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. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

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. RESULTS: Using the SmartTarget fusion system, an MRI-US image alignment error was determined to be 2.0 ± 1.0 mm (mean ± SD), and an overall system instrument targeting error of 3.0 ± 1.2 mm. Three needle deployments for each target phantom lesion was found to result in a 100% lesion hit rate and a median predicted cancer core length of 5.2 mm. CONCLUSIONS: The application of a comprehensive, unbiased validation assessment for MR/US guided systems can provide useful information on system performance for quality assurance and system comparison. Furthermore, such an analysis can be helpful in identifying relationships between these errors, providing insight into the technical behavior of these systems.

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. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated.

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. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

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. Two new calibration methods are demonstrated: a temporal calibration to determine the time delay between force and position measurements, and a gravitational calibration to remove the effect of gravity on the recorded force. Measurements made with the system showed good agreement with those obtained from a standard materials testing machine. The system's uses include three-dimensional (3-D) ultrasound imaging, force-based deformation correction of ultrasound images, and indentation testing.

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. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided. RESULTS: Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. CONCLUSION: The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

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. Typically, quantitative soft tissue analysis uses nominal values of soft tissue biomechanical properties. However, in practice, soft tissue properties can vary significantly between individuals. Hence, for soft tissue methodologies to reach their full potential as patient-specific techniques, there is a need to develop ways to efficiently measure soft tissue mechanical properties in vivo. This paper describes a prototype real-time ultrasound (US) indentation test system developed to meet this need. The system is based on the integration of a force sensor and an optical tracking system with a commercial US machine integrated with a suite of analysis methodologies. In a study on a single-layer phantom, we used the system to compare various methods of estimating linear elastic properties (via a theoretical approximation, 2-D finite element analysis, 3-D finite element analysis and a standard material-testing method). In a second study on a three-layer gelatin phantom, we describe a new finite-element-based inverse solution for recovering the Young's moduli of each layer to show how the system can estimate properties of internal components of soft tissue. Finally, we show how the system can be used to derive a modified quasilinear viscoelastic (QVL) model on real breast tissue.

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. The significant shape change of breasts between these two positions due to gravity loading, external forces and related constraints makes the alignment task extremely difficult. Our previous studies have shown that either nonrigid intensity-based image registration or biomechanical modelling alone are limited in their ability to capture such a large deformation. To tackle this problem, we proposed in this paper a nonlinear biomechanical model-based image registration method with a simultaneous optimization procedure for both the material parameters of breast tissues and the direction of the gravitational force. First, finite element (FE) based biomechanical modelling is used to estimate a physically plausible deformation of the pectoral muscle and the major deformation of breast tissues due to gravity loading. Then, nonrigid intensity-based image registration is employed to recover the remaining deformation that FE analyses do not capture due to the simplifications and approximations of biomechanical models and the uncertainties of external forces and constraints. We assess the registration performance of the proposed method using the target registration error of skin fiducial markers and the Dice similarity coefficient (DSC) of fibroglandular tissues. The registration results on prone and supine MR image pairs are compared with those from two alternative nonrigid registration methods for five breasts. Overall, the proposed algorithm achieved the best registration performance on fiducial markers (target registration error, 8.44 ±5.5 mm for 45 fiducial markers) and higher overlap rates on segmentation propagation of fibroglandular tissues (DSC value > 82%).

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. However, the high computational time associated with breast compression simulation using commercial packages, did not allow the optimisation of both pose and FEM parameters in the same framework. We use a fast explicit Finite Element (FE) solver that runs on a graphics card, enabling the FEM-based transformation model to be fully integrated into the optimisation scheme. The transformation model has seven degrees of freedom, which include parameters for both the initial rigid-body pose of the breast prior to mammographic compression, and those of the biomechanical model. The framework was tested on ten clinical cases and the results were compared against an affine transformation model, previously proposed for the same task. The mean registration error was 11.6±3.8mm for the CC and 11±5.4mm for the MLO view registrations, indicating that this could be a useful clinical tool.

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. Numerical simulations of tablet surface density distribution by finite element analysis exhibit excellent agreement with the TPI measured SRI maps. These results show that TPI has an advantage over traditional diametral compression and is more suitable for nondestructive hardness and density distribution monitoring and control of pharmaceutical manufacturing processes.

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. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.

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.