[Evaluation for Resolution Property of CT Image with Half-reconstruction Algorithm].

PURPOSE: The aim of this study was to investigate the resolution property in XY-plane for half-reconstruction computed tomography (CT) image by measuring 360° multi-directional modulation transfer functions (MTFs). MATERIALS AND METHODS: The 360° multi-directional MTFs were measured by use of a wire method to obtain line spread function with 15° interval in XY-plane. The MTFs of half-reconstruction CT image were measured with 100 mm off-center positions on the X- and Y-axis (X+100 mm, X-100 mm, Y+100 mm, and Y-100 mm) and compared with those of full-reconstruction CT image. We measured the MTFs of the half-reconstruction CT image at X+100 mm position with various X-ray tube positions of projection dataset. RESULTS: There were obvious differences for the MTFs of the half-reconstruction CT image between the tangential and radial directions at each measurement position. The dependences of the resolution property for the half-reconstruction CT image on positions and directions in XYplane were similar to those for the full-reconstruction CT image. The higher and the lower MTFs of the half-reconstruction CT image at X+100 mm position were measured with X-ray tube position of projection dataset at +X side and at -X side compared with those of the full-reconstruction CT image, respectively. CONCLUSION: We conclude that the half-reconstruction CT image had similar resolution property in XY-plane to the full-reconstruction CT image and showed dependency on the X-ray tube position of projection dataset for MTF in the tangential direction.

Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network.

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner.