Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

Breast dosimetry in alternative X-ray-based imaging modalities used in current clinical practices

In X-ray breast imaging, Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT), are the standard and largely used techniques, both for diagnostic and screening purposes. Other techniques, such as dedicated Breast Computed Tomography (BCT) and Contrast Enhanced Mammography (CEM) have been developed as an alternative or a complementary technique to the established ones. The performance of these imaging techniques is being continuously assessed to improve the image quality and to reduce the radiation dose. These imaging modalities are predominantly used in the diagnostic setting to resolve incomplete or indeterminate findings detected with conventional screening examinations and could potentially be used either as an adjunct or as a primary screening tool in select populations, such as for women with dense breasts. The aim of this review is to describe the radiation dosimetry for these imaging techniques, and to compare the mean glandular dose with standard breast imaging modalities, such as DM and DBT.

Development of a dynamic imaging method for gravitropism in pea sprouts using clinical magnetic resonance imaging system

Although magnetic resonance imaging (MRI) is a useful technique, only a few studies have investigated the dynamic behavior of small subjects using MRI owing to constraints such as experimental space and signal amount. In this study, to acquire high-resolution continuous three-dimensional gravitropism data of pea (Pisum sativum) sprouts, we developed a small-bore MRI signal receiver coil that can be used in a clinical MRI and adjusted the imaging sequence. It was expected that such an arrangement would improve signal sensitivity and improve the signal-to-noise ratio (SNR) of the acquired image. All MRI experiments were performed using a 3.0-T clinical MRI scanner. An SNR comparison using an agarose gel phantom to confirm the improved performance of the small-bore receiver coil and an imaging experiment of pea sprouts exhibiting gravitropism were performed. The SNRs of the images acquired with a standard 32-channel head coil and the new small-bore receiver coil were 5.23±0.90 and 57.75±12.53, respectively. The SNR of the images recorded using the new coil was approximately 11-fold higher than that of the standard coil. In addition, when the accuracy of MR imaging that captures the movement of pea sprout was verified, the difference in position information from the optical image was found to be small and could be used for measurements. These results of this study enable the application of a clinical MRI system for dynamic plant MRI. We believe that this study is a significant first step in the development of plant MRI technique.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.