State of the art: Eye-tracking studies in medical imaging

Eye-tracking – the process of measuring where people look in a visual field – has been widely used to study how humans process visual information. In medical imaging, eye-tracking has become a popular technique in many applications to reveal how visual search and recognition tasks are performed, providing information that can improve human performance. In this paper, we present a comprehensive review of eye-tracking studies conducted with medical images and videos for diverse research purposes, including identification of degree of expertise, development of training, and understanding and modelling of visual search patterns. In addition, we present our recent eye-tracking study that involves a large number of screening mammograms viewed by experienced breast radiologists. Based on the eye-tracking data, we evaluate the plausibility of predicting visual attention by computational models

Models of breast lesions based on three-dimensional X-ray breast images

This paper presents a method for creation of computational models of breast lesions with irregular shapes from patient Digital Breast Tomosynthesis (DBT) images or breast cadavers and whole-body Computed Tomography (CT) images. The approach includes six basic steps: (a) normalization of the intensity of the tomographic images; (b) image noise reduction; (c) binarization of the lesion area, (d) application of morphological operations to further decrease the level of artefacts; (e) application of a region growing technique to segment the lesion; and (f) creation of a final 3D lesion model. The algorithm is semi-automatic as the initial selection of the region of the lesion and the seeds for the region growing are done interactively. A software tool, performing all of the required steps, was developed in MATLAB. The method was tested and evaluated by analysing anonymized sets of DBT patient images diagnosed with lesions. Experienced radiologists evaluated the segmentation of the tumours in the slices and the obtained 3D lesion shapes. They concluded for a quite satisfactory delineation of the lesions. In addition, for three DBT cases, a delineation of the tumours was performed independently by the radiologists. In all cases the abnormality volumes segmented by the proposed algorithm were smaller than those outlined by the experts. The calculated Dice similarity coefficients for algorithm-radiologist and radiologist-radiologist showed similar values. Another selected tumour case was introduced into a computational breast model to recursively assess the algorithm. The relative volume difference between the ground-truth tumour volume and the one obtained by applying the algorithm on the synthetic volume from the virtual DBT study is 5% which demonstrates the satisfactory performance of the proposed segmentation algorithm. The software tool we developed was used to create models of different breast abnormalities, which were then stored in a database for use by researchers working in this field

Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography

Deep learning (DL) models can be trained on contrast-enhanced mammography (CEM) images to detect and classify lesions in the breast. As they often put more emphasis on the masses enhanced in the recombined image, they can fail in recognizing microcalcification clusters since these are hardly enhanced and are mainly visible in the (processed) lowenergy image. Therefore, we developed a method to create synthetic data with simulated microcalcification clusters to be used for data augmentation and explainability studies when training DL models. At first 3-dimensional voxel models of simulated microcalcification clusters based on descriptors of the shape and structure were constructed. In a set of 500 simulated microcalcification clusters the range of the size and of the number of microcalcifications per cluster followed the distribution of real clusters. The insertion of these clusters in real images of non-delineated CEM cases was evaluated by radiologists. The realism score was acceptable for single view applications. Radiologists could more easily categorize synthetic clusters into benign versus malignant than real clusters. In a second phase of the work, the role of synthetic data for training and/or explaining DL models was explored. A Mask R-CNN model was trained with synthetic CEM images containing microcalcification clusters. After a training run of 100 epochs the model was found to overfit on a training set of 192 images. In an evaluation with multiple test sets, it was found that this high level of sensitivity was due to the model being capable of recognizing the image rather than the cluster. Synthetic data could be applied for more tests, such as the impact of particular features in both background and lesion models

The development and validation of a methodology to compare 2D digital mammography and breast tomosynthesis

Two-dimensional (2D) full field digital mammography (FFDM) is an established technique for breast cancer screening and diagnosis, yet a substantial percentage of breast cancers evades detection. Retrospective studies have shown that this percentage ranges from 1% - 35% and hence there is common consensus that sensitivity and specificity should be further improved, especially in dense breasts. A major drawback of 2D mammography is the presence of overlapping structure in the image, or in other words, the representation of three-dimensional (3D) breast structure as 2D projection. This has two consequences: first, there is the reduction of the visibility or ‘conspicuity’ of lesions and second, the overlapping (normal) structures can be mistaken for lesions. Digital breast tomosynthesis (DBT) is a pseudo-3D breast imaging technique that attempts to overcome this superposition problem. In DBT, a sequence of projection images is acquired, typically covering an angular range of 10°-50°. This technique allows a cross-sectional visualization of the breast, with the aim of reducing the superposition of tissue and hence improving cancer detection. Justification of both the screening and diagnostic use of DBT in the management of breast cancer calls for an evaluation of DBT against the current standard technique, namely FFDM. This thesis focuses on the development of methodologies for the comparison of detection performance between DBT and FFDM. Given that image quality in this modality is largely determined by the visualization of breast structures and lesions in the focal plane and the efficiency with which overlying tissues are suppressed, a primary requirement is the presence of a ‘disturbing’ 3D structure within the performance test. The applicability of a previously designed physical structure was investigated in the first part of the thesis. Gang et al. showed that a mixture of equal volumes of spheres in water has similar statistical properties to various anatomical sites such as breast structure. Applying this to mammography, a compressed breast-shaped container was built and filled with water and acrylic spheres of six different diameters. The structure was evaluated by fitting a power law function in the low spatial frequency region of the measured power spectra (PS) for FFDM and DBT images: the power law exponent β defines the texture while κ is the power spectrum magnitude. The spheres in water structure was compared against real breast structure together with two commercially available phantoms. None of the three phantoms presented an exact match to patient PS in terms of κ and β but the power law theory gave valuable insight into the physical design, construction and optimization of the structured phantom. After reducing the thickness of the phantom to bring the PS data closer to those of the patients, the search began for appropriate target objects to embed in the sphere-based background structure for use in detectability studies. The simulation of masses started with spheres of different glandular tissue equivalent densities while for the representation of microcalcifications, particles of calcium carbonate (CaCO3) were used. The x-ray projection properties of the spheres were evaluated by embedding the spheres in a homogeneous medium of vegetable oil, while microcalcifications were embedded in polymethyl methacrylate (PMMA). In order to insert these objects in a structured background, a hybrid method was developed. Templates of the objects were created by dividing projection images of the targets embedded in the homogeneous background by images of the background only. These templates were then inserted in the DICOM ‘for processing’ FFDM and DBT projection images of patients. Following image processing/reconstruction, the detection of these spherical densities and microcalcifications in FFDM and DBT patient images was compared using a human observer detection study. All spherical densities had a significantly higher detection rate in DBT, while microcalcification detection was superior in FFDM. In the third part, we applied a Fourier-based model observer for the detection of targets in breast structure in order to avoid the time-consuming human observer studies that were realized in part two. This model observer has an analytical formulation that uses explicitly measured system sharpness and noise parameters and hence allows the influence of these parameters on FFDM and DBT imaging performance. Although good correlations between the theoretical detectability index (d’) and a more simple image quality metric, signal difference-to-noise ratio (SDNR) were found, the detectability index only matched human performance for subtle targets while detection of target objects that were obviously visible, was underestimated by the model observer. Future work will develop and evaluate other model observers that better predict human performance. The hybrid database is useful for a number of reasons: lesion models are well known, pairs of FFDM and DBT images are available and moreover the images include all system properties without the need for validated simulation software. Potential applications include the investigation of the effect of acquisition parameters, reconstruction algorithms, anatomical structure and displays on lesion detection. In the last chapter we brought these individual elements together and produced clinically relevant targets that were embedded within the spheres-in-water phantom. The targets were generated by scaling voxel models of non-spiculated and spiculated breast tumors to different sizes and 3D printing these model lesions. For the 3D printing, different printing materials were characterized in terms of their x-ray attenuation properties and the material was chosen that gave the lowest contrast against the sphere background while having attenuation properties that most matched those of malignant tissue. A standard approach was used for microcalcification, namely particles of CaCO3. Images of the phantom were acquired on four different DBT systems, using both 2D and DBT modes. Target detectability was assessed via a four-alternative forced choice study and compared between FFDM and DBT. The results supported the clinical findings of studies found in the literature: DBT performed better for the detection of both mass types, while both modalities were equivalent for the detection of microcalcifications. This multi-modality phantom study is the basis for further DBT optimization studies, in terms of reconstruction algorithm, operating dose level and angular range. For example, we have shown that some improvement of the detection of small calcifications is needed. Finally, it is our hope that the proposed phantom will be used routinely by medical physics services in the acceptance and commissioning testing of DBT systems and possibly for quality assurance purposes. For this to become a practical proposition, future research that enables the automated evaluation of target detectability is essential.nrpages: 197status: publishe

Comparative power law analysis of structured breast phantom and patient images in digital mammography and breast tomosynthesis

This work characterizes three candidate mammography phantoms with structured background in terms of power law analysis in the low frequency region of the power spectrum for 2D (planar) mammography and digital breast tomosynthesis (DBT).status: publishe

Virtual study to investigate the detectability of breast abnormalities on 2D mammography and digital breast tomosynthesis

This paper describes the use of virtual clinical trial software, as developed and improved in the frame of the Horizon2020 MaXIMA project, to study particular aspects of 2D mammography and digital breast tomosynthesis. A voxel-based breast phantom with inserted mathematical models of an irregular mass and two microcalcifications was created. Image acquisition was simulated by using XRAYImagingSimulator, while image reconstruction was accomplished with FDKR software. Series of images were created for different angular ranges with an identical total dose. Detectability of the abnormalities was investigated using visual assessment and quantitative measurements. The results agree with other studies in literature studying the same aspects and therefore confirm the value of the new framework for other future applications

Design of a model observer to evaluate calcification detectability in breast tomosynthesis and application to smoothing prior optimization

In this work, the authors design and validate a model observer that can detect groups of microcalcifications in a four-alternative forced choice experiment and use it to optimize a smoothing prior for detectability of microcalcifications.received: 2015-11-19 revised: 2016-09-21 accepted: 2016-10-25 published: 2016-11-21status: publishe

Visual grading analysis of digital neonatal chest phantom X-ray images: Impact of detector type, dose and image processing on image quality

To evaluate the impact of digital detector, dose level and post-processing on neonatal chest phantom X-ray image quality (IQ).status: accepte

Real space channelization for generic DBT system image quality evaluation with channelized Hotelling observer

© 2017 SPIE. Digital breast tomosynthesis (DBT) is a relatively new 3D mammography technique that promises better detection of low contrast masses than conventional 2D mammography. The parameter space for DBT is large however and finding an optimal balance between dose and image quality remains challenging. Given the large number of conditions and images required in optimization studies, the use of human observers (HO) is time consuming and certainly not feasible for the tuning of all degrees of freedom. Our goal was to develop a model observer (MO) that could predict human detectability for clinically relevant details embedded within a newly developed structured phantom for DBT applications. DBT series were acquired on GE SenoClaire 3D, Giotto Class, Fujifilm AMULET Innovality and Philips MicroDose systems at different dose levels, Siemens Inspiration DBT acquisitions were reconstructed with different algorithms, while a larger set of DBT series was acquired on Hologic Dimensions system for first reproducibility testing. A channelized Hotelling observer (CHO) with Gabor channels was developed The parameters of the Gabor channels were tuned on all systems at standard scanning conditions and the candidate that produced the best fit for all systems was chosen. After tuning, the MO was applied to all systems and conditions. Linear regression lines between MO and HO scores were calculated, giving correlation coefficients between 0.87 and 0.99 for all tested conditions.status: publishe

A procedure towards optimised prenatal CT scan protocols for low dose imaging of suspected skeletal dypsplasia

status: Published onlin