Fig 1.
Flowchart of patient and dataset selection for liver US image enhancement.
A total of 746 examinations obtained using a 12-year-old US system (input domain) and 2,652 examinations obtained using a newer high-end system (target domain) were screened. Exclusion criteria included patients younger than 17 years (n = 38), examinations predominantly consisting of color Doppler images (n = 3), and suboptimal studies according to the Korean Society of Ultrasound in Medicine guidelines (n = 179). After applying these criteria, eligible cases were randomly selected and divided into training, validation, and test sets. Images from the older device were used as the input dataset, and images from the newer device were used as the target dataset for unsupervised training.
Table 1.
Table for dataset description.
Fig 2.
Architecture of the proposed Switchable CycleGAN for liver US image enhancement.
(a) Overall framework of the Switchable CycleGAN. Images from the low-quality domain (Domain X) are translated to the high-quality domain (Domain Y) using a single shared generator (Generator A) modulated by an AdaIN code generated from the ACG. Cycle-consistency loss enforces reconstruction of the original domain, and identity loss constrains unnecessary modifications. Two discriminators (A and B) distinguish real and generated images in each domain. (b) Detailed architecture of the generator and ACG. The generator consists of convolutional, instance normalization, AdaIN-modulated layers, and up sampling blocks. The ACG produces channel-wise modulation parameters (mean and variance) that control domain translation within a shared feature space. (c) Architecture of the discriminator composed of convolutional layers with progressive down sampling to classify real versus synthetic images. Note: CycleGAN, cycle generative adversarial network; US, ultrasound; AdaIN, adaptive instance normalization; ACG, AdaIN Code Generator.
Table 2.
The scoring system of assessment for image quality.
Table 3.
Baseline patient characteristics.
Table 4.
Image quality assessment and inter-reader agreement of reviewer 1 and reviewer 2.
Fig 3.
Representative example of brightness improvement in liver US.
(a) Original right intercostal grey-scale liver US image obtained using a 12-year-old US system. (b) Post-processed image generated by the proposed model trained with images from a newer 4-year-old system as the target domain. The enhanced image demonstrates improved global brightness and clearer visualization of hepatic parenchyma. Both reviewers assigned a higher brightness score to the post-processed image (score 4) compared with the original image (score 3). Note: US, ultrasound.
Fig 4.
Representative example of contrast improvement in liver US.
(a) Original right intercostal grey-scale liver US image obtained using a 12-year-old US system. (b) Post-processed image generated by the proposed model trained with images from a newer 4-year-old system as the target domain. The enhanced image demonstrates improved contrast between hepatic parenchyma and adjacent structures. Both reviewers assigned higher contrast scores to the post-processed image (score 5) compared with the original image (score 3). Note: US, ultrasound.
Fig 5.
Representative example of reduced reverberation artifacts in liver US.
(a) Original right intercostal grey-scale liver US image obtained using a 12-year-old US system. (b) Post-processed image generated by the proposed model trained with images from a newer 4-year-old system as the target domain. The enhanced image shows reduced near-field reverberation artifacts and improved visualization of hepatic parenchyma. Both reviewers assigned higher reverberation artifact scores to the post-processed image (score 4) compared with the original image (score 3). Note: US, ultrasound.
Fig 6.
Representative example of focal lesion detection in liver US.
A 73-year-old woman experienced right intercostal grey-scale liver US examination. (a) Original image obtained using a 12-year-old US system. (b) Post-processed image generated by the proposed model trained with images from a newer 4-year-old system as the target domain. In the post-processed image, an approximately 2-cm hypoechoic nodule in the right hepatic lobe is more clearly visualized. Both reviewers identified the lesion in the enhanced image, whereas it was not detected in the original image. Note: US, ultrasound.
Table 5.
Inter-reader agreement of diagnosis between reviewers 1 and 2.
Fig 7.
Representative example of LC detection in US.
A 74-year-old woman with chronic hepatitis C virus infection experienced right subcostal grey-scale liver US examination. (a, b) Original images obtained using a 12-year-old US system. (c, d) Post-processed images generated by the proposed model trained with images from a newer 4-year-old system as the target domain. In the enhanced images, hepatic surface nodularity is more clearly visualized due to improved near-field contrast. Both reviewers diagnosed LC in the post-processed images, whereas only one reviewer diagnosed LC in the original images. Note: US, ultrasound; LC, liver cirrhosis.
Fig 8.
Qualitative comparison of the proposed method with conventional filtering and state-of-the-art I2I translation approaches.
For each sample, the first row shows results from the input image, the proposed method, Shock filter, and Bilateral filter. The second row shows results from NEGCUT, Negative Sample Pruning, UVCGAN, and StegoGAN. Two representative liver US samples are presented (Sample 1 and Sample 2). Compared with conventional filters and other generative models, the proposed method demonstrates balanced enhancement of brightness and contrast while preserving hepatic parenchymal texture and anatomical structures without excessive smoothing or oversaturation.
Table 6.
Comparison results of CR and CNR value.
Table 7.
Comparison results of FID score.
Table 8.
Quantitative evaluation of structure preservation (The parentheses indicate the correlation value between input and each method).
Fig 9.
Representative images for failure case due to heterogeneous parenchyma.
(a) Original right intercostal grey-scale liver US image obtained using a 12-year-old US system. (b) Post-processed image generated by the proposed model trained with images from a newer 4-year-old system as the target domain.