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

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

X-ray and neutron μCT of biomedical samples: from image acquisition to quantification

Even though the validity of x-ray computed tomography in the analysis of biomedical samples is nowadays undisputed, the more recent imaging techniques and more advanced instruments (such as synchrotrons) are still relatively unknown to many medical doctors that could benefit from them.The doctoral work presented in this thesis joins a collective effort from the imaging community to demonstrate potential applications of advanced x-ray and neutron imaging methods to preclinical medical research, with the hope of contributing to reach a “critical mass” in the medical community and in the public opinion as well.Two main lines of work are detailed, one focused on the ex vivo evaluation of corrosion processes of magnesium-based biodegradable implants for osteosynthesis, the other dedicated to the assessment of neuropathy in human gastroenteric dysmotility. The aimed endpoint was to develop pipelines, from image acquisition all the way to data quantification, that could be used by other research groups with similar questions and may inspire future interdisciplinary collaborations between medicine, natural science and engineering.In the first line of work, we have attempted to employ synchrotron-radiation micro-computed tomography (µCT) coupled with in situ loading tests to assess the mechanical properties of the bone-implant interface (Paper I). We have revealed the crucial importance of the radiation dose deposited on the sample, and that the mechanical loading geometry should be accurately determined in the planning steps of the experiment. Moving away from the mechanical testing, we have also explored a novel three-dimensional analysis of the corrosion by-products of biodegradable implants by combining x-ray µCT, neutron µCT and x-ray fluorescence mapping (Papers IV and V). The second line of work has assessed the potential of x-ray phase-contrast µCT and nano-resolution holotomography as ways to perform virtual histology of unstained peripheral and autonomic neural tissue. In full-thickness biopsies of the myenteric nervous system, qualitative and potentially quantitative differences have been shown between controls and patients affected by gastrointestinal dysmotility (Paper II). In unstained skin biopsies, the methods have failed to visualise peripheral nerves, but we could identify structural changes in the connective tissue of some patients when compared to controls and other patients (Paper III)

Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-specific IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a task-agnostic IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed algorithm using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images, from 850 patients. The proposed reward shaping strategy, with appropriately weighted task-specific and task-agnostic qualities, successfully identified samples that need re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.or

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue