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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. Wed . "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 = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • 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: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.« less
  • Purpose: The aim of this study is to evaluate the feasibility of using a dual-energy CBCT (DECBCT) in proton therapy treatment planning to allow for accurate electron density estimation. Methods: For direct comparison, two scenarios were selected: a dual-energy fan-beam CT (high: 140 kVp, low: 80 kVp) and a DECBCT (high: 125 kVp, low: 80 kVp). A Gammex 467 tissue characterization phantom was used, including the rods of air, water, bone (B2–30% mineral), cortical bone (SB3), lung (LN-300), brain, liver and adipose. For the CBCT, Hounsfield Unit (HU) numbers were first obtained from the reconstructed images after a calibration wasmore » made based on water (=0) and air materials (=−1000). For each tissue surrogate, region-of-interest (ROI) analyses were made to derive high-energy and low-energy HU values (HUhigh and HUlow), which were subsequently used to estimate electron density based on the algorithm as previously described by Hunemohr N., et al. Parameters k1 and k2 are energy dependent and can be derived from calibration materials. Results: While for the dual-energy FBCT, the electron density is found be within +/−3% error relative to the values provided by the phantom vendor: −1.8% (water), 0.03% (lung), 1.1% (brain), −2.82% (adipose), −0.49% (liver) and −1.89% (cortical bones). While for the DECBCT, the estimation of electron density exhibits a relatively larger variation: −1.76% (water), −36.7% (lung), −1.92% (brain), −3.43% (adipose), 8.1% (liver) and 9.5% (cortical bones). Conclusion: For DECBCT, the accuracy of electron density estimation is inferior to that of a FBCT, especially for materials of either low-density (lung) or high density (cortical bone) compared to water. Such limitation arises from inaccurate HU number derivation in a CBCT. Advanced scatter-correction and HU calibration routines, as well as the deployment of photon counting CT detectors need be investigated to minimize the difference between FBCT and CBCT.« less
  • Purpose: To compute daily dose delivered during radiotherapy, deformable registration needs to be relatively fast, automated, and accurate. The aim of this study was to evaluate the performance of commercial deformable registration software for deforming between two modalities: planning computed tomography (pCT) images acquired for treatment planning and cone beam (CB) CT images acquired prior to each fraction of prostate cancer radiotherapy. Methods: A workflow was designed using MIM Software™ that aligned and deformed pCT into daily CBCT images in two steps: (1) rigid shifts applied after daily CBCT imaging to align patient anatomy to the pCT and (2) normalizedmore » intensity-based deformable registration to account for interfractional anatomical variations. The physician-approved CTV and organ and risk (OAR) contours were deformed from the pCT to daily CBCT over the course of treatment. The same structures were delineated on each daily CBCT by a radiation oncologist. Dice similarity coefficient (DSC) mean and standard deviations were calculated to quantify the deformable registration quality for prostate, bladder, rectum and femoral heads. Results: To date, contour comparisons have been analyzed for 31 daily fractions of 2 of 10 of the cohort. Interim analysis shows that right and left femoral head contours demonstrate the highest agreement (DSC: 0.96±0.02) with physician contours. Additionally, deformed bladder (DSC: 0.81±0.09) and prostate (DSC: 0.80±0.07) have good agreement with physician-defined daily contours. Rectum contours have the highest variations (DSC: 0.66±0.10) between the deformed and physician-defined contours on daily CBCT imaging. Conclusion: For structures with relatively high contrast boundaries on CBCT, the MIM automated deformable registration provided accurate representations of the daily contours during treatment delivery. These findings will permit subsequent investigations to automate daily dose computation from CBCT. However, improved methods need to be investigated to improve deformable results for rectum contours.« less
  • Purpose: Dual energy (DE) CT can be used to characterize tissue composition. One application of DE CT is to measure electron density (ED, rho) and atomic number (Z) for use in radiation therapy treatment planning. This work evaluated the accuracy and stability of ED estimation as patient size varied for both single-energy (SE) and DE CT. Methods: An ED phantom (CIRS) and four torso-shaped water tanks (lateral widths 15, 25, 35 and 45 cm) containing 8 tissue-simulating cylinders of known ED were scanned on a dual-source CT system (Siemens Somatom Force) in SE (120 kV) and DE (90/150Sn) modes. Additionalmore » scans were performed on the 15 and 25 cm water tanks using DE techniques of 70/150Sn and 80/150Sn, respectively. CTDIvol was matched for all SE and DE scans for a given phantom size. Images were reconstructed using quantitative kernels to preserve CT number accuracy. ED was estimated in each test cylinder and in solid and liquid water using calibration measurements acquired in the CIRS phantom (SE) and a Rho-Z algorithm (DE). Results: ED estimates showed good agreement with the nominal ED values when using Rho-Z (slope = 1.0051, R2 = 0.9982). Mean percent error was similar between SE (1.21%) and DE (1.28%). Mean deviation across patient size decreased 34% (1.43% with SE, 0.95% with DE). When compared to 90/150Sn, DE techniques of 70/150Sn and 80/150Sn showed mean differences in ED of 0.43% and 0.15%, respectively. Conclusion: While both DE Rho-Z and SE CT number calibration methods are both accurate for estimating ED, Rho-Z offers the advantages of having less variability across patient size, not requiring a phantom calibration, and being able to distinguish between materials of similar attenuation, but different chemical composition. Low kV DE pairs are an option in small patients due to lack of effect on ED accuracy. This research was supported by Siemens Healthcare.« less