• Journal of radiological science and technology
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• v.47 no.2
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• pp.97-105
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• 2024

The examination needle used in ultrasound biopsy is a medical device used to determine whether there is an abnormality in the tissue. Typically, stainless steel is the standard material used for such needles; however, this study wanted to identify a material that could better enhance sound compared to traditional stainless steel. In this study, six types of needle materials available with the biopsy gun were inserted into pork and ultrasound images according to the curved probe and linear probe were evaluated using ultrasound equipment. The findings revealed significant improvements in ultrasound acoustic enhancement with alternative materials compared to stainless steel (p<0.05). The results regarding the depth of each ultrasound image using the curved probe showed that tungsten and brass had high sound enhancement(p<0.05), while with the linear probe, sound enhancement was high in brass, pla, aluminum, and copper(p<0.05). Due to these results, the previously used stainless needle showed lower ultrasound acoustic enhancement than the five types of materials being compared. Consequently, the outcomes of this study provide valuable insights for the development of new needle technologies aimed at minimizing patient risks and improving diagnostic accuracy.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.