Shading correction algorithm for improvement of cone-beam CT images in radiotherapy

Marchant, T.; Moore, Christopher; Rowbottom, C.; Mackay, Ranald I; Williams, Peter C.

doi:10.1088/0031-9155/53/20/010

Cited by 67 publications

(81 citation statements)

References 21 publications

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“…Marchant et al [17] have also described the histogram matching using the linear scaling. In their method, however, the differences in CBCT values for bone region were larger than soft tissue region because their method was global linear scaling of the CBCT values for the all regions.…”

Section: Discussionmentioning

confidence: 99%

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Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning

Abe¹,

Tateoka²,

Saito³

et al. 2017

IJMPCERO

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Cone-beam CT (CBCT) images acquired during radiation treatment can be used to recalculate the dose distribution as well as to confirm the treatment location. However, it is difficult to obtain the electron densities (EDs) necessary for dose calculation from CBCT images because of the effects of scatter contamination during CBCT image acquisition. This paper presents a mathematical method for converting the pixel values of CBCT images (CBCT values) into Hounsfield units (HUs) of radiation treatment simulation CT (simCT) images for use in radiation treatment planning. CBCT values are converted into HUs by matching the histograms of the CBCT values with the histograms of the HUs for each slice via linear scaling of the CBCT values. For prostate cancer and head-and-neck cancer patients, the EDs obtained from converted CBCT values (mCBCT values) show good agreement with the EDs obtained from HUs, within approximately 3.0%, and the dose calculated on the basis of CBCT images shows good agreement with the dose calculated on the basis of the simCT images, within approximately 2.0%. Because the CBCT values are converted for each slice, this conversion method can account for variation in the CBCT values associated with differences in body size, body shape, and inner tissue structures, as well as in longitudinally displaced positions from the isocenter, unlike conventional methods that use electron density phantoms.

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Section: Discussionmentioning

confidence: 99%

“…in the same patients to the same regions in the CBCT images [14] [15] [16]. Marchant et al [17] created a shading map to correct CBCT values.…”

Section: Introductionmentioning

confidence: 99%

Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning

Abe¹,

Tateoka²,

Saito³

et al. 2017

IJMPCERO

View full text Add to dashboard Cite

show abstract

“…A future version of this PSC method could potentially be improved using a piece‐wise continuous linear calibration curves that calibrates both lower and higher density materials separately. Furthermore, the limited FoV, noise, and artifacts present in CBCTs may pose additional challenges in sites such as the pelvis or thorax 21, 22. Therefore, more sophisticated methods of extending the CBCT field‐of‐view (such as fusion‐aligned reprojection techniques23), and reducing the noise and artifacts present in the CBCT, will be investigated in the future.…”

Section: Discussionmentioning

confidence: 99%

Patient‐specific calibration of cone‐beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment

MacFarlane

Wong

Hoover

et al. 2018

J Applied Clin Med Phys

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PurposeIn this work, we propose a new method of calibrating cone beam computed tomography (CBCT) data sets for radiotherapy dose calculation and plan assessment. The motivation for this patient‐specific calibration (PSC) method is to develop an efficient, robust, and accurate CBCT calibration process that is less susceptible to deformable image registration (DIR) errors.MethodsInstead of mapping the CT numbers voxel‐by‐voxel with traditional DIR calibration methods, the PSC methods generates correlation plots between deformably registered planning CT and CBCT voxel values, for each image slice. A linear calibration curve specific to each slice is then obtained by least‐squares fitting, and applied to the CBCT slice's voxel values. This allows each CBCT slice to be corrected using DIR without altering the patient geometry through regional DIR errors.A retrospective study was performed on 15 head‐and‐neck cancer patients, each having routine CBCTs and a middle‐of‐treatment re‐planning CT (reCT). The original treatment plan was re‐calculated on the patient's reCT image set (serving as the gold standard) as well as the image sets produced by voxel‐to‐voxel DIR, density‐overriding, and the new PSC calibration methods. Dose accuracy of each calibration method was compared to the reference reCT data set using common dose‐volume metrics and 3D gamma analysis. A phantom study was also performed to assess the accuracy of the DIR and PSC CBCT calibration methods compared with planning CT.ResultsCompared with the gold standard using reCT, the average dose metric differences were ≤ 1.1% for all three methods (PSC: −0.3%; DIR: −0.7%; density‐override: −1.1%). The average gamma pass rates with thresholds 3%, 3 mm were also similar among the three techniques (PSC: 95.0%; DIR: 96.1%; density‐override: 94.4%).ConclusionsAn automated patient‐specific calibration method was developed which yielded strong dosimetric agreement with the results obtained using a re‐planning CT for head‐and‐neck patients.

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“…All CBCT images were processed to restore HU values and remove non-uniformity artifacts using an updated and optimized version of a previously reported correction algorithm 12 , referred to here as the "shading correction". The correction uses prior information from a high-quality, fan-beam CT image of the patient acquired prior to treatment for use in RT planning.…”

Section: Shading Correction Algorithmmentioning

confidence: 99%

“…We previously developed a prior-image based correction method to restore HU values and improve uniformity of CBCT images 12 . Here we validate the accuracy with which corrected CBCT can be used for dosimetric assessment of RT delivery, based on a large sample of clinical images at three different anatomical sites.…”

Section: Introductionmentioning

confidence: 99%

Shading correction for cone-beam CT in radiotherapy: validation of dose calculation accuracy using clinical images

Marchant

Joshi

Moore

2017

Medical Imaging 2017: Physics of Medical Imaging

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Cone-beam CT (CBCT) images are routinely acquired to verify patient position in radiotherapy (RT), but are typically not calibrated in Hounsfield Units (HU) and feature non-uniformity due to X-ray scatter and detector persistence effects. This prevents direct use of CBCT for re-calculation of RT delivered dose. We previously developed a prior-image based correction method to restore HU values and improve uniformity of CBCT images. Here we validate the accuracy with which corrected CBCT can be used for dosimetric assessment of RT delivery, using CBCT images and RT plans for 45 patients including pelvis, lung and head sites. Dose distributions were calculated based on each patient's original RT plan and using CBCT image values for tissue heterogeneity correction. Clinically relevant dose metrics were calculated (e.g. median and minimum target dose, maximum organ at risk dose). Accuracy of CBCT based dose metrics was determined using an "override ratio" method where the ratio of the dose metric to that calculated on a bulk-density assigned version of the image is assumed to be constant for each patient, allowing comparison to "gold standard" CT. For pelvis and head images the proportion of dose errors >2% was reduced from 40% to 1.3% after applying shading correction. For lung images the proportion of dose errors >3% was reduced from 66% to 2.2%. Application of shading correction to CBCT images greatly improves their utility for dosimetric assessment of RT delivery, allowing high confidence that CBCT dose calculations are accurate within 2-3%.

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Shading correction algorithm for improvement of cone-beam CT images in radiotherapy

Cited by 67 publications

References 21 publications

Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning

Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning

Patient‐specific calibration of cone‐beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment

Shading correction for cone-beam CT in radiotherapy: validation of dose calculation accuracy using clinical images

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