Expanding bullets and ballistic gelatin - A restricted expansion experiment.

Ballistic gelatin has gained a status as standard method for terminal ballistic testing and experimenting. Variation considering the recipe and manufacturing of the blocks exists. The golden standard has been a cuboid gelatin block, dimensions varying according to the type and kinetic energy of the ammunition. Powerful ammunition requires larger gelatin blocks, making their handling and manufacturing difficult. This is the case especially with powerful, expanding hunting ammunition that leave most of their kinetic energy within the gelatin block. High speed cameras reveal that blocks tend to expand or swell significantly and even travel upon impact, potentially affecting to some basic values of terminal ballistics such as cavitation and energy transfer. In this study, we wanted to experiment new method to study terminal ballistics of high power, expanding ammunition by using cylinder shaped gelatin blocks. Secondly, we used a plastic tube around the gelatin cylinder to restrict the expansion/swelling. Thirdly we attached our gelatin target to a sturdy platform to restrict the movement of the cylinder and potentially improve the energy transfer of the bullet into the gelatin. To conduct our study we compared our experimental setting with a traditional, cuboid gelatin block. After the test firing the blocks underwent computed tomography scanning with clinical equipment. Three-dimensional reconstructions of gelatin cavitation and bullet fragment deposition were created. Our results clearly demonstrate that the restricted expansion of the block also clearly restricts the cavitation inside the gelatin. We believe that the method can be further developed, and it allows better potential for ballistic testing of heavy ammunition. In addition, it may aid in terminal ballistic reconstruction of forensic cases with gunshot trauma in anatomical structures fully enclosed by connective tissue such as brain and structures of the thorax.

Three-dimensional visualization of gunshot cavities in ballistic gelatine with computed tomography - A forensic ballistics case study.

Three-dimensional (3D) imaging, primarily computed tomography (CT), has proven valuable in the documentation and analysis of gunshot injuries. Explicit visualization of findings may play a pivotal role in judicial settings. This forensic ballistics case study aimed to examine the potential of CT-based 3D reconstruction to digitally visualize gunshot cavities in ballistic gelatine. Three .30 caliber bullets of different types (full metal jacket, soft point, and expanding monolithic) were fired into standardized blocks of 10% ballistic gelatine. The blocks underwent CT scanning with clinical equipment. Gelatine and air were segmented from the CT data using an open-source software. 3D reconstruction views of the segmented gelatine and air components were created. The gunshot cavities were clearly observed in both gelatine and air segmentation. The differences in cavitation between bullet types were evident in both reconstruction approaches, although gelatine segmentation produced higher resolution of small details. The obvious benefit of digital reconstruction was the ability to freely tilt and rotate the 3D images, with the possibility of taking measurements manually or automatically from any plane. Moreover, all the data can be stored for future analysis. This study introduces a preliminary method for digital visualization and documentation of gunshot cavitation in ballistic gelatine, to be fine-tuned and implemented for research purposes and routine practice in forensic institutions.

Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine.

PURPOSE: Artificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has faced a need to enhance MRI scanner throughput, and we investigate the utility of new commercial deep learning reconstruction (DLR) algorithm for this purpose. In this work, a multidisciplinary team evaluated the impact of the widespread deployment of a new commercial deep learning reconstruction (DLR) algorithm for our magnetic resonance imaging scanner fleet. METHODS: Our analysis centers on the DLR algorithm's effects on patient throughput and investment costs, contrasting these with alternative strategies for capacity expansion-namely, acquiring additional MRI scanners and increasing device utilization on weekends. We provide a framework for assessing the financial implications of new technologies in a trial phase, aiding in informed decision-making for healthcare investments. RESULTS: We demonstrate substantial reductions in total operating costs compared to other capacity-enhancing methods. Specifically, the cost of adopting the deep learning technology for our entire scanner fleet is only 11 % compared to procuring an additional scanner and 20 % compared to the weekend utilization costs of existing devices. CONCLUSIONS: Procuring DLR for our existing five-scanner fleet allows us to sustain our current MRI service levels without the need for an additional scanner, thereby achieving considerable cost savings. These reductions highlight the efficiency and economic viability of DLR in optimizing MRI service delivery.

Observing the fragmentation of two expanding bullet types and a full metal-jacketed bullet with computed tomography-a forensic ballistics case study.

Computed tomography (CT) may have a crucial role in the forensic documentation and analysis of firearm injuries. The aim of this forensic ballistics case study was to explore whether two types of expanding bullets and a full metal-jacketed bullet could be differentiated by inspecting bullet fragments and fragmentation pattern in CT. Three types of .30 caliber bullets (full metal-jacketed Norma Jaktmatch, expanding full-copper Norma Ecostrike, and expanding soft-point Norma Oryx) were test fired from a distance of 5 m to blocks of 10% ballistic gelatine. CT scans of the blocks were obtained with clinical equipment and metal artifact reduction. Radiopaque fragments were identified and fragmentation parameters were obtained from the scans (total number of fragments, maximum diameter of the largest fragment, distance between entrance and the closest fragment, length of the fragment cloud, and maximum diameters of the fragment cloud). The fragmentation patterns were additionally visualized by means of 3D reconstruction. In CT, the bullet types differed in several fragmentation parameters. While the expanding full-copper bullet Ecostrike left behind only a single fragment near the end of the bullet channel, the soft-point Oryx had hundreds of fragments deposited throughout the channel. For both expanding bullets Ecostrike and Oryx, the fragments were clearly smaller than those left behind by the full metal-jacketed Jaktmatch. This was surprising as the full metal-jacketed bullet was expected to remain intact. The fragment cloud of Jaktmatch had similar mediolateral and superoinferior diameters to that of Oryx; however, fragments were deposited in the second half of the gelatine block, and not throughout the block. This case study provides a basis and potential methodology for further experiments. The findings are expected to benefit forensic practitioners with limited background information on gunshot injury cases, for example, those that involve several potential firearms or atypical gunshot wounds. The findings may prove beneficial for both human and wildlife forensics.

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications.

PURPOSE: The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods. METHODS: Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses. RESULTS: The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods. CONCLUSION: Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.