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The IAEA remote and automated quality control methodology for radiography and mammography.

ABSTRACT: Radiography remains the most widely used imaging modality throughout the world. Additionally, while it has been demonstrated that a quality control (QC) program, especially in mammography, improves image quality, weekly technologist QC testing might be lacking even where there is clinical qualified medical physicist (CQMP) support. Therefore, the International Atomic Energy Agency (IAEA) developed simple QC phantoms that can easily be used on a regular basis (daily/weekly) for radiography and mammography. These are simple in design and use materials that are easily accessible in most parts of the world. A software application is also developed that automatically analyzes images and Digital Imaging and Communications in Medicine (DICOM) header information. It exports data to a comma-separated values (CSV) file that is read by a Microsoft Excel® spreadsheet for documentation and graphical analysis. The phantom and the software were tested in four institutions (in Costa Rica and the United States of America) both on computed radiography and direct digital mammography and radiography systems. Data were collected over a 3-year period. No corrective actions were taken on the data, but service was performed on two of the units. Results demonstrated noise that could be attributed to suboptimal placement of the phantom and incorrect data being put into the DICOM header. Preliminary evaluation of the IAEA methodology has demonstrated that it can provide meaningful QC data that are sensitive to changes in the imaging systems. Care must be taken at implementation to properly train personnel and ensure that the image data, including the DICOM header, are being correctly transmitted. The methodology gives the opportunity for a single CQMP to provide QC services even to remote sites where travel is prohibitive, and it is feasible and easy to implement.

SUBMITTER: Mora P

PROVIDER: S-EPMC8598138 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies.A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive (99m)Tc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers.The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 +/- 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 +/- 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 +/- 0.01; sensitivity, 100%; and specificity, 70%.A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.

Project description:BackgroundAutomatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC).ResultsWe compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively.ConclusionsThe proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process incorporates more multi-scale spatial context, and captures more local and global context to predict a precise pixel-wise segmentation map of an input full MG image. These detected maps can help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. We show that using transfer learning, introducing augmentation, and modifying the architecture of the original model results in better performance in terms of the mean accuracy, the mean DI, and the mean IOU in detecting mass lesion compared to the other DL and the conventional models.

Project description:UnlabelledThe International Atomic Energy Agency (IAEA) has a long tradition of supporting development of methodologies for national networks providing quality audits in radiotherapy. A series of co-ordinated research projects (CRPs) has been conducted by the IAEA since 1995 assisting national external audit groups developing national audit programs. The CRP 'Development of Quality Audits for Radiotherapy Dosimetry for Complex Treatment Techniques' was conducted in 2009-2012 as an extension of previously developed audit programs.Material and methodsThe CRP work described in this paper focused on developing and testing two steps of dosimetry audit: verification of heterogeneity corrections, and treatment planning system (TPS) modeling of small MLC fields, which are important for the initial stages of complex radiation treatments, such as IMRT. The project involved development of a new solid slab phantom with heterogeneities containing special measurement inserts for thermoluminescent dosimeters (TLD) and radiochromic films. The phantom and the audit methodology has been developed at the IAEA and tested in multi-center studies involving the CRP participants.ResultsThe results of multi-center testing of methodology for two steps of dosimetry audit show that the design of audit procedures is adequate and the methodology is feasible for meeting the audit objectives. A total of 97% TLD results in heterogeneity situations obtained in the study were within 3% and all results within 5% agreement with the TPS predicted doses. In contrast, only 64% small beam profiles were within 3?mm agreement between the TPS calculated and film measured doses. Film dosimetry results have highlighted some limitations in TPS modeling of small beam profiles in the direction of MLC leave movements.DiscussionThrough multi-center testing, any challenges or difficulties in the proposed audit methodology were identified, and the methodology improved. Using the experience of these studies, the participants could incorporate the auditing procedures in their national programs.

Project description:PurposeAdaptive radiation therapy (ART) on the integrated Elekta Unity magnetic resonance (MR)-linac requires routine quality assurance to verify delivery accuracy and system data transfer. In this work, our objective was to develop and validate a novel automated end-to-end test suite that verifies data transfer between multiple software platforms and quantifies the performance of multiple machine subcomponents critical to the ART process.MethodsWe designed and implemented a software tool to quantify the MR and megavoltage (MV) isocenter coincidence, treatment couch positioning consistency, isocenter shift accuracy for the adapted plan as well as the MLC and jaw position accuracy following the beam aperture adaptation. Our tool employs a reference treatment plan with a simulated isocenter shift generated on an MR image of a readily available phantom with MR and MV visible fiducials. Execution of the test occurs within the standard adapt-to-position (ATP) clinical workflow with MV images collected of the delivered treatment fields. Using descriptive statistics, we quantified uncertainty in couch positioning, isocentre shift as well as the jaw and MLC positions of the adapted fields. We also executed sensitivity measurements to evaluate the detection algorithm's performance.ResultsWe report the results of 301 daily testing instances. We demonstrated consistent tracking of the MR-to-MV alignment with respect to the established value and to detect small changes on the order of 0.2 mm following machine service events. We found couch position consistency relative to the test baseline value was within 95% CI [-0.31, 0.26 mm]. For phantom shifts that form the basis for the plan adaptation, we found agreement between MV-image-detected phantom shift and online image registration, within ± 1.5 mm in all directions with a 95% CI difference of [-1.29, 0.79 mm]. For beam aperture adaptation accuracy, we found differences between the planned and detected jaw positions had a mean value of 0.27 mm and 95% CI of [-0.29, 0.82 mm] and -0.17 mm and 95% CI of [-0.37, 0.05 mm] for the MLC positions. Automated fiducial detected accuracy was within 0.08 ± 0.20 mm of manual localization. Introduced jaw and MLC position errors (1-10 mm) were detected within 0.55 mm (within 1 mm for 15/256 instances for the jaws). Phantom shifts (1.3 or 5 mm in each cardinal direction) from a reference position were detected within 0.26 mm.ConclusionsWe have demonstrated the accuracy and sensitivity of a daily end-to-end test suite capable of detecting errors in multiple machine subcomponents including system data transfer. Our test suite evaluates the entire treatment workflow and has captured system communication issues prior to patient treatment. With automated processing and the use of a standard vendor-provided phantom, it is possible to expand to other Unity sites.

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The IAEA remote and automated quality control methodology for radiography and mammography.

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