with the approved plan. Future work on this research will aim to increase the number of patients and replace the planned fractionated dose per control point with the actual fractionated dose.« less
  • Purpose: To assess dose calculation accuracy of cone-beam CT (CBCT) based treatment plans using a patient-specific stepwise CT-density conversion table in comparison to conventional CT-based treatment plans. Methods: Unlike CT-based treatment planning which use fixed CT-density table, this study used patient-specific CT-density table to minimize the errors in reconstructed mass densities due to the effects of CBCT Hounsfield unit (HU) uncertainties. The patient-specific CT-density table was a stepwise function which maps HUs to only 6 classes of materials with different mass densities: air (0.00121g/cm3), lung (0.26g/cm3), adipose (0.95g/cm3), tissue (1.05 g/cm3), cartilage/bone (1.6g/cm3), and other (3g/cm3). HU thresholds to definemore » different materials were adjusted for each CBCT via best match with the known tissue types in these images. Dose distributions were compared between CT-based plans and CBCT-based plans (IMRT/VMAT) for four types of treatment sites: head and neck (HN), lung, pancreas, and pelvis. For dosimetric comparison, PTV mean dose in both plans were compared. A gamma analysis was also performed to directly compare dosimetry in the two plans. Results: Compared to CT-based plans, the differences for PTV mean dose were 0.1% for pelvis, 1.1% for pancreas, 1.8% for lung, and −2.5% for HN in CBCT-based plans. The gamma passing rate was 99.8% for pelvis, 99.6% for pancreas, and 99.3% for lung with 3%/3mm criteria, and 80.5% for head and neck with 5%/3mm criteria. Different dosimetry accuracy level was observed: 1% for pelvis, 3% for lung and pancreas, and 5% for head and neck. Conclusion: By converting CBCT data to 6 classes of materials for dose calculation, 3% of dose calculation accuracy can be achieved for anatomical sites studied here, except HN which had a 5% accuracy. CBCT-based treatment planning using a patient-specific stepwise CT-density table can facilitate the evaluation of dosimetry changes resulting from variation in patient anatomy.« less
  • Purpose: To verify the accuracy of total body irradiation (TBI) measurement commissioning data using the treatment planning system (TPS) for a wide range of patient separations. Methods: Our institution conducts TBI treatments with an 18MV photon beam at 380cm extended SSD using an AP/PA technique. Currently, the monitor units (MU) per field for patient treatments are determined using a lookup table generated from TMR measurements in a water phantom (75 × 41 × 30.5 cm3). The dose prescribed to an umbilicus midline point at spine level is determined based on patient separation, dose/ field and dose rate/MU. One-dimensional heterogeneous dosemore » calculations from Pinnacle TPS were validated with thermoluminescent dosimeters (TLD) placed in an average adult anthropomorphic phantom and also in-vivo on four patients with large separations. Subsequently, twelve patients with various separations (17–47cm) were retrospectively analyzed. Computed tomography (CT) scans were acquired in the left and right decubitus positions from vertex to knee. A treatment plan for each patient was generated. The ratio of the lookup table MU to the heterogeneous TPS MU was compared. Results: TLD Measurements in the anthropomorphic phantom and large TBI patients agreed with Pinnacle calculated dose within 2.8% and 2%, respectively. The heterogeneous calculation compared to the lookup table agreed within 8.1% (ratio range: 1.014–1.081). A trend of reduced accuracy was observed when patient separation increases. Conclusion: The TPS dose calculation accuracy was confirmed by TLD measurements, showing that Pinnacle can model the extended SSD dose without commissioning a special beam model for the extended SSD geometry. The difference between the lookup table and TPS calculation potentially comes from lack of scatter during commissioning when compared to extreme patient sizes. The observed trend suggests the need for development of a correction factor between the lookup table and TPS dose calculations.« less
  • Purpose: To evaluate the metal artifacts in kilovoltage computed tomography (kVCT) images that are corrected using a normalized metal artifact reduction (NMAR) method with megavoltage CT (MVCT) prior images.Methods: Tissue characterization phantoms containing bilateral steel inserts are used in all experiments. Two MVCT images, one without any metal artifact corrections and the other corrected using a modified iterative maximum likelihood polychromatic algorithm for CT (IMPACT) are translated to pseudo-kVCT images. These are then used as prior images without tissue classification in an NMAR technique for correcting the experimental kVCT image. The IMPACT method in MVCT included an additional model formore » the pair/triplet production process and the energy dependent response of the MVCT detectors. An experimental kVCT image, without the metal inserts and reconstructed using the filtered back projection (FBP) method, is artificially patched with the known steel inserts to get a reference image. The regular NMAR image containing the steel inserts that uses tissue classified kVCT prior and the NMAR images reconstructed using MVCT priors are compared with the reference image for metal artifact reduction. The Eclipse treatment planning system is used to calculate radiotherapy dose distributions on the corrected images and on the reference image using the Anisotropic Analytical Algorithm with 6 MV parallel opposed 5 × 10 cm{sup 2} fields passing through the bilateral steel inserts, and the results are compared. Gafchromic film is used to measure the actual dose delivered in a plane perpendicular to the beams at the isocenter.Results: The streaking and shading in the NMAR image using tissue classifications are significantly reduced. However, the structures, including metal, are deformed. Some uniform regions appear to have eroded from one side. There is a large variation of attenuation values inside the metal inserts. Similar results are seen in commercially corrected image. Use of MVCT prior images without tissue classification in NMAR significantly reduces these problems. The radiation dose calculated on the reference image is close to the dose measured using the film. Compared to the reference image, the calculated dose difference in the conventional NMAR image, the corrected images using uncorrected MVCT image, and IMPACT corrected MVCT image as priors is ∼15.5%, ∼5%, and ∼2.7%, respectively, at the isocenter.Conclusions: The deformation and erosion of the structures present in regular NMAR corrected images can be largely reduced by using MVCT priors without tissue segmentation. The attenuation value of metal being incorrect, large dose differences relative to the true value can result when using the conventional NMAR image. This difference can be significantly reduced if MVCT images are used as priors. Reduced tissue deformation, better tissue visualization, and correct information about the electron density of the tissues and metals in the artifact corrected images could help delineate the structures better, as well as calculate radiation dose more correctly, thus enhancing the quality of the radiotherapy treatment planning.« less