In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans...In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.展开更多
In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the co...In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.展开更多
AIM:To explore whether computer tomography coronary angiography(CTCA) using iterative reconstruction(IR) leads to significant radiation dose reduction without a significant loss in image interpretability compared to c...AIM:To explore whether computer tomography coronary angiography(CTCA) using iterative reconstruction(IR) leads to significant radiation dose reduction without a significant loss in image interpretability compared to conventional filtered back projection(FBP).METHODS:A consecutive series of 200 patients referred to our institution to undergo CTCA constituted the study population.Patients were sequentially assigned to FBP or IR.All studies were acquired with a 256-slice CT scanner.A coronary segment was considered interpretable if image quality was adequate for evaluation of coronary lesions in all segments ≥ 1.5 mm.RESULTS:The mean age was 56.3±9.6 years and165(83%) were male,with no significant differences between groups.Most scans were acquired using prospective ECG triggering,without differences between groups(FBP 84%vs IR 82%;P=0.71).A total of 3198(94%) coronary segments were deemed of diagnostic quality.The percent assessable coronary segments was similar between groups(FBP 91.7%±4.0% vs IR92.5% ± 2.8%; P=0.12).Radiation dose was significantly lower in the IR group(2.8±1.4 mSvvs 4.6±3.0mSv;P<0.0001).Image noise(37.8±1.4 HUvs 38.2±2.4 HU; P=0.20) and signal density(461.7±51.9HU vs 462.2±51.2 HU; P=0.54) levels did not differ between FBP and IR groups,respectively.The IR group was associated to significant effective dose reductions,irrespective of the acquisition mode.CONCLUSION:Application of IR in CTCA preserves image interpretability despite a significant reduction in radiation dose.展开更多
Purpose: The purpose of this study has been to evaluate the diagnostic information contained in the CT scout view in the detection of body packing. Materials and methods: Retrospect analysis of 43 CT scans between Jul...Purpose: The purpose of this study has been to evaluate the diagnostic information contained in the CT scout view in the detection of body packing. Materials and methods: Retrospect analysis of 43 CT scans between July 2011 and June 2013 in asymptomatic suspects of body packing (29 men, 14 females, mean age 38 ± 9 years). Results: A total of 11 positive cases of body packing were identified. In 10 (91%) of the cases packets were relatively large and spares in number (3 or less);in 7 (64%) a single packet has been identified. 6 (55%) of the packets were located rectally, 4 (36%) vaginally and in 1 (9%) case multiple small packets of approximately 1 cm in size were found to have been ingested orally. Maximum and minimum diameters were 5.9 ± 3 cm and 2.9 ± 1.4 cm, respectively. The mean weight of packets was 7.5 ± 4.2 g (range 2 - 54 g). In 73% (n = 8) heroin had been detected;other drugs such as cocaine (n = 1) and cannabis (n = 1) were encountered once, respectively. One packet was identified retrospectively and its content could therefore not be identified. The average effective dose was 3.8 ± 2.1 mSv for CT, of that 0.12 ± 0.01 mSv was required for the CT scout view. Conclusion: If CT scout view were treated as a diagnostic image, some CT scans may be omitted, thereby maintaining streamlined operations and achieving further dose reduction jointly in the workup of body packing.展开更多
Objective:To explore and analyze the clinical effect of low-dose Betaloc combined with amiodarone in treating ventricular arrhythmia.Methods:70 patients with ventricular arrhythmia who were admitted to the Department ...Objective:To explore and analyze the clinical effect of low-dose Betaloc combined with amiodarone in treating ventricular arrhythmia.Methods:70 patients with ventricular arrhythmia who were admitted to the Department of Cardiology of our hospital between August 2022 and August 2023 were selected as research subjects.They were divided into two groups using the coin-tossing method:the combination group(n=35)and the reference group(n=35).The combination group was treated with low-dose Betaloc and amiodarone,and the control group was treated with low-dose Betaloc alone.The treatment efficacy,cardiac function indicators,and related tested indicators of the two groups were compared.Results:The total efficacy of the treatment received by the combination group was much higher than that of the control group(P<0.05).Besides,after treatment,the cardiac function indicators such as left ventricular ejection fraction(LVEF),left ventricular end-systolic volume(LVESV),and cardiac index(CI)of the patients in the combination group were significantly better than those of the reference group(P<0.05).Furthermore,the high-sensitivity C-reactive protein(Hs-CRP),N-terminal prohormone of brain natriuretic peptide(NT-proBNP),adiponectin(APN),and other related test indicators of the patients in the combination group were significantly better than those of the reference group(P<0.05).Conclusion:Low-dose Betaloc combined with amiodarone has a noticeable effect in treating ventricular arrhythmia and deserves to be widely promoted.展开更多
