Fusion of magnetic resonance and ultrasound images for endometriosis detection

Endometriosis is a gynecologic disorder that typically affects women in their reproductive age and is associated with chronic pelvic pain and infertility. In the context of pre-operative diagnosis and guided surgery, endometriosis is a typical example of pathology that requires the use of both magnetic resonance (MR) and ultrasound (US) modalities. These modalities are used side by sidebecause they contain complementary information. However, MRI and US images have different spatial resolutions, fields of view and contrasts and are corrupted by different kinds of noise, which results in important challenges related to their analysis by radiologists. The fusion of MR and US images is a way of facilitating the task of medical experts and improve the pre-operative diagnosis and the surgery mapping. The object of this PhD thesis is to propose a new automatic fusion method for MRI and US images. First, we assume that the MR and US images to be fused are aligned, i.e., there is no geometric distortion between these images. We propose a fusion method for MR and US images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on an inverse problem, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to modelthe relationships between the gray levels of the MR and US images. However, the proposed fusion method is very sensitive to registration errors. Thus, in a second step, we introduce a joint fusion and registration method for MR and US images. Registration is a complicated task in practical applications. The proposed MR/US image fusion performs jointly super-resolution of the MR image and despeckling of the US image, and is able to automatically account for registration errors. A polynomial function is used to link ultrasound and MR images in the fusion process while an appropriate similarity measure is introduced to handle the registration problem. The proposed registration is based on a non-rigid transformation containing a local elastic B-spline model and a global affine transformation. The fusion and registration operations are performed alternatively simplifying the underlying optimization problem. The interest of the joint fusion and registration is analyzed using synthetic and experimental phantom images

Endometriosis

Endometriosis is a common and serious disease that is estimated to cost the world economy $9.7 billion a year. Most of these costs come from lost productivity at work. As such, it is important to help women receive earlier diagnosis and more effective treatment. This book presents a comprehensive overview of endometriosis, including information on molecular diagnostics and imaging methods for early detection as well as new, less-invasive treatments that preserve women’s fertility

Experience and Enlightenment of Handheld Ultrasound Applications in Multiple Scenarios Based on 5G Technology

In the digital age, the miniaturization of portable ultrasound equipment has brought both opportunities and challenges to the healthcare industry. Handheld ultrasound (HHU) devices are tablet or smartphone-sized scanners that are highly portable, have lower costs, produce no harmful side effects, and consume less power, making them suitable for use in different environments. HHU devices are primarily designed for new users of ultrasound scanners with varying backgrounds to evaluate different structures of the human body in various clinical settings. HHU applications based on Fifth-generation (5G) wireless network communication and artificial intelligence (AI) technology provide new healthcare solutions. The main application scenarios for HHU devices currently include in-hospital use, remote medical treatment, emergency rescue, and home monitoring. These scenarios allow for rapid image acquisition and real-time image interpretation, thereby improving the efficiency and quality of healthcare, reducing medical costs, and improving the allocation and utilization of medical resources. However, there remain some technical challenges and weaknesses such as device safety, data privacy, and network stability. With the continuous integration of AI technology, HHU applications will find wider use and promotion, bringing about more opportunities and challenges to the healthcare industry. This article reviews the application experience and insights of 5G technology in the field of HHU, aiming to provide fresh evidence and references for future research and applications

Application of Advanced MRI to Fetal Medicine and Surgery

Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Use of Software Tools to Implement Quality Control of Ultrasound Images in a Large Clinical Trial

Research Question This thesis aims to answer the question as to whether software tools might be developed for automating the analysis of images used to measure ovaries in transvaginal sonography (TVS) exams. Such tools would allow the routine collection of independent and objective metrics at low cost and might be used to drive a programme of continuous Quality Improvement (QI) in TVS scanning. The tools will be assessed by processing images from thousands of TVS exams performed by the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Background This research is important because TVS is core to any ovarian cancer (OC) screening strategy yet independent and objective quality control (QC) metrics for this procedure are not routinely obtained due to the high cost of manual image inspection. Improving the quality of TVS in the National Health Service (NHS) would assist in the early diagnosis of the disease and result in improved outcome for some women. Therefore, the research has clear translational potential for the >1.2 million scans performed annually by the NHS. Research Findings A study performed to process images from 1,000 TVS exams has shown the tool produces accurate and reliable QC metrics. A further study revealed that over half of these exams should have been classified as unsatisfactory as an expert review of the images showed that that the sonographer had mistakenly measured a structure that was not an ovary. It also reported a correlation between such ovary visualisation and a novel metric (DCR) measured by the tools from the examination images. Conclusion The research results suggest both a need to improve the quality of TVS scanning and the viability of achieving this objective by introducing a QI programme driven by metrics gathered by software tools able to analyze the images used to measure ovaries