Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Academic radiology ; h5-index 0.0

OBJECTIVES : Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into image reconstruction. In this phantom study, we compared the image noise characteristics, spatial resolution, and task-based detectability on DLR images and images reconstructed with other state-of-the art techniques.

METHODS : We scanned a phantom harboring cylindrical modules with different contrast on a 320-row detector CT scanner. Phantom images were reconstructed with filtered back projection, hybrid iterative reconstruction, model-based iterative reconstruction, and DLR. The standard deviation of the CT number and the noise power spectrum were calculated for noise characterization. The 10% modulation-transfer function (MTF) level was used to evaluate spatial resolution; task-based detectability was assessed using the model observer method.

RESULTS : On images reconstructed with DLR, the noise was lower than on images subjected to other reconstructions, especially at low radiation dose settings. Noise power spectrum measurements also showed that the noise amplitude was lower, especially for low-frequency components, on DLR images. Based on the MTF, spatial resolution was higher on model-based iterative reconstruction image than DLR image, however, for lower-contrast objects, the MTF on DLR images was comparable to images reconstructed with other methods. The machine observer study showed that at reduced radiation-dose settings, DLR yielded the best detectability.

CONCLUSION : On DLR images, the image noise was lower, and high-contrast spatial resolution and task-based detectability were better than on images reconstructed with other state-of-the art techniques. DLR also outperformed other methods with respect to task-based detectability.

Higaki Toru, Nakamura Yuko, Zhou Jian, Yu Zhou, Nemoto Takuya, Tatsugami Fuminari, Awai Kazuo

2020-Jan

Phantoms, X-ray computed tomography, artificial intelligence, imaging, machine learning, neural networks