We herein describe a needle insertion method involving tissue preloading for accurate breast tumor treatment. A mechanical preloading probe locates a tumor lesion from ultrasound imaging information and reduces lesion displacement during needle insertion by pressing the breast tissue. We validated the insertion accuracy of this method by numerical simulation and experiments both in vitro and in vivo. For further accuracy enhancement, we evaluated the geometry effect of the preloading probe on needle insertion accuracy by experiments in vitro. We compared the insertion accuracy between insertion with preloading using different probe diameters and normal needle insertion. In addition, we compared insertion accuracy at different tumor depths. The data indicated a tendency for adaptation of larger preloading probe diameters with deeper tumors. This suggests the potential for our method to enhance placement accuracy by real-time geometry regulation.

We describe a palpation-based needle insertion method for diagnostic biopsy and radiofrequency ablation treatment of a breast tumor. The mechanical palpation probe locates cancerous tissue from ultrasound imaging information and reduces tissue displacement during needle insertion by pressing the breast tissue. We examined the insertion accuracy of this method and compared palpation-based needle insertion with normal needle insertion both in vitro and in vivo. Palpation-based needle insertion had a smaller error, suggesting that this procedure is a safe, effective alternative to traditional methods of breast needle insertion.

An innovative application of adaptive thresholding is used for calcification regions detection in intravascular ultrasound images. A priori knowledge about the acoustic shadow that usually follows the calcification regions is used as discriminant of other bright regions of the image. Tests were carried out with 20 in vivo coronary artery images obtained from different patients. This proposed algorithm presented specificity of 88% and sensitivity of 84%. A ROC curve, whose AUC was equal to 0.87, was plotted for evaluation of the algorithm performance