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

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

Ultrasound Elastography

Elastography, the science of creating noninvasive images of mechanical characteristics of tissues, has been rapidly evolving in recent years. The advantage of this technique resides in the ability to rapidly detect and quantify the changes in the stiffness of soft tissues resulting from specific pathological or physiological processes. Ultrasound elastography is nowadays applied especially on the liver and breast, but the technique has been increasingly used for other tissues including the thyroid, lymph nodes, spleen, pancreas, gastrointestinal tract, kidney, prostate, and the musculoskeletal and vascular systems. This book presents some of the applications of strain and shear-wave ultrasound elastography in hepatic, pancreatic, breast, and musculoskeletal conditions

Development of a Harmonic Motion Imaging guided Focused Ultrasound system for breast tumor characterization and treatment monitoring

Breast cancer is the most common cancer and the second leading cause of cancer death among women. About 1 in 8 U.S. women (about 12%) will develop invasive breast cancer over the course of their lifetime. Existing methods of early detection of breast cancer include mammography and palpation, either by patient self-examination or clinical breast exam. Palpation is the manual detection of differences in tissue stiffness between breast tumors and normal breast tissue. The success of palpation relies on the fact that the stiffness of breast tumors is often an order of magnitude greater than that of normal breast tissue, i.e., breast lesions feel ''hard'' or ''lumpy'' as compared to normal breast tissue. A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammography is less likely to reveal breast tumors in women younger than 50 years with denser breast than in older women. When a suspicious site is detected in the breast through a breast self-exam or on a screening mammogram, the doctor may request an ultrasound of the breast tissue. A breast ultrasound can provide evidence about whether the lump is a solid mass, a cyst filled with fluid, or a combination of the two. An invasive needle biopsy is the only diagnostic procedure that can definitely determine if the suspicious area is cancerous. In the clinic, 80% of women who have a breast biopsy do not have breast cancer. Most women with breast cancer diagnosed will have some type of surgery to remove the tumor. Depending on the type of breast cancer and how advanced it is, the patient might need other types of treatment as well, such as chemotherapy and radiation therapy. Image-guided minimally-invasive treatment of localized breast tumor as an alternative to traditional breast surgery, such as high intensity focused ultrasound (HIFU) treatment, has become a subject of intensive research. HIFU applies extreme high temperatures to induce irreversible cell injury, tumor apoptosis and coagulative necrosis. Compared with conventional surgical procedures the main advantages of HIFU ablation lie in the fact that it is non-invasive, less scarring and less painful, allowing for shorter recovery time. HIFU can be guided by MRI (MRgFUS) or by conventional diagnostic ultrasound (USgFUS). Worldwide, thousands of patients with uterine fibroids, liver cancer, breast cancer, pancreatic cancer, bone tumors, and renal cancer have been treated by USgFUS. In this dissertation, the objective is to develop an integrated Harmonic Motion Imaging guided Focused Ultrasound (HMIgFUS) system as a clinical monitoring technique for breast HIFU with the added capability of detecting tumors for treatment planning, evaluation of tissue stiffness changes during HIFU ablation for treatment monitoring in real time, and assessment of thermal lesion sizes after treatment evaluation. A new HIFU treatment planning method was described that used oscillatory radiation force induced displacement amplitude variations to detect the HIFU focal spot before lesioning. Using this method, we were able to visualize the HMIgFUS focal region at variable depths. By comparing the estimated displacement profiles with lesion locations in pathology, we demonstrated the feasibility of using this HMI-based technique to localize the HIFU focal spot and predict lesion location during the planning phase. For HIFU monitoring, a HIFU lesion detection and ablation monitoring method was first developed using oscillatory radiation force induced displacement amplitude variations in real time. Using this method, the HMIgFUS focal region and lesion formation were visualized in real time at a feedback rate of 2.4 Hz. By comparing the estimated lesion size against gross pathology, the feasibility of using HMIgFUS to monitor treatment and lesion formation without interruption is demonstrated. In order to reduce the imaging time, it is shown in this dissertation that using the steered FUS beam, HMI can be used to image a 2.3 times larger ROI without requiring physical movement of the transducer. Using steering for HMI can be used to shorten the total imaging duration without requiring physical movement of the transducer. For the application of breast tumor, HMI and HMIgFUS were optimized and applied to ex vivo breast tissue. The results showed that HMI is experimentally capable of mapping and differentiating stiffness in normal and abnormal breast tissues. HMIgFUS can also successfully generate thermal lesions on normal and pathological breast tissues. HMI has also been applied to post-surgical breast mastectomy specimens to mimic the in vivo environment. In the end, the first HMI clinical system has been built with added capability of GUP-based parallel beamforming. A clinical trial has been approved at Columbia University to image breast tumor on patient. The HMI clinical system has shown to be able to map fibroadenoma mass on two patients with valid HMI displacement. The study in this dissertation may yield an early-detection technique for breast cancer without any age discrimination and thus, increase the survival rate

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome