Application of QC_DR software for acceptance testing and routine quality control of direct digital radiography systems: initial experiences using the Italian Association of Physicist in Medicine quality control protocol

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

Evaluation of cassette-based digital radiography detectors using standardized image quality metrics: AAPM TG-150 Draft Image Detector Tests

The purpose of this study was to evaluate several of the standardized image quality metrics proposed by the American Association of Physics in Medicine (AAPM) Task Group 150. The task group suggested region‐of‐interest (ROI)‐based techniques to measure nonuniformity, minimum signal‐to‐noise ratio (SNR), number of anomalous pixels, and modulation transfer function (MTF). This study evaluated the effects of ROI size and layout on the image metrics by using four different ROI sets, assessed result uncertainty by repeating measurements, and compared results with two commercially available quality control tools, namely the Carestream DIRECTVIEW Total Quality Tool (TQT) and the GE Healthcare Quality Assurance Process (QAP). Seven Carestream DRX‐1C (CsI) detectors on mobile DR systems and four GE FlashPad detectors in radiographic rooms were tested. Images were analyzed using MATLAB software that had been previously validated and reported. Our values for signal and SNR nonuniformity and MTF agree with values published by other investigators. Our results show that ROI size affects nonuniformity and minimum SNR measurements, but not detection of anomalous pixels. Exposure geometry affects all tested image metrics except for the MTF. TG‐150 metrics in general agree with the TQT, but agree with the QAP only for local and global signal nonuniformity. The difference in SNR nonuniformity and MTF values between the TG‐150 and QAP may be explained by differences in the calculation of noise and acquisition beam quality, respectively. TG‐150's SNR nonuniformity metrics are also more sensitive to detector nonuniformity compared to the QAP. Our results suggest that fixed ROI size should be used for consistency because nonuniformity metrics depend on ROI size. Ideally, detector tests should be performed at the exact calibration position. If not feasible, a baseline should be established from the mean of several repeated measurements. Our study indicates that the TG‐150 tests can be used as an independent standardized procedure for detector performance assessment.

PACS number(s): 87.57.‐s, 87.57.C

Free software for performing physical analysis of systems for digital radiography and mammography

PURPOSE

In this paper, the authors present a free software for assisting users in achieving the physical characterization of x-ray digital systems and image quality checks.

METHODS

The program was developed as a plugin of a well-known public-domain suite ImageJ. The software can assist users in calculating various physical parameters such as the response curve (also termed signal transfer property), modulation transfer function (MTF), noise power spectra (NPS), and detective quantum efficiency (DQE). It also includes the computation of some image quality checks: defective pixel analysis, uniformity, dark analysis, and lag.

RESULTS

The software was made available in 2009 and has been used during the last couple of years by many users who gave us valuable feedback for improving its usability. It was tested for achieving the physical characterization of several clinical systems for digital radiography and mammography. Various published papers made use of the outcomes of the plugin.

CONCLUSIONS

This software is potentially beneficial to a variety of users: physicists working in hospitals, staff working in radiological departments, such as medical physicists, physicians, engineers. The plugin, together with a brief user manual, are freely available and can be found online (www.medphys.it/downloads.htm). With our plugin users can estimate all three most important parameters used for physical characterization (MTF, NPS, and also DQE). The plugin can run on any operating system equipped with ImageJ suite. The authors validated the software by comparing MTF and NPS curves on a common set of images with those obtained with other dedicated programs, achieving a very good agreement.