SwePub
Sök i SwePub databas

  Extended search

Träfflista för sökning "WFRF:(Yu Lifeng) srt2:(2020-2024)"

Search: WFRF:(Yu Lifeng) > (2020-2024)

  • Result 1-10 of 13
Sort/group result
   
EnumerationReferenceCoverFind
1.
  • Axelsson, Rebecca, et al. (author)
  • Computer model of mechanical imaging acquisition for virtual clinical trials
  • 2021
  • In: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640191 ; 11595, s. 1-115950
  • Conference paper (peer-reviewed)abstract
    • Malignant breast tumours can be distinguished from benign lesions and normal tissue based on their mechanical properties. Our pilot studies have demonstrated the potential of using Mechanical Imaging (MI) combined with mammography to reduce recalls and false positives in breast cancer screening by more accurately identifying benign lesions. To enable further optimization of MI we propose a computer simulation of the MI acquisition, for use in a Virtual Clinical Trial (VCT) framework. VCTs are computer simulated clinical trials used to efficiently evaluate clinical imaging systems. A linear elastic finite element (FE) model of the breast under dynamic compression was implemented using an open-source FE solver. A spherical tumour (15 mm in diameter) was inserted into the simulated predominantly adipose breast. The location and stiffness of the tumour was varied. The average stress on the compressed breast surface was calculated and compared with the local average stress at the tumour location and the Relative Mean Pressure over lesion Area (RMPA) was calculated. Preliminary results were within a realistic range with an average stress on the breast (tumour) of 5.9-16.6 kPa which is in agreement with published values between 1.0 - 22.5 kPa. This corresponds to RMPA values of 0.96-2.15 depending on stiffness and location of the tumour. This can lead to more detailed validation of various MI acquisition schemes through VCTs before their use in clinical studies.
  •  
2.
  • Bjerkén, Anna, et al. (author)
  • Dose evaluation of simultaneous breast radiography and mechanical imaging
  • 2023
  • In: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. ; 12463
  • Conference paper (peer-reviewed)abstract
    • This study investigates the impact in terms of radiation dose when performing simultaneous digital breast tomosynthesis(DBT) and mechanical imaging (MI) – DBTMI. DBTMI has demonstrated the potential to increase specificity of cancerdetection, and reduce unnecessary biopsies, as compared to digital mammography (DM) screening. The presence of theMI sensor during simultaneous image acquisition may increase the radiation dose when automatic exposure control is used.In this project, a radiation dose study was conducted on clinically available breast imaging systems with and without theMI sensor. We have investigated three approaches to analyse the dose increase in DBTMI, using (i) the estimates of averageglandular dose (AGD) reported in DICOM headers of radiography images; (ii) AGD measured by a conventionaldosemeter; and (iii) AGD measured by optically stimulated luminescence using NaCl pellets. The relative increase in AGDestimated from DICOM headers when using the MI sensor was on average 10.7% and 12.4%, for DM and DBTmeasurements, respectively. The relative increase in AGD using the conventional dosemeter was 11.2% in DM mode and12.2% in DBT mode. The relative increase in AGD using NaCl pellets was 14.6% in DM mode. Our measurements suggestthat the use of simultaneous breast radiography and MI increases the AGD by 13% on average. The increase in dose is stillbelow the acceptable values in mammography screening recommended by the European Guidelines.
  •  
3.
  • Costa, Arthur C., et al. (author)
  • Assessment of projection interpolation to compensate for the increased radiation dose in DBTMI
  • 2023
  • In: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. - 1605-7422. - 9781510660311 ; 12463
  • Conference paper (peer-reviewed)abstract
