Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection

Peng, Jiayuan; Shi, Chengyu; Laugeman, Eric; Hu, Weigang; Zhang, Zhen; Mutic, Sasa; Cai, Bin

doi:10.1002/mp.14010

Cited by 43 publications

(32 citation statements)

References 22 publications

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“…4(b) can be treated as an IMPT plan. The pattern can be further analyzed by using tighter (1%/1 mm with 10% threshold and 90% passing rate) gamma index 22 parameter or Structural SIMilarity (SSIM) index 23 . It will be easy for a program such as the python‐web application to do so and automatically email the result to the medical physicist group as records.…”

Section: Discussionmentioning

confidence: 99%

Auto‐Trending daily quality assurance program for a pencil beam scanning proton system aligned with TG 224

Shi

Chen

et al. 2020

J Applied Clin Med Phys

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The Daily Quality Assurance (DQA) for a proton modality is not standardized. The modern pencil beam scanning proton system is becoming a trend and an increasing number of proton centers with PBS are either under construction or in planning. The American Association of Physicists in Medicine has a Task Group 224 report published in 2019 for proton modality routine QA. Therefore, there is a clinical need to explore a DQA procedure to meet the TG 224 guideline. The MatriXX PT and a customized phantom were used for the dosimetry constancy checking. An OBI box was used for imaging QA. The MyQA(TM) software was used for logging the dosimetry results. An in‐house developed application was applied to log and auto analyze the DQA results. Another in‐house developed program "DailyQATrend" was used to create DQA databases for further analysis. All the functional and easy determined tasks passed. For dosimetry constancy checking, the outputs for four gantry rooms were within ±3% with room to room baseline differences within ±1%. The energy checking was within ±1%. The spot location checking from the baseline was within 0.63 mm and the spot size checking from the baseline was within −1.41 ± 1.27 mm (left–right) and −0.24 ± 1.27 mm (in–out) by averaging all the energies. We have found that there was also a trend for the beam energies of two treatment rooms slowly going down (0.76% per month and 0.48 per month) after analyzing the whole data trend with linear regression. A DQA program for a PBS proton system has been developed and fully implemented into the clinic. The DQA program meets the TG 224 guideline and has web‐based logging and auto treading functions. The clinical data show the DQA program is efficient and has the potential to identify the PBS proton system potential issue.

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Section: Discussionmentioning

confidence: 99%

Auto‐Trending daily quality assurance program for a pencil beam scanning proton system aligned with TG 224

Shi

Chen

et al. 2020

J Applied Clin Med Phys

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“…The rationale to introduce an additional metric of a Gaussian-transformed DD was due to the reported insensitivity of the SSIM sub-indices to small absolute luminance changes. 13,14 Thus, four difference maps were obtained for each measured PD image.…”

Section: Methodsmentioning

confidence: 99%

“…One weakness of the SSIM analysis is that the luminance index is relatively insensitive to small luminance differences between two images 13,14 . Therefore, the only use of SSIM analysis would not be sufficient in detecting small absolute CU changes in the measured PD images.…”

Section: Methodsmentioning

confidence: 99%

“…One weakness of the SSIM analysis is that the luminance index is relatively insensitive to small luminance differences between two images. 13,14 Therefore, the only use of SSIM analysis would not be sufficient in detecting small absolute CU changes in the measured PD images. To mitigate this insensitivity, the pixelwise relative difference between the measured and predicted PD images was calculated as well, acting as a straightforward measure of the absolute CU changes.…”

Section: C2 Dose Difference Analysismentioning

confidence: 99%

“…13 The SSIM index was developed to measure the similarity between two images, modeling any discrepancy as a combination of loss in correlation, luminance difference, and contrast difference. The capability of the SSIM analysis to reveal different types of errors between two dose distributions has been demonstrated by Peng et al 14 The sub-indices of SSIM, luminance, contrast, and structure, were indicated capable of detecting absolute dose error, gradient discrepancy, and dose structure error, respectively. Most importantly, the SSIM sub-index maps could not only indicate the location of large discrepancies, but also demonstrate different types of error-related patterns, which make the SSIM analysis a potential tool for error identification in IMRT QA.…”

Section: Introductionmentioning

confidence: 99%

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The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Wang

Zhou

et al. 2020

Medical Physics

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Purpose The implementation of radiomics and machine learning (ML) techniques on analyzing two‐dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub‐index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub‐index maps and develop ML models to classify delivery errors in patient‐specific dynamic IMRT QA. Methods Twenty‐one sliding‐window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine‐related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same‐directional and opposite‐directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub‐index maps, and one dose difference‐derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross‐validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error‐free and the erroneous cases was compared to that of the conventional gamma analysis. Results The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear‐SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error‐free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. Conclusions We proposed an ML‐based method for machine‐related error identification in patient‐specific dynamic IMRT QA, where radiomic analysis on SSIM sub‐index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error...

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Assessing quality assurance of multi‐leaf collimator using the structural similarity index

Zhang,

Lasio

et al. 2024

J Applied Clin Med Phys

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PurposeThis study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi‐leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12‐week period of MLC QA in our clinic.MethodTo assess sensitivity to MLC positioning errors, we designed a 1 cm slit on the reference MLC, subsequently shifted by 0.5–5 mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25 cm × 25 cm open fields on the portal image with varying Monitor Units (MUs) of 96–100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25 mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored.ResultsThe Gamma index test started detecting the MLC shift at a threshold of 4 mm, whereas the SSI* metric showed sensitivity to shifts as small as 2 mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1 mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam.ConclusionsOur findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

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Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection

Cited by 43 publications

References 22 publications

Auto‐Trending daily quality assurance program for a pencil beam scanning proton system aligned with TG 224

Auto‐Trending daily quality assurance program for a pencil beam scanning proton system aligned with TG 224

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Assessing quality assurance of multi‐leaf collimator using the structural similarity index

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