CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images.

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images.

Technical Note: Error metrics for estimating the accuracy of needle/instrument placement during transperineal magnetic resonance/ultrasound-guided prostate interventions.

Purpose: Image-guided systems that fuse magnetic resonance imaging (MRI) with three-dimensional (3D) ultrasound (US) images for performing targeted prostate needle biopsy and minimally invasive treatments for prostate cancer are of increasing clinical interest. To date, a wide range of different accuracy estimation procedures and error metrics have been reported, which makes comparing the performance of different systems difficult.

Methods: A set of nine measures are presented to assess the accuracy of MRI-US image registration, needle positioning, needle guidance, and overall system error, with the aim of providing a methodology for estimating the accuracy of instrument placement using a MR/US-guided transperineal approach.

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs.

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients.

A review of biomechanically informed breast image registration.

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc.

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.

Purpose: NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.

Methods: The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues.

MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters.

Determining corresponding regions between an MRI and an X-ray mammogram is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes between the two image acquisitions. In this work we propose an intensity-based image registration framework, where the biomechanical transformation model parameters and the rigid-body transformation parameters are optimised simultaneously. Patient-specific biomechanical modelling of the breast derived from diagnostic, prone MRI has been previously used for this task.

A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images.

Preoperative diagnostic magnetic resonance (MR) breast images can provide good contrast between different tissues and 3-D information about suspicious tissues. Aligning preoperative diagnostic MR images with a patient in the theatre during breast conserving surgery could assist surgeons in achieving the complete excision of cancer with sufficient margins. Typically, preoperative diagnostic MR breast images of a patient are obtained in the prone position, while surgery is performed in the supine position.

Hardness and density distributions of pharmaceutical tablets measured by terahertz pulsed imaging.

We present terahertz pulsed imaging (TPI) as a novel tool to quantify the hardness and surface density distribution of pharmaceutical tablets. Good agreement between the surface refractive index (SRI) measured by TPI and the crushing force measured from diametral compression tests was found using a set of tablets that were compacted at various compression forces. We also found a strong correlation between TPI results and tablet bulk density, and how these relate to tablet hardness.

Development of patient-specific biomechanical models for predicting large breast deformation.

Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation.

Variations in compaction behaviour for tablets of different size and shape, revealed by small-angle X-ray scattering.

Local variations in compaction behaviour were investigated, for specimens of different shapes and thickness, comparing predictions from finite element (FE) modelling and results from a recently developed method using small-angle X-ray scattering (SAXS). Good agreement was generally obtained between these methods, in terms of the variations of density, compaction strain and principal strain direction within the specimens examined. The combination of SAXS and FE methods appeared particularly suitable for studying pharmaceutical tablets, revealing effects (such as nano-strain of intragranular morphology and strain direction) that are not easily observed by other methods, and which may have significant effects on tablet integrity or swelling and drug delivery characteristics.

A system for simultaneously measuring contact force, ultrasound, and position information for use in force-based correction of freehand scanning.

During freehand ultrasound imaging, the sonographer places the ultrasound probe on the patient's skin. This paper describes a system that simultaneously records the position of the probe, the contact force between the probe and skin, and the ultrasound image. The system consists of an ultrasound machine, a probe, a force sensor, an optical localizer, and a host computer.

A novel ultrasound indentation system for measuring biomechanical properties of in vivo soft tissue.

Technologies for soft tissue analysis are advancing at a rapid place. For instance, elastography, which provides soft tissue strain images, is starting to be tried in clinical practice as a tool for diagnosing cancer. Soft tissue deformation modeling and analysis is also an active area of research that has application in surgery planning and treatment.