Evaluation of juxta-apical radiolucency in cone beam CT images.
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ABSTRACT:
SUBMITTER: Kapila R
PROVIDER: S-EPMC4082266 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.
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Project description:PurposeTo assess the ability of Cone-Beam CT (CBCT), performed during the Transcatheter Arterial Chemoembolization (TACE), in predicting the response to treatment of hepatocellular carcinoma (HCC).Materials and methodsWe evaluated fifty patients (M/F = 40/10, mean age: 66.7 years ± 8.22) with hepatocellular carcinoma (HCC), for a total of 82 nodules evaluated (mean diameter: 21.4 ± 11 mm). All patients performed a CT scan one month before and one month after TACE. After TACE is completed, a CBCT was performed to assess the degree of drug retention in the lesions. For each lesion, the major diameter, volume, and density of the vital portion were evaluated. The response to TACE was assessed using the mRECIST criteria on the CT scan carried out one month after the procedure. The receiver operating characteristic (ROC) curves were performed to assess the accuracy of the CBCT in predicting the response to treatment and to identify the cut-off points for each parameter under examination.ResultsA complete response (CR) was observed in 24/50 patients (48%), a partial response (PR) in 16/50 (32%), stable disease (SD) in 8/50 (16%), and progressive disease (PD) in 2/50 (4%). Evaluation of the area under the ROC curve showed that the diameter, volume, and density of the lesion, measured with CBTC, had an accuracy of 94%, 96%, and 98%, respectively, in discriminating a complete response from a not complete response.ConclusionCBCT is effective in predicting short-term response at 1-month follow-up of HCC treated by chemoembolization.
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Project description:Background & purposeFour dimensional Cone beam CT (CBCT) has many potential benefits for radiotherapy but suffers from poor image quality, long acquisition times, and/or long reconstruction times. In this work we present a fast iterative reconstruction algorithm for 4D reconstruction of fast acquisition cone beam CT, as well as a new method for temporal regularization and compare to state of the art methods for 4D CBCT.Materials & methodsRegularization parameters for the iterative algorithms were found automatically via computer optimization on 60 s acquisitions using the XCAT phantom. Nineteen lung cancer patients were scanned with 60 s arcs using the onboard image on a Varian trilogy linear accelerator. Images were reconstructed using an accelerated ordered subset algorithm. A frequency based temporal regularization algorithm was developed and compared to the McKinnon-Bates algorithm, 4D total variation and prior images compressed sensing (PICCS).ResultsAll reconstructions were completed in 60 s or less. The proposed method provided a structural similarity of 0.915, compared with 0.786 for the classic McKinnon-bates method. For the patient study, it provided fewer image artefacts than PICCS, and better spatial resolution than 4D TV.ConclusionFour dimensional iterative CBCT reconstruction was done in less than 60 s, demonstrating the clinical feasibility. The frequency based method outperformed 4D total variation and PICCS on the simulated data, and for patients allowed for tumor location based on 60 s acquisitions, even for slowly breathing patients. It should thus be suitable for routine clinical use.
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Project description:Background and purposeThe prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients.Materials and methodsThis retrospective study includes a training dataset of 141 stage I-IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo)radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan-Meier curves and c-index.ResultsThe radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence.ConclusionIn this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets.
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