Classification of Pulmonary Damage Stages Caused by COVID-19 Disease from CT Scans via Transfer Learning

The COVID-19 pandemic has produced social and economic changes that are still affecting our lives. The coronavirus is proinflammatory, it is replicating, and it is quickly spreading. The most affected organ is the lung, and the evolution of the disease can degenerate very rapidly from the early phase, also known as mild to moderate and even severe stages, where the percentage of recovered patients is very low. Therefore, a fast and automatic method to detect the disease stages for patients who underwent a computer tomography investigation can improve the clinical protocol. Transfer learning is used do tackle this issue, mainly by decreasing the computational time. The dataset is composed of images from public databases from 118 patients and new data from 55 patients collected during the COVID-19 spread in Romania in the spring of 2020. Even if the disease detection by the computerized tomography scans was studied using deep learning algorithms, to our knowledge, there are no studies related to the multiclass classification of the images into pulmonary damage stages. This could be helpful for physicians to automatically establish the disease severity and decide on the proper treatment for patients and any special surveillance, if needed. An evaluation study was completed by considering six different pre-trained CNNs. The results are encouraging, assuring an accuracy of around 87%. The clinical impact is still huge, even if the disease spread and severity are currently diminished

Optical to Planar X-ray Mouse Image Mapping in Preclinical Nuclear Medicine Using Conditional Adversarial Networks

In the current work, a pix2pix conditional generative adversarial network has been evaluated as a potential solution for generating adequately accurate synthesized morphological X-ray images by translating standard photographic images of mice. Such an approach will benefit 2D functional molecular imaging techniques, such as planar radioisotope and/or fluorescence/bioluminescence imaging, by providing high-resolution information for anatomical mapping, but not for diagnosis, using conventional photographic sensors. Planar functional imaging offers an efficient alternative to biodistribution ex vivo studies and/or 3D high-end molecular imaging systems since it can be effectively used to track new tracers and study the accumulation from zero point in time post-injection. The superimposition of functional information with an artificially produced X-ray image may enhance overall image information in such systems without added complexity and cost. The network has been trained in 700 input (photography)/ground truth (X-ray) paired mouse images and evaluated using a test dataset composed of 80 photographic images and 80 ground truth X-ray images. Performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and Fréchet inception distance (FID) were used to quantitatively evaluate the proposed approach in the acquired dataset

Reduction of motion, truncation and flow artifacts using BLADE sequences in cervical spine MR imaging

Purpose To assess the efficacy of the BLADE technique (MR imaging with ‘rotating blade-like k-space covering’) to significantly reduce motion, truncation, flow and other artifacts in cervical spine compared to the conventional technique. Materials and methods In eighty consecutive subjects, who had been routinely scanned for cervical spine examination, the following pairs of sequences were compared: a) T2 TSE SAG vs. T2 TSE SAG BLADE and b) T2 TIRM SAG vs. T2 TIRM SAG BLADE. A quantitative analysis was performed using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measures. A qualitative analysis was also performed by two radiologists, who graded seven image characteristics on a 5-point scale (0: non-visualization; 1: poor; 2: average; 3: good; 4: excellent). The observers also evaluated the presence of image artifacts (motion, truncation, flow, indentation). Results In quantitative analysis, the CNR values of the CSF/SC between TIRM SAG and TIRM SAG BLADE were found to present statistically significant differences (p < 0.001). Regarding motion and truncation artifacts, the T2 TSE BLADE SAG was superior compared to the T2 TSE SAG, and the T2 TIRM BLADE SAG was superior compared to the T2 TIRM SAG. Regarding flow artifacts, T2 TIRM BLADE SAG eliminated more artifacts than T2 TIRM SAG. Conclusions In cervical spine MRI, BLADE sequences appear to significantly reduce motion, truncation and flow artifacts and improve image quality. BLADE sequences are proposed to be used for uncooperative subjects. Nevertheless, more research needs to be done by testing additional specific pathologies