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Title: SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

Abstract

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images.more » The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from Varian Medical System.« less

Authors:
 [1];  [2]
  1. Washington University in St Louis, Taian, Shandong (China)
  2. Washington University School of Medicine, St Louis, MO (United States)
Publication Date:
OSTI Identifier:
22494044
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 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; ACCURACY; COMPUTERIZED TOMOGRAPHY; DATASETS; ERRORS; IMAGE PROCESSING; IMAGES; PATIENTS; RADIOTHERAPY

Citation Formats

Qiu, J, and Yang, D. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images. United States: N. p., 2015. Web. doi:10.1118/1.4924103.
Qiu, J, & Yang, D. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images. United States. doi:10.1118/1.4924103.
Qiu, J, and Yang, D. Mon . "SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images". United States. doi:10.1118/1.4924103.
@article{osti_22494044,
title = {SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images},
author = {Qiu, J and Yang, D},
abstractNote = {Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from Varian Medical System.},
doi = {10.1118/1.4924103},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: Current Intensity Modulated Radiation Therapy (IMRT) is plan-centered. At each treatment fraction, we position the patient to match the setup in treatment plan. Inaccurate setup can compromise delivered dose distribution, and hence leading to suboptimal treatments. Moreover, current setup approach via couch shift under image guidance can correct translational errors, while rotational and deformation errors are hard to address. To overcome these problems, we propose in this abstract a patient-centered scheme to mitigate impacts of treatment setup errors. Methods: In the patient-centered scheme, we first position the patient on the couch approximately matching the planned-setup. Our Supercomputing Online Replanningmore » Environment (SCORE) is then employed to design an optimal treatment plan based on the daily patient geometry. It hence mitigates the impacts of treatment setup error and reduces the requirements on setup accuracy. We have conducted simulations studies in 10 head-and-neck (HN) patients to investigate the feasibility of this scheme. Rotational and deformation setup errors were simulated. Specifically, 1, 3, 5, 7 degrees of rotations were put on pitch, roll, and yaw directions; deformation errors were simulated by splitting neck movements into four basic types: rotation, lateral bending, flexion and extension. Setup variation ranges are based on observed numbers in previous studies. Dosimetric impacts of our scheme were evaluated on PTVs and OARs in comparison with original plan dose with original geometry and original plan recalculated dose with new setup geometries. Results: With conventional plan-centered approach, setup error could lead to significant PTV D99 decrease (−0.25∼+32.42%) and contralateral-parotid Dmean increase (−35.09∼+42.90%). The patientcentered approach is effective in mitigating such impacts to 0∼+0.20% and −0.03∼+5.01%, respectively. Computation time is <128 s. Conclusion: Patient-centered scheme is proposed to mitigate setup error impacts using replanning. Its superiority in terms of dosimetric impacts and feasibility has been shown through simulation studies on HN cases.« less
  • Purpose: To analyze the effect of computing radiation dose on automatically generated MR-based simulated CT images compared to true patient CTs. Methods: Six prostate cancer patients received a regular planning CT for RT planning as well as a conventional 3D fast-field dual-echo scan on a Philips 3.0T Achieva, adding approximately 2 min of scan time to the clinical protocol. Simulated CTs (simCT) where synthesized by assigning known average CT values to the tissue classes air, water, fat, cortical and cancellous bone. For this, Dixon reconstruction of the nearly out-of-phase (echo 1) and in-phase images (echo 2) allowed for water andmore » fat classification. Model based bone segmentation was performed on a combination of the DIXON images. A subsequent automatic threshold divides into cortical and cancellous bone. For validation, the simCT was registered to the true CT and clinical treatment plans were re-computed on the simCT in pinnacle{sup 3}. To differentiate effects related to the 5 tissue classes and changes in the patient anatomy not compensated by rigid registration, we also calculate the dose on a stratified CT, where HU values are sorted in to the same 5 tissue classes as the simCT. Results: Dose and volume parameters on PTV and risk organs as used for the clinical approval were compared. All deviations are below 1.1%, except the anal sphincter mean dose, which is at most 2.2%, but well below clinical acceptance threshold. Average deviations are below 0.4% for PTV and risk organs and 1.3% for the anal sphincter. The deviations of the stratifiedCT are in the same range as for the simCT. All plans would have passed clinical acceptance thresholds on the simulated CT images. Conclusion: This study demonstrated the clinical usability of MR based dose calculation with the presented Dixon acquisition and subsequent fully automatic image processing. N. Schadewaldt, H. Schulz, M. Helle and S. Renisch are employed by Phlips Technologie Innovative Techonologies, a subsidiary of Royal Philips NV.« less
  • 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,more » 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.« less
  • 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: In room kilovoltage x-ray (kV) imaging provides higher contrast than Megavoltage (MV) imaging with faster acquisition time compared with on-board cone-beam computed tomography (CBCT), thus improving patient setup accuracy and efficiency. In this study we evaluated the clinical feasibility of utilizing kV imaging for whole breast radiation patient setup. Methods: For six breast cancer patients with whole breast treatment plans using two opposed tangential fields, MV-based patient setup was conducted by aligning patient markers with in room lasers and MV portal images. Beam-eye viewed kV images were acquired using Varian OBI system after the set up process. In housemore » software was developed to transfer MLC blocks information overlaying onto kV images to demonstrate the field shape for verification. KV-based patient digital shift was derived by performing rigid registration between kV image and the digitally reconstructed radiography (DRR) to align the bony structure. This digital shift between kV-based and MV-based setup was defined as setup deviation. Results: Six sets of kV images were acquired for breast patients. The mean setup deviation was 2.3mm, 2.2mm and 1.8mm for anterior-posterior, superior-inferior and left-right direction respectively. The average setup deviation magnitude was 4.3±1.7mm for six patients. Patient with large breast had a larger setup deviation (4.4–6.2mm). There was no strong correlation between MV-based shift and setup deviation. Conclusion: A preliminary clinical workflow for kV-based whole breast radiation setup was established and tested. We observed setup deviation of the magnitude below than 5mm. With the benefit of providing higher contrast and MLC block overlaid on the images for treatment field verification, it is feasible to use kV imaging for breast patient setup.« less