Use of TrueBeam developer mode for imaging QA

Valdés, Gilmer; Morin, Olivier; Valenciaga, Y.; Kirby, Niel; Pouliot, Jean; Chuang, Cynthia

doi:10.1120/jacmp.v16i4.5363

Cited by 38 publications

(34 citation statements)

References 13 publications

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“…Similarly, Valdes et al. used texture features to identify image quality problems during QA of linac‐based cone beam computed tomography systems …”

Section: Discussionmentioning

confidence: 99%

“…13 Similarly, Valdes et al used texture features to identify image quality problems during QA of linac-based cone beam computed tomography systems. 14 Recently, the first reports of deep learning for radiotherapy QA have appeared as Interian et al applied deep learning to simulated radiation fluence maps in order to predict gamma pass rates using a 2D diode array. 15 There are several distinctions between that work and the results presented here.…”

Section: Discussionmentioning

confidence: 99%

“…While most of the work in radiomics has been in the field of radiology, radiation oncology is rich in quantitative spatial information, including initial diagnostic imaging and patient simulation, treatment planning systems with detailed image annotation and volumetric dose maps, and sophisticated onboard systems capable of verification imaging in a variety of ways. Most reports to date have used image features such as the so‐called texture features, which consist of features derived from intensity histograms, co‐occurrence matrices, neighborhood differences matrices, zone size matrices, and other prior applications in the literature The first reports of applying radiomics to radiotherapy QA have begun to appear, including analysis of cone‐beam CT images to detect image quality changes and analysis of patient‐specific QA images to predict simulated radiotherapy errors and correlate to gamma pass rates …”

Section: Introductionmentioning

confidence: 99%

“…Most reports to date have used image features such as the socalled texture features, which consist of features derived from intensity histograms, co-occurrence matrices, neighborhood differences matrices, zone size matrices, and other prior applications in the literature [10][11][12] The first reports of applying radiomics to radiotherapy QA have begun to appear, including analysis of cone-beam CT images to detect image quality changes and analysis of patient-specific QA images to predict simulated radiotherapy errors and correlate to gamma pass rates. [13][14][15] At the same time, the groundbreaking performance of convolutional neural networks in computer vision such as ImageNet 16 has led to a rapid ascent of interest in the application of deep learning to medical imaging. 17,18 In cases where the sample size is small, deep learning can be used for representation learning.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

et al. 2018

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Purpose Patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific QA. Methods Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to (a) the error‐free case, (b) a random multileaf collimator (MLC) error case, and (c) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID), and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k‐nearest‐neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two‐class experiment classified images as error‐free or containing any MLC error, and the three‐class experiment classified images as error‐free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold‐based passing criteria were calculated for comparison. Results In total, 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two‐class experiment and 64.3% in the three‐class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two‐class experiment and 53.7% in the three‐class experiment. Variability between the results of the four machine learning classifiers was lower for the deep learning approach vs the texture feature approach. Both radiomic approaches were superior to threshold‐based passing criteria. Conclusions Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold‐based passing criteria. The results suggest that radiomic QA is a promising direction f...

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“…Similarly, Valdes et al. used texture features to identify image quality problems during QA of linac‐based cone beam computed tomography systems …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

et al. 2018

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show abstract

“…Du et al proposed a simple, accurate Winston‐Lutz type measurement to check the coincidence of imaging center with radiation isocenter. More recently, Valdes et al developed an automated QA approach with XML script to test various components of the OBI‐EPID system. These proposed approaches showed promising results in achieving automated and accurate QA measurements through exclusively use of the flat panel imager while eliminating the need for third‐party measurement devices.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

et al. 2018

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IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

J Applied Clin Med Phys

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127

138

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PurposeTo validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

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Use of TrueBeam developer mode for imaging QA

Cited by 38 publications

References 13 publications

Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

IMRT QA using machine learning: A multi‐institutional validation

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