MRI image synthesis for fluid-attenuated inversion recovery and diffusion-weighted images with deep learning

This study aims to synthesize fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted images (DWI) with a deep conditional adversarial network from T1- and T2-weighted magnetic resonance imaging (MRI) images. A total of 1980 images of 102 patients were split into two datasets: 1470 (68 patients) in a training set and 510 (34 patients) in a test set. The prediction framework was based on a convolutional neural network with a generator and discriminator. T1-weighted, T2-weighted, and composite images were used as inputs. The digital imaging and communications in medicine (DICOM) images were converted to 8-bit red-green-blue images. The red and blue channels of the composite images were assigned to 8-bit grayscale pixel values in T1-weighted images, and the green channel was assigned to those in T2-weighted images. The prediction FLAIR and DWI images were of the same objects as the inputs. For the results, the prediction model with composite MRI input images in the DWI image showed the smallest relative mean absolute error (rMAE) and largest mutual information (MI), and that in the FLAIR image showed the largest relative mean-square error (rMSE), relative root-mean-square error (rRMSE), and peak signal-to-noise ratio (PSNR). For the FLAIR image, the prediction model with the T2-weighted MRI input images generated more accurate synthesis results than that with the T1-weighted inputs. The proposed image synthesis framework can improve the versatility and quality of multi-contrast MRI without extra scans. The composite input MRI image contributes to synthesizing the multi-contrast MRI image efficiently.

Emergence of teleradiology, PACS, and other radiology IT solutions in Acta Radiologica

For this historical review, we searched a database containing all the articles published in Acta Radiologica during its 100-year history to find those on the use of information technology (IT) in radiology. After reading the full texts, we selected the presented articles according to major radiology IT domains such as teleradiology, picture archiving and communication systems, image processing, image analysis, and computer-aided diagnostics in order to describe the development as it appeared in the journal. Publications generally follow IT megatrends, but because the contents of Acta Radiologica are mainly clinically oriented, some technology achievements appear later than they do in journals discussing mainly imaging informatics topics.

Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Purpose

To synthesize a dual‐energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV‐CT) image using a deep convolutional adversarial network.

Methods

A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kV‐CT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV‐CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI).

Results

The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image.

Conclusions

The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single‐energy CT images.

Lung image quality with 320-row wide-volume CT scans: the effect of prospective ECG-gating and comparisons with 64-row helical CT scans

RATIONALES AND OBJECTIVES

To evaluate the image quality of 320-row wide-volume (WV) computed tomography (CT) scans in comparison with 64-row helical scans for the lung.

MATERIALS AND METHODS

The Institutional Review Board of each institution approved this prospective, multicenter study and informed consent was obtained. A total of 73 subjects underwent two types of chest CT, including 320-row WV scans and 64-row helical scans. Both scans used the same tube voltage, tube current, exposure time setting, and slice thickness. The helical scans were not electrocardiogram (ECG)-gated. For the WV scans, prospective ECG-gating was used for 38 subjects, whereas the other 35 subjects did not have ECG-gating. Using a 5-point scale from 1 (nondiagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for five lobes and the lingula. The differences in the scores between WV scans and helical scans were compared using Wilcoxon's signed-rank test.

RESULTS

The WV scans with ECG-gating had significantly higher scores than 64-row helical scans for all lobes and lingula (right lower lobe, P < .01; other lobes and lingula, P < .0001, respectively). The 320-row WV scans without ECG-gating also had significantly higher scores than 64-row helical scans (P < .05), except for nonsignificant differences for the left upper lobe.

CONCLUSIONS

Lung image quality of ECG-gated WV scans, which do not require any additional radiation exposure, is better than that of non-ECG-gated 64-row helical scans. Non-ECG-gated 320-row WV scans are comparable or slightly superior to non-ECG-gated 64-row helical scans.