Aspects of simulator cone-beam CT for radiotherapy treatment planning

2010 ◽  
Vol 9 (3) ◽  
pp. 165-174
Author(s):  
Denise Irvine ◽  
Mark McJury
Keyword(s):  
Image Quality ◽  
Treatment Planning ◽  
Dose Calculation ◽  
Cone Beam ◽  
Modulation Transfer ◽  
Ct Scanner ◽  
Low Contrast ◽  
Treatment Plans ◽  
Ct Data ◽  
Conventional Ct

AbstractBackground and purpose: Following a recent major upgrade in cone-beam computed tomography (CBCT) software and functionality, we have reassessed aspects of our Varian Acuity simulator performance for use in treatment planning. The feasibility of using CBCT for treatment planning has been assessed and here we report specifically on Hounsfield number (HN) accuracy and related dose errors, and digitally reconstructed radiograph (DRR) image quality.Methods: Using a Catphan® 600 CT phantom, HN accuracy and uniformity were investigated for a range of CBCT imaging modes. This included the variation in HNs with scan length and phantom position. Results were compared with those acquired from conventional CT. Treatment plans for three sites were generated using the Rando phantom, and results from CBCT-based data were compared to that from CT-based data using a gamma analysis. Image quality of DRRs based on CBCT data were compared with those from CT data both quantitatively, by calculating the modulation transfer function (MTF) and qualitatively, by counting the number of line pairs visible on a phantom.Results and conclusions: Catphan data showed that for certain cases, the HN calibration of the Acuity CBCT was out of tolerance and could lead to errors in dose calculation of >2%. HNs were only acceptable for scan lengths >10 cm. In multi-scan mode, geometric shifts and differences in HNs were seen on CT slices on either side of the interface between the two acquisitions. However, comparisons between treatment plans calculated using CBCT data and conventional CT data from Rando phantoms showed that head, pelvis and thorax plans were acceptable. CBCT DRR image quality compared favourably with a conventional CT scanner in some respects; however, image uniformity and low contrast resolution were poorer due to the ‘cupping’ artefact obtained with CBCT scans.

scholarly journals Patient-based low dose cone beam CT acquisition settings for prostate image-guided radiotherapy treatments on a Varian TrueBeam linear accelerator

2020 ◽  
Vol 93 (1115) ◽  
pp. 20200412
Author(s):  
Maria Antonietta Piliero ◽  
Margherita Casiraghi ◽  
Davide Giovanni Bosetti ◽  
Simona Cima ◽  
Letizia Deantonio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Quality ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Subjective Evaluation ◽  
Cone Beam ◽  
Low Contrast ◽  
Image Guided Radiotherapy ◽  
Image Guided ◽  
Prostate Cancer Radiotherapy

Objective: To evaluate the performance of low dose cone beam CT (CBCT) acquisition protocols for image-guided radiotherapy of prostate cancer. Methods: CBCT images of patients undergoing prostate cancer radiotherapy were acquired with the settings currently used in our department and two low dose settings at 50% and 63% lower exposure. Four experienced radiation oncologists and two radiation therapy technologists graded the images on five image quality characteristics. The scores were analysed through Visual Grading Regression, using the acquisition settings and the patient size as covariates. Results: The low dose acquisition settings have no impact on the image quality for patients with body profile length at hip level below 100 cm. Conclusions: A reduction of about 60% of the dose is feasible for patients with size below 100 cm. The visibility of low contrast features can be compromised if using the low dose acquisition settings for patients with hip size above 100 cm. Advances in knowledge: Low dose CBCT acquisition protocols for the pelvis, based on subjective evaluation of patient images.

TU-EE-A2-03: Evaluation of Dose Calculation in KV-Cone Beam CT Data Sets

2005 ◽  
Vol 32 (6Part17) ◽  
pp. 2109-2109
Author(s):  
T Tücking ◽  
S Nill ◽  
U Oelfke
Keyword(s):  
Cone Beam Ct ◽  
Dose Calculation ◽  
Cone Beam ◽  
Data Sets ◽  
Ct Data

SU-E-T-613: A Method to Improve Cone Beam CT Image Quality and Hounsfield Units Accuracy for Prostate Proton Treatment Planning

2011 ◽  
Vol 38 (6Part20) ◽  
pp. 3630-3630 ◽  
Author(s):  
R Betancourt ◽  
H Lu ◽  
J McDonough ◽  
Z Tochner ◽  
S Both
Keyword(s):  
Image Quality ◽  
Treatment Planning ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Image ◽  
Hounsfield Units ◽  
Proton Treatment ◽  
Ct Image Quality

scholarly journals Numerical evaluation of image parameters of ETR-1

2016 ◽  
Vol 2 (1) ◽  
pp. 489-491
Author(s):  
Shamim Ahmed ◽  
Marian Krüger ◽  
Christian Willomitzer ◽  
Golam A. Zakaria
Keyword(s):  
Image Quality ◽  
Treatment Planning ◽  
Numerical Evaluation ◽  
Numerical Estimation ◽  
Low Contrast ◽  
Precise Result ◽  
Test Tool ◽  
Naked Eye ◽  
Pre Treatment

AbstractIn this work, we developed a method to handle the image quality test-tool precisely. This test-tool is important to evaluate the quality of the medical images for pre-treatment planning phase. But the achieved images are estimated by naked eyes, which does not provide the precise result. Our main goal is to get the desired image parameters numerically. This numerical estimation overcomes the limitation of naked eye observation. Hence, it enhances the pre-treatment planning. The ETR-1 test-tool is considered here. The contrast, the low contrast details and line-pairs (lp/mm) were estimated.

