The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of hete...The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of heterogeneous reservoir cores relies heavily on high-resolution CT scanning images.However,this approach requires a considerable amount of data and is associated with high costs.To solve this problem,a method for predicting core permeability based on deep learning using CT scan images with diff erent resolutions is proposed in this work.First,the high-resolution CT scans are preprocessed and then cubic subsets are extracted.The permeability of each subset is estimated using the Lattice Boltzmann Method(LBM)and forms the training set for the convolutional neural network(CNN)model.Subsequently,the highresolution images are downsampled to obtain the low-resolution grayscale images.In the comparative analysis of the porosities of diff erent low-resolution images,the low-resolution image with a resolution of 10%of the original image is considered as the test set in this paper.It is found that the permeabilities predicted from the low-resolution images are in good agreement with the values calculated by the LBM.In addition,the test data are compared with the results of the Kozeny-Carman(KC)model and the measured permeability of the whole sample.The results show that the prediction of the permeability of tight carbonate rock based on deep learning using CT scans with diff erent resolutions is reliable.展开更多
Background: Among medical technologies that use ionizing radiation, CT is currently the radio diagnostic technic that can deliver the highest radiation to the Patient compared with other conventional procedures. In de...Background: Among medical technologies that use ionizing radiation, CT is currently the radio diagnostic technic that can deliver the highest radiation to the Patient compared with other conventional procedures. In developing countries, the uses and risks of CT have not been well characterized. Objective: To estimate the lifetime attributable risk (LAR) incidence and mortality for cancer for each procedure for adult’s patients who had Computed Tomography examinations in 10 imaging centers in the city of Douala-Cameroon so as to provide a reference data. Materials and Methods: We conducted a cross-sectional study describing radiation dose associated with the 8 most common types of diagnostic CT studies performed on 1287 consecutive adult patients at 10 Douala radiology department. We estimated lifetime attributable risks of cancer by study type from these measured doses. Estimation of LAR for cancer incidence and mortality was based on the effective dose, patient’s sex and age at exposure using the BIER VII preferred models. Results: Mean effective dose from CT scans examinations varied from: 0.30 and 8.81 mSv. The highest doses were observed for lumbar spine CT (8.81 mSv), followed by abdomen-pelvis procedure (6.46 mSv), chest-abdomen-pelvic CT (6.61 mSv), chest CT (3.90 mSv), cervical Spine CT (3.05 mSv), head CT (1.7 mSv) and lower for sinus CT (0.30 mSv). The LAR values of all cancer from patients’ CT scans obtained vary from 67.13 excess per 100,000 (about 1 in 1489) and 0.45 excess per 100,000 (about 1 in 222,222). All cancer risk was high for lumbar spine CT in women 20 years old (67.13 excess deaths in 100,000 scans) followed by chest-abdomen-pelvic CT (50.36 excess deaths in 100,000 scans) and abdomen-pelvic CT (49.22 excess deaths in 100,000 scans) for the same age group. The LAR of incidence and mortality values were higher from female’s patients than males and higher for younger than older patients. Conclusion: This study was set out to estimate the LAR values associated with adult common CT scans procedures. The data indicates, LAR risks related to induced cancer from CT exposures were estimated to be low. This risk can be relatively significant for younger age group compared to older age group. The LAR values obtained will help to better evaluate radiation exposure risk, before ordering a CT scans examinations.展开更多
文摘The low-resolution CT scan images obtained from drill core generally struggle with problems such as insufficient pore structure information and incomplete image details.Consequently,predicting the permeability of heterogeneous reservoir cores relies heavily on high-resolution CT scanning images.However,this approach requires a considerable amount of data and is associated with high costs.To solve this problem,a method for predicting core permeability based on deep learning using CT scan images with diff erent resolutions is proposed in this work.First,the high-resolution CT scans are preprocessed and then cubic subsets are extracted.The permeability of each subset is estimated using the Lattice Boltzmann Method(LBM)and forms the training set for the convolutional neural network(CNN)model.Subsequently,the highresolution images are downsampled to obtain the low-resolution grayscale images.In the comparative analysis of the porosities of diff erent low-resolution images,the low-resolution image with a resolution of 10%of the original image is considered as the test set in this paper.It is found that the permeabilities predicted from the low-resolution images are in good agreement with the values calculated by the LBM.In addition,the test data are compared with the results of the Kozeny-Carman(KC)model and the measured permeability of the whole sample.The results show that the prediction of the permeability of tight carbonate rock based on deep learning using CT scans with diff erent resolutions is reliable.
文摘Background: Among medical technologies that use ionizing radiation, CT is currently the radio diagnostic technic that can deliver the highest radiation to the Patient compared with other conventional procedures. In developing countries, the uses and risks of CT have not been well characterized. Objective: To estimate the lifetime attributable risk (LAR) incidence and mortality for cancer for each procedure for adult’s patients who had Computed Tomography examinations in 10 imaging centers in the city of Douala-Cameroon so as to provide a reference data. Materials and Methods: We conducted a cross-sectional study describing radiation dose associated with the 8 most common types of diagnostic CT studies performed on 1287 consecutive adult patients at 10 Douala radiology department. We estimated lifetime attributable risks of cancer by study type from these measured doses. Estimation of LAR for cancer incidence and mortality was based on the effective dose, patient’s sex and age at exposure using the BIER VII preferred models. Results: Mean effective dose from CT scans examinations varied from: 0.30 and 8.81 mSv. The highest doses were observed for lumbar spine CT (8.81 mSv), followed by abdomen-pelvis procedure (6.46 mSv), chest-abdomen-pelvic CT (6.61 mSv), chest CT (3.90 mSv), cervical Spine CT (3.05 mSv), head CT (1.7 mSv) and lower for sinus CT (0.30 mSv). The LAR values of all cancer from patients’ CT scans obtained vary from 67.13 excess per 100,000 (about 1 in 1489) and 0.45 excess per 100,000 (about 1 in 222,222). All cancer risk was high for lumbar spine CT in women 20 years old (67.13 excess deaths in 100,000 scans) followed by chest-abdomen-pelvic CT (50.36 excess deaths in 100,000 scans) and abdomen-pelvic CT (49.22 excess deaths in 100,000 scans) for the same age group. The LAR of incidence and mortality values were higher from female’s patients than males and higher for younger than older patients. Conclusion: This study was set out to estimate the LAR values associated with adult common CT scans procedures. The data indicates, LAR risks related to induced cancer from CT exposures were estimated to be low. This risk can be relatively significant for younger age group compared to older age group. The LAR values obtained will help to better evaluate radiation exposure risk, before ordering a CT scans examinations.
基金the National Natural Science Foundation of China(5117214451372153)+2 种基金Science Technology Foundation of shanghai(12410710300)Leading Academic Discipline Project of Shanghai Municipal Education Commission(J51503)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning
