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  • 1
    Online Resource
    Online Resource
    IOS Press ; 2023
    In:  Journal of X-Ray Science and Technology Vol. 31, No. 2 ( 2023-03-15), p. 319-336
    In: Journal of X-Ray Science and Technology, IOS Press, Vol. 31, No. 2 ( 2023-03-15), p. 319-336
    Abstract: BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.
    Type of Medium: Online Resource
    ISSN: 0895-3996 , 1095-9114
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2012019-9
    SSG: 11
    Location Call Number Limitation Availability
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  • 2
    In: Journal of Alzheimer's Disease, IOS Press, Vol. 86, No. 4 ( 2022-04-19), p. 1783-1796
    Abstract: Background: Fine particulate matter (particulate matter 2.5, PM2.5) is considered one of the harmful factors to neuronal functions. Apoptosis is one of the mechanisms of neuronal injury induced by PM2.5. Methylcobalamine (MeCbl) has been shown to have anti-apoptotic and neuroprotective effects. Objective: The current work tried to explore the neuroprotective effects and mechanisms that MeCbl protects mice against cognitive impairment and neuronal apoptosis induced by chronic real-time PM2.5 exposure. Methods: Twenty-four 6-week-old male C57BL/6 mice were exposed to ambient PM2.5 and fed with MeCbl for 6 months. Morris water maze was used to evaluate the changes of spatial learning and memory ability in mice. PC12 cells and primary hippocampal neurons were applied as the in vitro model. Cell viability, cellular reactive oxygen species (ROS) and the expressions of apoptosis-related proteins were examined. And cells were stained with JC-1 and mitochondrial membrane potential was evaluated. Results: In C57BL/6 mice, MeCbl supplementation alleviated cognitive impairment and apoptosis-related protein expression induced by PM2.5 exposure. In in vitro cell model, MeCbl supplementation could effectively rescue the downregulation of cell viability induced by PM2.5, and inhibited the increased levels of ROS, cellular apoptosis, and the expressions of apoptosis related proteins related to PM2.5 treatment, which may be associated with modulation of mitochondrial function. Conclusion: MeCbl treatment alleviated cognitive impairment and neuronal apoptosis induced by PM2.5 both in vivo and in vitro. The mechanism for the neuroprotective effects of MeCbl may at least be partially dependent on the regulation of mitochondrial apoptosis.
    Type of Medium: Online Resource
    ISSN: 1387-2877 , 1875-8908
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2022
    detail.hit.zdb_id: 2070772-1
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  • 3
    In: Journal of Back and Musculoskeletal Rehabilitation, IOS Press, Vol. 36, No. 5 ( 2023-08-31), p. 1075-1086
    Abstract: BACKGROUND: Previous literature suggests that transversus abdominis (TrA) activation is diminished in chronic low back pain (cLBP) subjects compared to healthy subjects in less-functional positions. However, few studies have investigated the effects of upright functional movement on TrA activation in cLBP individuals. OBJECTIVE: This pilot study aimed to compare TrA activation characteristics in healthy and cLBP subjects during the movement of double leg standing (DLS) to single leg standing (SLS) and to a 30∘ single leg quarter squat (QSLS). METHODS: TrA activation was determined by the percentage change in TrA thickness from DLS to SLS and DLS to QSLS. TrA thickness was measured in 14 healthy and 14 cLBP participants using ultrasound imaging with a probe holder at 20 mm and 30 mm from the fascia conjunction point. RESULTS: At both measurement points (20 and 30 mm), there were no significant main effects of body sides, lower limb movements and the interactions between them on TrA activations between the healthy and cLBP participants even after covariates were adjusted for (all p 〉 0.05). CONCLUSIONS: Results from this study suggest the evaluation of TrA activation during upright functional movements as part of an assessment for cLBP management may not be suggested.
    Type of Medium: Online Resource
    ISSN: 1878-6324 , 1053-8127
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2029336-7
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  • 4
    Online Resource
    Online Resource
    IOS Press ; 2022
    In:  Journal of X-Ray Science and Technology Vol. 30, No. 3 ( 2022-04-15), p. 613-630
    In: Journal of X-Ray Science and Technology, IOS Press, Vol. 30, No. 3 ( 2022-04-15), p. 613-630
    Abstract: BACKGROUND: Image reconstruction for realistic medical images under incomplete observation is still one of the core tasks for computed tomography (CT). However, the stair-case artifacts of Total variation (TV) based ones have restricted the usage of the reconstructed images. OBJECTIVE: This work aims to propose and test an accurate and efficient algorithm to improve reconstruction quality under the idea of synergy between local and nonlocal regularizations. METHODS: The total variation combining the nonlocal means filtration is proposed and the alternating direction method of multipliers is utilized to develop an efficient algorithm. The first order approximation of linear expansion at intermediate point is applied to overcome the computation of the huge CT system matrix. RESULTS: The proposed method improves root mean squared error by 25.6% compared to the recent block-matching sparsity regularization (BMSR) on simulation dataset of 19 views. The structure similarities of image of the new method is higher than 0.95, while that of BMSR is about 0.92. Moreover, on real rabbit dataset of 20 views, the peak signal-to-noise ratio (PSNR) of the new method is 36.84, while using other methods PSNR are lower than 35.81. CONCLUSIONS: The proposed method shows advantages on noise suppression and detail preservations over the competing algorithms used in CT image reconstruction.
    Type of Medium: Online Resource
    ISSN: 0895-3996 , 1095-9114
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2022
    detail.hit.zdb_id: 2012019-9
    SSG: 11
    Location Call Number Limitation Availability
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