Technical assessment of a cone-beam CT scanner for otolaryngology imaging: Image quality, dose, and technique protocols

2012 ◽  
Vol 39 (8) ◽  
pp. 4932-4942 ◽  
Author(s):  
J. Xu ◽  
D. D. Reh ◽  
J. P. Carey ◽  
M. Mahesh ◽  
J. H. Siewerdsen
Keyword(s):  
Image Quality ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Scanner ◽  
Technical Assessment

scholarly journals Initial Validation of Proton Dose Calculations on SPR Images from DECT in Treatment Planning System

2020 ◽  
Vol 7 (2) ◽  
pp. 51-61
Author(s):  
Sina Mossahebi ◽  
Pouya Sabouri ◽  
Haijian Chen ◽  
Michelle Mundis ◽  
Matthew O'Neil ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Treatment Planning ◽  
Effective Atomic Number ◽  
Dose Calculation ◽  
Target Volume ◽  
Planning System ◽  
Treatment Planning System ◽  
Relative Electron ◽  
Treatment Plans ◽  
Proton Dose

Abstract Purpose To investigate and quantify the potential benefits associated with the use of stopping-power-ratio (SPR) images created from dual-energy computed tomography (DECT) images for proton dose calculation in a clinical proton treatment planning system (TPS). Materials and Methods The DECT and single-energy computed tomography (SECT) scans obtained for 26 plastic tissue surrogate plugs were placed individually in a tissue-equivalent plastic phantom. Relative-electron density (ρe) and effective atomic number (Zeff) images were reconstructed from the DECT scans and used to create an SPR image set for each plug. Next, the SPR for each plug was measured in a clinical proton beam for comparison of the calculated values in the SPR images. The SPR images and SECTs were then imported into a clinical TPS, and treatment plans were developed consisting of a single field delivering a 10 × 10 × 10-cm3 spread-out Bragg peak to a clinical target volume that contained the plugs. To verify the accuracy of the TPS dose calculated from the SPR images and SECTs, treatment plans were delivered to the phantom containing each plug, and comparisons of point-dose measurements and 2-dimensional γ-analysis were performed. Results For all 26 plugs considered in this study, SPR values for each plug from the SPR images were within 2% agreement with measurements. Additionally, treatment plans developed with the SPR images agreed with the measured point dose to within 2%, whereas a 3% agreement was observed for SECT-based plans. γ-Index pass rates were > 90% for all SECT plans and > 97% for all SPR image–based plans. Conclusion Treatment plans created in a TPS with SPR images obtained from DECT scans are accurate to within guidelines set for validation of clinical treatment plans at our center. The calculated doses from the SPR image–based treatment plans showed better agreement to measured doses than identical plans created with standard SECT scans.

scholarly journals Quantifying the Utility of Low Resolution Brain Imaging:: Treatment of Infant Hydrocephalus

2021 ◽  
Author(s):  
Joshua Harper ◽  
Venkateswararao Cherukuri ◽  
Tom O'Riley ◽  
Mingzhao Yu ◽  
Edith Mbabazi-Kabachelor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Treatment Planning ◽  
Image Resolution ◽  
Learning Enhancement ◽  
Low Contrast ◽  
Substantial Risk ◽  
Noise Ratio ◽  
Hydrocephalus Treatment ◽  
Post Hoc

As low-field MRI technology is being disseminated into clinical settings, it is important to assess the image quality required to properly diagnose and treat a given disease. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. Images were degraded in terms of resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in LMIC for assessment of clinical utility in treatment planning for hydrocephalus. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of a useful image for hydrocephalus treatment planning. For images with 128x128 resolution, a contrast-to-noise ratio of 2.5 has a high probability of being useful (91%, 95% CI 73% to 96%; P=2e-16). Deep learning enhancement of a 128x128 image with very low contrast-to-noise (1.5) and low probability of being useful (23%, 95% CI 14% to 36%; P=2e-16) increases CNR improving the apparent likelihood of being useful, but carries substantial risk of structural errors leading to misleading clinical interpretation (CNR after enhancement = 5; risk of misleading results = 21%, 95% CI 3% to 32%; P=7e-11). Lower quality images not customarily considered acceptable by clinicians can be useful in planning hydrocephalus treatment. We find substantial risk of misleading structural errors when using deep learning enhancement of low quality images. These findings advocate for new standards in assessing acceptable image quality for clinical use.

scholarly journals X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Sorapong Aootaphao ◽  
Saowapak S. Thongvigitmanee ◽  
Jartuwat Rajruangrabin ◽  
Chalinee Thanasupsombat ◽  
Tanapon Srivongsa ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Image Quality ◽  
Soft Tissues ◽  
Scatter Correction ◽  
Cone Beam ◽  
Ct Number ◽  
Low Contrast ◽  
X Ray ◽  
Cupping Artifacts ◽  
The Brain

Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.

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