We propose a method for measuring the modulation transfer function (MTF) of a computed tomography (CT) system by use of a circular edge method with a logistic curve-fitting technique. An American College of Radiology (ACR) phantom was scanned by a Philips Brilliance system, and axial images were reconstructed by the filtered back projection algorithm with a standard reconstruction filter. The radial MTF was measured from a disk image of a rod or cylinder in the ACR phantom by use of the circular edge method. In this study, we applied a logistic curve-fitting technique to an edge-spread function (ESF) to eliminate noise because the edge method is very susceptible to noise in the ESF in a CT image. The circular edge method with the logistic curve-fitting technique provided the MTF without fluctuations due to noise for the entire spatial frequency range. The MTF was not affected by the tube current, the slice thickness, or the disk contrast, which were factors related to the amount of noise in the CT image. However, the MTF was affected by the location of the disk and by the disk size, depending on the average distance from the isocenter to the disk edge. Our results indicated that the MTF measured by the circular edge method with the logistic curve-fitting technique was not susceptible to noise in CT images. Therefore, this method is useful for MTF measurement for not only high-contrast objects, but also low-contrast objects with a large amount of noise.
The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as “Normal,” “No Opacity/Not Normal,” or “Opacity” by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.
The fact that accurate detection of metastatic brain tumors is important for making decisions on the treatment course of patients prompted us to develop a computer-aided diagnostic scheme for detecting metastatic brain tumors. In this paper, we first describe how we extracted the cerebral parenchyma region using a standard deviation filter. Second, initial candidates for tumors were decided by sphericity and cross-correlation value with a simulated ring template. Third, we made true positive and false positive templates obtained from actual clinical images and applied the template matching technique to them. Finally, we detected metastatic tumors using these two characteristics. Our improved method was applied to 13 cases with 97 brain metastases. Sensitivity of detection of metastatic brain tumors was 80.4%, with 5.6 false positives per patient. Our proposed method has potential for detection of metastatic brain tumors in brain magnetic resonance (MR) images.
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