A Computer-Based Cascaded Modeling and Experimental Approach to the Physical Characterization of a Clinical Full-Field Mammography System

This study characterizes the image quality parameters of a clinical full-field digital mammography system at various x-ray spectral conditions. The energy of the incident x-ray beam, the spectral characteristics, and breast thickness impact the physical performance such as the detective quantum efficiency of the system, thereby affecting the overall performance. The modulation transfer function, noise power spectrum were measured without the anti-scatter grid, and the detective quantum efficiency was calculated for different incident x-ray conditions. Detective quantum efficiency was also calculated with the anti-scatter grid placed above the detector to study its impact. Results indicate a substantial drop in the detective quantum efficiency with the anti-scatter grid under certain conditions. It was also determined that detective quantum efficiency decreases as x-ray beam hardening is increased. A spatial frequency-dependent cascaded liner systems model was developed to predict the detective quantum efficiency of the system for different target-filter combinations. This theoretical model is based upon a serial cascade approach in which the system is conceptually divided into a number of discrete stages. Each stage represents a physical process having intrinsic signal and noise transfer properties. A match between the predicted data and the experimental detective quantum efficiency data confirmed the validity of the model. Contrast-detail performance, a widely used quality control tool to assess clinical imaging systems, for the clinical full-field digital mammography was studied using a commercially available CDMAM phantom to learn the effects of Joint Photographic Experts Group 2000 (JPEG2000) compression technique on detectability. A 4-alternative forced choice experiment was conducted. The images were compressed at three different compression ratios (10:1, 20:1 and 30:1). From the contrast-detail curves generated from the observer data at 50% and 75% threshold levels, it was concluded that uncompressed images exhibit lower (better) contrast-detail characteristics than compressed images but a certain limit to compression, without substantial loss of visual quality, can be used

Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

A Digital X-Ray Tomosynthesis Coupled Near Infrared Spectral Tomography System for Dual-Modality Breast Imaging

A Near Infrared Spectral Tomography (NIRST) system has been developed and integrated into a commercial Digital Breast Tomosynthesis (DBT) scanner to allow structural and functional imaging of breast in vivo. The NIRST instrument uses an 8-wavelength continuous wave (CW) laser-based scanning source assembly and a 75-element silicon photodiode solid-state detector panel to produce dense spectral and spatial projection data from which spectrally constrained 3D tomographic images of tissue chromophores are produced. Integration of the optical imaging system into the DBT scanner allows direct co-registration of the optical and DBT images, while also facilitating the synergistic use of x-ray contrast as anatomical priors in optical image reconstruction. Currently, the total scan time for a combined NIRST-DBT exam is ~50s with data collection from 8 wavelengths in the optical scan requiring ~42s to complete. The system was tested in breast simulating phantoms constructed using intralipid and blood in an agarose matrix with a 3 cm x 2 cm cylindrical inclusion at 1 cm depth from the surface. Diffuse image reconstruction of total hemoglobin (HbT) concentration resulted in accurate recovery of the lateral size and position of the inclusion to within 6% and 8%, respectively. Use of DBT structural priors in the NIRST reconstruction process improved the quantitative accuracy of the HbT recovery, and led to linear changes in imaged versus actual contrast, underscoring the advantages of dual-modality optical imaging approaches. The quantitative accuracy of the system can be further improved with independent measurements of scattering properties through integration of frequency or time domain data

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <+/-3%; dose <+/-20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.Comment: Accepted in Medical Image Analysi

A Prototype Computational Phantom to Create Digital Images for Research and Training in Diagnostic Radiology

Research in the processing, compression, transmission, and interpretation of digital radiographic images require the testing and evaluation of a wide variety of images, varying both in format and in spatial resolution. If receiver operating characteristic (ROC) analysis or a related method is used to evaluate the performance of observers using novel vs. conventional displays, large numbers of test images containing known abnormalities are required. This report describes a convenient, inexpensive, and reproducible source of test images, having any desired resolution and containing precisely defined abnormalities of unlimited subtlety. The images are generated by computing x-ray transmission through mathematically defined, three dimensional masses according to Beer\u27s Law. A procedure is presented for generating computer simulated chest radiographs and mammograms, which can contain various classes of abnormalities, including tumors, infiltrates, cavities, pleural effusions, cardiac chamber enlargement, and soft tissue calcifications. Test images can be created from simple computational models of superimposed spherical densities. The approach provides a flexible, inexpensive, easy-to-use research tool for investigators exploring digital techniques in diagnostic radiology. Such simulation software may also be of benefit as a training tool, when employed to generate numerous test images containing subtle abnormalities for programmed instruction and testing

Exploring the visualisation of the cervicothoracic junction in lateral spine radiography using high dynamic range techniques

The C7/T1 junction is an important landmark for spinal injuries. It is traditionally difficult to visualise in a lateral X-ray image due to the rapid change in the bodys anatomy at the level of the junction, where the shoulders cause a large increase in attenuation. To explore methods of enhancing the appearance of this important area, lateral radiographs of a shoulder girdle phantom were subjected to high dynamic range (HDR) processing and tone mapping. A shoulder girdle phantom was constructed using Perspex, shoulder girdle and vertebral bones and water to reproduce the attenuation caused by soft tissue. The design allowed for the removal of the shoulder girdle in order for the cervical vertebrae to be imaged separately. HDR was explored for single and dual-energy X-ray images of the phantom. In the case of single-image HDR, the HDR image of the phantom without water was constructed by combining images created with varying contrast windows throughout the contrast range of an X-ray image. It was found that an overlap of larger contrast windows with a lower number of images performed better than smaller contrast windows and more images when creating an HDR to be tone mapped. Poor results on the phantom without water precluded further testing of single-image HDR on images of the phantom with water, which would have higher attenuation. Dual energy HDR image construction explored images of the phantom both with and without water. A set of images acquired at lower attenuation (phantom without water) was used to evaluate the performance of the various tone mapping algorithms. The tone mapping was then performed on the phantom images containing water. These results showed how each tone mapping algorithm differs and the effects of global vs. local processing. The results revealed that the built-in MatLab algorithm, based on an improved Ward histogram adjustment approach, produces the most desirable result. None of the HDR tone mapped images produced were diagnostically useful. Signal to noise ratio (SNR) analysis was performed on the cervical region of the HDR tone mapped image. It used the scan of the phantom without the shoulder girdle obstruction (imaged under the same conditions) as a reference image. The SNR results quantitatively show that the selection of exposure values affects the visualisation of the tone mapped image. The highest SNR was produced for the 100 - 120 kV dual energy X-ray image pair. The study was limited by the range of HDR image construction techniques employed and the tone mapping algorithms explored. Future studies could explore other HDR image construction techniques and the combination of global and local tone mapping algorithms. Furthermore, the phantom can be replaced by a cadaver for algorithm testing under more realistic conditions