ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.

Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.

Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting.

Development of a simple numerical model for trabecular bone structures.

PURPOSE: Advances in additive manufacturing processes are enabling the fabrication of surrogate bone structures for applications including use in high-resolution anthropomorphic phantoms. In this research, a simple numerical model is proposed that enables the generation of microarchitecture with similar statistical distribution to trabecular bone. METHODS: A human humerus, radius, ulna, and several vertebrae were scanned on the Imaging and Medical beamline at the Australian Synchrotron and the proposed numerical model was developed through the definition of two complex functions that encode the trabecular thickness and position-dependant spacing to generate volumetric surrogate trabecular structures. The structures reproduced those observed at 19 separate axial locations through the experimental bone volumes. The applicability of the model when incorporating a two-material approximation to absorption- and phase-contrast CT was also investigated through simulation. RESULTS: The synthetic structures, when compared with the real trabecular microarchitecture, yielded an average mean thickness error of 2 µm, and a mean difference in standard deviation of 33 µm for the humerus, 24 µm for the ulna and radius, and 15 µm for the vertebrae. Simulated absorption- and propagation-based phase contrast CT projection data were generated and reconstructed using the derived mathematical simplifications from the two-material approximation, and the phase-contrast effects were successfully demonstrated. CONCLUSIONS: The presented model reproduced trabecular distributions that could be used to generate phantoms for quality assurance and validation processes. The implication of utilizing a two-material approximation results in simplification of the additive manufacturing process and the generation of synthetic data that could be used for training of machine learning applications.

Effect of external fixation rod coupling in computed tomography.

External fixation is a common tool in the treatment of complex fractures, correction of limb deformity, and salvage arthrodesis. These devices typically incorporate radio-opaque metal rods/struts connected at varying distances and orientations between rings. Whilst the predominant imaging modality is plain film radiology, computed tomography (CT) may be performed in order for the surgeon to make a more confident clinical decision (e.g. timing of frame removal, assessment of degree of arthrodesis). We used a fractured sheep leg to systematically assess CT imaging performance with a Discovery CT750 HD CT scanner (GE Healthcare) to show how rod coupling in both traditional Ilizarov and hexapod frames distorts images. We also investigated the role of dual-energy CT (DECT) and metal artefact reduction software (MARS) on the visualisation of the fractured leg. Whilst mechanical reasons predominantly dictate the rod/strut configurations when building a circular frame, rod coupling in CT can be minimised. Firstly, ideally, all or all but one rod can be removed during imaging resulting in no rod coupling. If this is not possible, strategies for configuring the rods to minimise the effect of the rod coupling on the region of interest are demonstrated, e.g., in the case of a four-rod construct, switching the two anterior rods to a more central single one will achieve this goal without particularly jeopardising mechanical strength for a short period. It is also shown that the addition of DECT and MARS results in a reduction of artefacts, but also affects tissue and bone differentiation.