BACKGROUND The recognition of idiopathic membranous nephropathy(IMN)as an autoimmune disease has paved the way for the use of B-cell-depleting agents,such as Rituximab(RTX),which is now a first-line drug for treating ...BACKGROUND The recognition of idiopathic membranous nephropathy(IMN)as an autoimmune disease has paved the way for the use of B-cell-depleting agents,such as Rituximab(RTX),which is now a first-line drug for treating IMN with proven safety and efficacy.Nevertheless,the usage of RTX for the treatment of refractory IMN remains controversial and challenging.AIM To evaluate the efficacy and safety of a new low-dose RTX regimen for the treatment of patients with refractory IMN.METHODS A retrospective study was performed on refractory IMN patients that accepted a low-dose RTX regimen(RTX,200 mg,once a month for five months)in the Xiyuan Hospital of Chinese Academy of Chinese Medical Sciences’Department of Nephrology from October 2019 to December 2021.To assess the clinical and immune remission data,we performed a 24 h urinary protein quantification(UTP)test and measured the serum albumin(ALB)and serum creatinine(SCr)levels,phospholipase A2 receptor(PLA2R)antibody titer,and CD19+B-cell count every three months.RESULTS A total of nine refractory IMN patients were analyzed.During follow-up conducted twelve months later,the results from the 24 h UTP decreased from baseline[8.14±6.05 g/d to 1.24±1.34 g/d(P<0.05)]and the ALB levels increased from baseline[28.06±8.42 g/L to 40.93±5.85 g/L(P<0.01)].Notably,after administering RTX for six months,the SCr decreased from 78.13±16.49μmol/L to 109.67±40.87μmol/L(P<0.05).All of the nine patients were positive for serum anti-PLA2R at the beginning,and four patients had normal anti-PLA2R titer levels at six months.The level of CD19+B-cells decreased to 0 at three months,and CD19+B-cell count remained at 0 up until six months of follow-up.CONCLUSION Our low-dose RTX regimen appears to be a promising treatment strategy for refractory IMN.展开更多
Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung...Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening.However,LDCT images suffer from severe noise,largely influencing the performance of lung nodule classification.Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using LDCT.To jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input images.More specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images anymore.The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification.Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision.Moreover,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.展开更多
基金supported by the National Natural Science Foundation of China(Nos.11975292,12222512)the CAS"Light of West Chin"Program+1 种基金the CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)。
文摘In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.
基金supported by National Natural Science Foundation ofChina (61672279)Project of “Six Talents Peak” in Jiangsu (2012-WLW-023)OpenFoundation of State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, Nanjing Hydraulic Research Institute, China (2016491411).
文摘In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.
文摘AIM:To explore whether computer tomography coronary angiography(CTCA) using iterative reconstruction(IR) leads to significant radiation dose reduction without a significant loss in image interpretability compared to conventional filtered back projection(FBP).METHODS:A consecutive series of 200 patients referred to our institution to undergo CTCA constituted the study population.Patients were sequentially assigned to FBP or IR.All studies were acquired with a 256-slice CT scanner.A coronary segment was considered interpretable if image quality was adequate for evaluation of coronary lesions in all segments ≥ 1.5 mm.RESULTS:The mean age was 56.3±9.6 years and165(83%) were male,with no significant differences between groups.Most scans were acquired using prospective ECG triggering,without differences between groups(FBP 84%vs IR 82%;P=0.71).A total of 3198(94%) coronary segments were deemed of diagnostic quality.The percent assessable coronary segments was similar between groups(FBP 91.7%±4.0% vs IR92.5% ± 2.8%; P=0.12).Radiation dose was significantly lower in the IR group(2.8±1.4 mSvvs 4.6±3.0mSv;P<0.0001).Image noise(37.8±1.4 HUvs 38.2±2.4 HU; P=0.20) and signal density(461.7±51.9HU vs 462.2±51.2 HU; P=0.54) levels did not differ between FBP and IR groups,respectively.The IR group was associated to significant effective dose reductions,irrespective of the acquisition mode.CONCLUSION:Application of IR in CTCA preserves image interpretability despite a significant reduction in radiation dose.
