Freehand 2D Ultrasound Probe Calibration for Image Fusion with 3D MRI/CT

The aim of this work is to implement a simple freehand ultrasound (US) probe calibration technique. This will enable us to visualize US image data during surgical procedures using augmented reality. The performance of the system was evaluated with different experiments using two different pose estimation techniques. A near-millimeter accuracy can be achieved with the proposed approach. The developed system is cost-effective, simple and rapid with low calibration erro

Wound healing in rabbit corneas after flapless refractive lenticule extraction with a 345 nm ultraviolet femtosecond laser

Purpose To characterize corneal wound healing in a rabbit model after flapless refractive lenticule extraction with a 345 nm ultraviolet femtosecond laser. Setting Departments of Ophthalmology and Anatomy II, University of Erlangen-Nürnberg and Wavelight GmbH, Erlangen, Germany. Design Methods Flapless refractive lenticule extraction was performed in 1 eye each of 20 New Zealand white rabbits (−5.0 diopters). Groups of 4 animals were euthanized after 48 hours, 1 week, 2 weeks, 4 weeks, and 3 months, respectively. Corneal samples were prepared for histology and fluorescence microscopy. To assess corneal cell death, proliferation, and myofibroblastic transdifferentiation, terminal uridine deoxynucleotidyl nick end-labeling (TUNEL) assay as well as immunostaining for Ki67 and α-smooth muscle actin (αSMA) were performed on sagittal cryosections. Results Histology revealed a zone of keratocyte depletion with a thickness of approximately 50 μm around the extraction site. At 48 hours, pronounced TUNEL staining of keratocytes was detected around the interface (159.9 cells/mm ± 18.4 [SD]), which steadily decreased to 74.9 ± 19.8 cells/mm at 1 week and 5.7 ± 4.8 cells/mm at 2 weeks. Ki67 staining of keratocytes was evident at 48 hours (10.0 ± 3.8 cells/mm), which then decreased at 1 week (5.2 ± 1.7 cells/mm) and 2 weeks (0.4 ± 0.5 cells/mm). From 4 weeks onward, no TUNEL or Ki67 staining was detected. The corneal stroma was αSMA-negative at all timepoints. Conclusion Application of the 345 nm laser showed no signs of problematic repair processes in the cornea, which supports the initiation of the clinical phase

First step to facilitate long term and multi centre studies of shear wave elastography in solid breast lesions using a computer assisted algorithm

Purpose: Shear wave elastography (SWE) visualises the elasticity of tissue. As malignant tissue is generally stiffer than benign tissue, SWE is helpful to diagnose solid breast lesions. Until now, quantitative measurements of elasticity parameters have been possible only, while the images were still saved on the ultrasound imaging device. This work aims to overcome this issue and introduces an algorithm allowing fast offline evaluation of SWE images. Methods: The algorithm was applied to a commercial phantom comprising three lesions of various elasticities and 207 in vivo solid breast lesions. All images were saved in DICOM, JPG and QDE (quantitative data export; for research only) format and evaluated according to our clinical routine using a computer-aided diagnosis algorithm. The results were compared to the manual evaluation (experienced radiologist and trained engineer) regarding their numerical discrepancies and their diagnostic performance using ROC and ICC analysis. Results: ICCs of the elasticity parameters in all formats were nearly perfect (0.861–0.990). AUC for all formats was nearly identical for ${E}_{\mathrm{max}}$ and ${E}_{\mathrm{mean}}$ (0.863–0.888). The diagnostic performance of SD using DICOM or JPG estimations was lower than the manual or QDE estimation (AUC 0.673 vs. 0.844). Conclusions: The algorithm introduced in this study is suitable for the estimation of the elasticity parameters offline from the ultrasound system to include images taken at different times and sites. This facilitates the performance of long-term and multi-centre studies

Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging

PURPOSE: Ultrasound (US) and Shear Wave Elastography (SWE) imaging are non-invasive methods used for breast lesion characterization. While US and SWE images provide both morphological information, SWE visualizes in addition the elasticity of tissue. In this study a Discriminative Convolutional Neural Network (DCNN) model is applied to US and SWE images and their combination to classify the breast lesions into malignant or benign cases. Furthermore, it is identified whether analysing only the region of the elastogram or including the surrounding B-mode image gives a superior performance. METHODS: The dataset used in this study consists of 746 images obtained from 207 patients comprising 486 malignant and 260 benign breast lesions. From each image the US and SWE image was extracted, once including only the region of the elastogram and once including also the surrounding B-mode image. These four datasets were applied individually to a DCNN to determine their predictive capability. Each the best US and SWE dataset were used to examine different combination methods with DCNN. The results were compared to the manual assessment by an expert radiologist. RESULTS: The combination of US and SWE images with the surrounding B-mode image using two ensembled DCNN models achieved best results with an accuracy of 93.53 %, sensitivity of 94.42 %, specificity of 90.75 % and area under the curve (AUC) of 96.55 %. CONCLUSION: This study showed that using the whole US and SWE images through DCNN was superior to methods, in which only the region of elastogram was used. Combining breast cancer US and SWE images with two ensembled DCNN models in parallel improved the results. The accuracy, sensitivity and AUC of the best combination method were significantly superior to the results of using a single dataset through DCNN and to the results of the expert radiologist