OBJECTIVE: This study aims to evaluate efficacy of using a bismuth breast shield and optimized scanning parameter to reduce breast absorbed doses from CT thorax examination.
METHODS: Five protocols comprising the standard CT thorax clinical protocol (CP1) and four modified protocols (CP2 to CP5) were applied in anthropomorphic phantom scans. The phantom was configured as a female by placing a breast component on the chest. The breast component was divided into four quadrants, where 2 thermoluminescence dosimeters (TLD-100) were inserted into each quadrant to measure the absorbed dose. The bismuth shield was placed over the breast component during CP4 and CP5 scans.
RESULTS: The pattern of absorbed doses in each breast and quadrant were approximately the same for all protocols, where the 4th quadrant > 3rd quadrant > 2nd quadrant > 1st quadrant. The mean absorbed dose value in CP3 was reduced to almost 34% of CP1's mean absorbed dose. It was reduced even lower to 15% of CP1's mean absorbed dose when the breast shield was used in CP5.
CONCLUSION: This study showed that CT radiation exposure on the breast could be reduced by using a bismuth shield and low tube potential protocol without compromising the image quality.
OBJECTIVE: This study aims to determine the bone protective effects of the standardized quassinoid-rich EL extract in testosterone-deficient rat model.
METHODS: Ninety-six intact male Sprague-Dawley rats were randomized into baseline, sham, orchidectomized, and chemically castrated groups. Chemical castration was performed via subcutaneous injection of degarelix at 2 mg/kg. The orchidectomized and degarelix-induced rats were administered with vehicle, intramuscularly injected with testosterone once a week, or orally supplemented with EL extract at doses of 25 mg/kg, 50 mg/kg or 100 mg/kg daily for 10 weeks. Bone mass, microarchitecture and strength were analyzed by dual-energy x-ray absorptiometry (DEXA), micro-CT and three-point bending test.
RESULTS: Whole body bone mineral density and femoral bone mineral content significantly increased in testosterone groups (p < 0.05). Micro-CT analysis revealed that trabecular bone volume, number, separation and connectivity density were significantly improved by testosterone administration. However, the structural model index was only improved in degarelix group supplemented with 100 mg/kg EL extract (P < 0.05). The improvement of cortical thickness by EL extract was similar to that of testosterone groups (p < 0.05). Biomechanically, EL extract supplementation was able to improve stiffness, strain and modulus of elasticity in degarelix-induced groups, while stress parameter was significantly improved in orchidectomized groups (p < 0.05).
CONCLUSION: Quassinoid-rich EL extract enables to protect against bone loss due to testosterone deficiency. The protective effect on cortical thickness and biomechanical parameters is comparable to testosterone group.
METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol) and SNR2 divided by the size-specific dose estimates (SSDE).
RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p
METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit.
RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful.
CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.