Image Quality Improvement of Mobile Ultrasound Devices

Project Leaders

Haoming Chen

Partner Organisations

西京医院

Mobile ultrasound devices play a crucial role in emergency and family doctor services, providing valuable diagnostic capabilities in a portable form factor. However, when compared to traditional medical ultrasound equipment, there is still scope for improvement in terms of image quality.

We propose the Hyper-CycleGAN model as a solution to enhance the image quality of mobile ultrasound devices. This model aims to establish a structure-preserving mapping between low-quality ultrasound images and their corresponding high-quality counterparts. Additionally, we plan to incorporate an attention mechanism into the model to enable it to better focus on important areas within the image, thus further enhancing the diagnostic relevance of the generated high-quality images. By leveraging advanced techniques and algorithms, our approach aims to bridge the gap in image quality between mobile ultrasound devices and traditional medical ultrasound equipment, thereby improving the accuracy and effectiveness of emergency and family doctor services.

Project Example

The first image is a low-resolution handheld ultrasound image, and the following five images are generated with settings for 2, 4, 6, 8, and 10 levels of image clarity.

A Transducer-adaptive Denoising Model for Medical Ultrasound Imaging

Project Leaders

Mingfu Jiang

Partner Organisations

南京航天航空大学

Ultrasound imaging is widely used in clinical diagnosis due to its advantages such as safety, non-invasiveness, convenience, and ease of operation. However, actual ultrasound images often suffer from problems such as speckle noise, low signal-to-noise ratio, and low contrast, requiring denoising of the ultrasound images.

During the process of ultrasound image acquisition, due to the reflection, scattering, and refraction characteristics of ultrasound echoes, as well as the heterogeneity and spatially uncertain properties of different parts of the human body, a large number of randomly distributed scattering particles are formed when ultrasound waves penetrate the human body. The interactions between scattering particles generate correlated scattering beams. During the process of echo reflection, interference effects from the interference of reflected echoes and mutual interference between scattering beams cause amplitude enhancement and attenuation due to the different phases of the echoes when different beams overlap. This leads to random fluctuations in the electrical signals in the output after envelope detection by the transducer, and generates speckle particles with different brightness levels in ultrasound images. This type of noise is commonly referred to as multiplicative speckle noise. Additionally, during the operation of the ultrasound device, internal components, circuits, electromagnetic interference, etc., produce additive Gaussian noise. These types of noise collectively contribute to the complex noise in medical ultrasound images.

Currently, researchers have studied denoising methods for medical ultrasound images. However, these methods cannot adaptively denoise the noise generated by different settings of medical ultrasound devices.

To overcome the challenges encountered in denoising ultrasound images, we propose a controllable method for denoising medical ultrasound images. This study mainly utilizes the TATLAB Toolbox to generate simulated noisy ultrasound image datasets and constructs an initial denoising model using the preprocessing of simulated noisy ultrasound images, the multi-level residual atrous spatial pyramid pooling (MRASPP) module, the nonlinear mapping convolutional neural network (NMCNNB) module, and the adaptive noise level and variable denoising intensity module. The model is trained through two rounds of inference.

This method can adapt to a wider range of denoising scenarios, adaptively denoise the images, flexibly adjust the denoising intensity, effectively remove noise from medical ultrasound images, and preserve the details of the images. Compared to existing denoising methods, the average values of SSIM and PSNR have been improved by 1.67% and 1.28%, respectively. A comparison was made between the denoised and original images to evaluate their performance in downstream image classification tasks. After denoising real breast ultrasound images, the ACC and AUC of tumor benign-malignant classification tasks were improved by 1.09% and 2.83%, respectively.

The proposed controllable method for denoising medical ultrasound images, using two rounds of inference, can adaptively select the noise level parameter for effective denoising of ultrasound images formed by different ultrasound devices and settings. This method provides a new approach for denoising other types of medical images.

Please note that the translation and revision have been done to the best of my abilities, but there might still be room for improvement.

Project Example

Return