文摘Purpose: The purpose of this study has been to evaluate the diagnostic information contained in the CT scout view in the detection of body packing. Materials and methods: Retrospect analysis of 43 CT scans between July 2011 and June 2013 in asymptomatic suspects of body packing (29 men, 14 females, mean age 38 ± 9 years). Results: A total of 11 positive cases of body packing were identified. In 10 (91%) of the cases packets were relatively large and spares in number (3 or less);in 7 (64%) a single packet has been identified. 6 (55%) of the packets were located rectally, 4 (36%) vaginally and in 1 (9%) case multiple small packets of approximately 1 cm in size were found to have been ingested orally. Maximum and minimum diameters were 5.9 ± 3 cm and 2.9 ± 1.4 cm, respectively. The mean weight of packets was 7.5 ± 4.2 g (range 2 - 54 g). In 73% (n = 8) heroin had been detected;other drugs such as cocaine (n = 1) and cannabis (n = 1) were encountered once, respectively. One packet was identified retrospectively and its content could therefore not be identified. The average effective dose was 3.8 ± 2.1 mSv for CT, of that 0.12 ± 0.01 mSv was required for the CT scout view. Conclusion: If CT scout view were treated as a diagnostic image, some CT scans may be omitted, thereby maintaining streamlined operations and achieving further dose reduction jointly in the workup of body packing.
文摘Objective:To explore and analyze the clinical effect of low-dose Betaloc combined with amiodarone in treating ventricular arrhythmia.Methods:70 patients with ventricular arrhythmia who were admitted to the Department of Cardiology of our hospital between August 2022 and August 2023 were selected as research subjects.They were divided into two groups using the coin-tossing method:the combination group(n=35)and the reference group(n=35).The combination group was treated with low-dose Betaloc and amiodarone,and the control group was treated with low-dose Betaloc alone.The treatment efficacy,cardiac function indicators,and related tested indicators of the two groups were compared.Results:The total efficacy of the treatment received by the combination group was much higher than that of the control group(P<0.05).Besides,after treatment,the cardiac function indicators such as left ventricular ejection fraction(LVEF),left ventricular end-systolic volume(LVESV),and cardiac index(CI)of the patients in the combination group were significantly better than those of the reference group(P<0.05).Furthermore,the high-sensitivity C-reactive protein(Hs-CRP),N-terminal prohormone of brain natriuretic peptide(NT-proBNP),adiponectin(APN),and other related test indicators of the patients in the combination group were significantly better than those of the reference group(P<0.05).Conclusion:Low-dose Betaloc combined with amiodarone has a noticeable effect in treating ventricular arrhythmia and deserves to be widely promoted.
基金Supported by National Key Research and Development Program of China,No.2019YFC1708503。
文摘BACKGROUND The recognition of idiopathic membranous nephropathy(IMN)as an autoimmune disease has paved the way for the use of B-cell-depleting agents,such as Rituximab(RTX),which is now a first-line drug for treating IMN with proven safety and efficacy.Nevertheless,the usage of RTX for the treatment of refractory IMN remains controversial and challenging.AIM To evaluate the efficacy and safety of a new low-dose RTX regimen for the treatment of patients with refractory IMN.METHODS A retrospective study was performed on refractory IMN patients that accepted a low-dose RTX regimen(RTX,200 mg,once a month for five months)in the Xiyuan Hospital of Chinese Academy of Chinese Medical Sciences’Department of Nephrology from October 2019 to December 2021.To assess the clinical and immune remission data,we performed a 24 h urinary protein quantification(UTP)test and measured the serum albumin(ALB)and serum creatinine(SCr)levels,phospholipase A2 receptor(PLA2R)antibody titer,and CD19+B-cell count every three months.RESULTS A total of nine refractory IMN patients were analyzed.During follow-up conducted twelve months later,the results from the 24 h UTP decreased from baseline[8.14±6.05 g/d to 1.24±1.34 g/d(P<0.05)]and the ALB levels increased from baseline[28.06±8.42 g/L to 40.93±5.85 g/L(P<0.01)].Notably,after administering RTX for six months,the SCr decreased from 78.13±16.49μmol/L to 109.67±40.87μmol/L(P<0.05).All of the nine patients were positive for serum anti-PLA2R at the beginning,and four patients had normal anti-PLA2R titer levels at six months.The level of CD19+B-cells decreased to 0 at three months,and CD19+B-cell count remained at 0 up until six months of follow-up.CONCLUSION Our low-dose RTX regimen appears to be a promising treatment strategy for refractory IMN.
基金supported in part by National Natural Science Foundation of China(No.62101136)Shanghai Municipal of Science and Technology Project(No.20JC1419500)+3 种基金Shanghai Sailing Program(No.21YF1402800)the Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01)ZJLab,Shanghai Center for Brain Science and Brain-Inspired Technology,the National Key R&D Program of China(No.2018YFB1305104)the Natural Science Foundation of Shanghai(No.21ZR1403600).
文摘Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening.However,LDCT images suffer from severe noise,largely influencing the performance of lung nodule classification.Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using LDCT.To jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input images.More specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images anymore.The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification.Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision.Moreover,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.