    • The combination of digital breast tomosynthesis (DBT) with other imaging modalities has been investigated in order to improve the detection and diagnosis of breast cancer. Mechanical Imaging (MI) measures the stress over the surface of the compressed breast, using a pressure sensor, during radiographic examination and its response has shown a correlation with the presence of malignant lesions. Thus, the combination of DBT and MI (DBTMI) has shown potential to reduce false positive results in breast cancer screening. However, compared to the conventional DBT exam, the presence of the MI sensor during mammographic image acquisition may cause a slight increase in the radiation dose. This work presents a proposal to reduce the radiation dose in DBTMI exams by removing some projections from the original set and replacing them with synthetic projections generated by a video frame interpolation (VFI) neural network. We compared several DBTMI acquisition arrangements, considering the removal of 16% of the original projections, using a deformable physical breast phantom, and evaluated the quality of the reconstructed images based on the Normalized Root Mean Squared Error (NRMSE). The results showed that, for some arrangements, the slices reconstructed with the addition of synthetic DBTMI projections presented better quality than when they were reconstructed with the reduced set of projections. Further studies must be carried out to optimize the interpolation approach.
  •  
4.
  • Dustler, Magnus, et al. (author)
  • Realism of mammography tissue patches simulated using perlin noise : A forced choice reading study
  • 2021
  • In: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640191 ; 11595
  • Conference paper (peer-reviewed)abstract
    • Software breast phantoms are central to the optimization of breast imaging, where in many cases the use of real images would be inefficient - or impossible. Establishing the realism of such phantoms is critical. For this study, patches of simulated breast tissue with different composition - fatty, scattered, heterogenous and dense tissue - were generated using a method based on Perlin noise. The composition of the patches is controlled by numerical parameters derived from input by radiologists and medical physicists with experience of breast imaging. Separate Perlin noise-based methods were used to simulate skin pores, high-frequency noise (representing quantum and electronic noise) and ligaments and vascular structures. In a forced choice reading study, the realism of the simulated tissue patches compared to patches from real mammograms was determined. Patches of 200-500 pixels were extracted from radiolucent, linear, nodular or homogenous (10 per category) mammograms randomly selected from a previously acquired dataset. Eighteen simulated patches in the same size range were added. Four readers, two radiologists and two medical physicists were shown the images in random order and asked to rate them as real or simulated. All readers accepted a substantial fraction of simulated images as real, ranging from 22% to 72%. Only two readers showed a significant difference in the number of images rated real in the real and simulated groups, 22% vs 73% (P=.0003) and 33% vs 63% (P=.04), respectively. These results suggest that the method employed can create images that are almost indistinguishable from patches of real mammograms.
  •  
5.
  • Fransson, V., et al. (author)
  • Deep learning volumetric brain segmentation based on spectral CT
  • 2023
  • In: Medical Imaging 2023 : Physics of Medical Imaging - Physics of Medical Imaging. - 1605-7422. - 9781510660311 ; 12463
  • Conference paper (peer-reviewed)abstract
    • The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural network was trained to identify grey and white matter in virtual monoenergetic images (70 keV) from spectral CT, using the MR-based segmentation as reference, and tested to assess its' performance. The network was able to identify both grey and white matter in roughly the correct areas. In general, there was an overestimation of grey matter. These results motivate further studies, as we predict that the network will be more accurate when trained on a larger data set.
  •  
6.
  • Fransson, Veronica, et al. (author)
  • Dose-length-product determination on cone beam computed tomography through experimental measurements and dose-area-product conversion
  • 2021
  • In: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640207 ; 11595
  • Conference paper (peer-reviewed)abstract
    • The dosimetry of cone beam computed tomography (CBCT) is not fully elaborated yet, and some of these systems presents dose-area-product (DAP) values after an examination rather than, as in the case of traditional CT, the doselength- product (DLP). The purpose of this study was to provide a reproducible and straight-forward method for DLP measurements on CBCT, as well as to validate a tool for estimating DLP for a CBCT system in terms of accuracy. A prototype conversion tool for estimating DLP, using the DAP value, was provided by the vendor of a CBCT system which currently display only DAP. The DAP to DLP conversion tool was validated using five protocols for extremity imaging. DLP was measured using a 30 cm ionization chamber and 30 cm long cylindrical PMMA-phantom. DLP, the integrated absorbed dose within the ionization chamber, was measured through central and peripheral measurements in the phantom in order to calculate the weighted DLP, DLPW,CBCT. Comparisons between DLPW,CBCT and estimated DLP, showed that the conversion tool was accurate within 10%, with a mean average error of 6.1% for all measured protocols. The variation between repeated measurements was small, making the method highly reproducible. In conclusion, in this study a simple method for determining DLP on CBCT was presented, and it was validated that the conversion tool can present the delivered dose in terms of DLP with high accuracy. The measured DLP, as well as the DLP estimated by the conversion tool, is suitable for quality control and relative dose comparisons between protocols, but its’ relation to the DLP of CT systems should be investigated further in order to relate to patient dose.
  •  
7.
  • Li, Xuechao, et al. (author)
  • Direct transformation of n-alkane into all-trans conjugated polyene via cascade dehydrogenation
  • 2021
  • In: National Science Review. - : Oxford University Press. - 2095-5138 .- 2053-714X. ; 8:10
  • Journal article (peer-reviewed)abstract
    • Selective C(sp(3))-H activation is of fundamental importance in processing alkane feedstocks to produce high-value-added chemical products. By virtue of an on-surface synthesis strategy, we report selective cascade dehydrogenation of n-alkane molecules under surface constraints, which yields monodispersed all-trans conjugated polyenes with unprecedented length controllability. We are also able to demonstrate the generality of this concept for alkyl-substituted molecules with programmable lengths and diverse functionalities, and more importantly its promising potential in molecular wiring.
  •  
8.
  • Li, Yachen, et al. (author)
  • Intrauterine and early postnatal exposures to submicron particulate matter and childhood allergic rhinitis : A multicity cross-sectional study in China
  • 2024
  • In: Environmental Research. - : Elsevier. - 0013-9351 .- 1096-0953. ; 247
  • Journal article (peer-reviewed)abstract
    • Background: Airborne particulate matter pollution has been linked to occurrence of childhood allergic rhinitis (AR). However, the relationships between exposure to particulate matter with an aerodynamic diameter <= 1 µm (PM1) during early life (in utero and first year of life) and the onset of childhood AR remain largely unknown. This study aims to investigate potential associations of in utero and first-year exposures to size-segregated PMs, including PM1, PM1-2.5, PM2.5, PM2.5-10, and PM10, with childhood AR.Methods: We investigated 29286 preschool children aged 3-6 years in 7 Chinese major cities during 2019-2020 as the Phase II of the China Children, Families, Health Study. Machine learning-based space-time models were utilized to estimate early-life residential exposure to PM1, PM2.5, and PM10 at 1 x 1-km resolutions. The concentrations of PM1-2.5 and PM2.5-10 were calculated by subtracting PM1 from PM2.5 and PM2.5 from PM10, respectively. Multiple mixed-effects logistic models were used to assess the odds ratios (ORs) and 95% confidence intervals (CIs) of childhood AR associated with per 10-µg/m3 increase in exposure to particulate air pollution during in utero period and the first year of life.Results: Among the 29286 children surveyed (mean +/- standard deviation, 4.9 +/- 0.9 years), 3652 (12.5%) were reported to be diagnosed with AR. Average PM1 concentrations during in utero period and the first year since birth were 36.3 +/- 8.6 µg/m3 and 33.1 +/- 6.9 µg/m3, respectively. Exposure to PM1 and PM2.5 during pregnancy and the first year of life was associated with an increased risk of AR in children, and the OR estimates were higher for each 10-µg/m3 increase in PM1 than for PM2.5 (e.g., 1.132 [95% CI: 1.022-1.254] vs. 1.079 [95% CI: 1.014-1.149] in pregnancy; 1.151 [95% CI: 1.014-1.306] vs. 1.095 [95% CI: 1.008-1.189] in the first year of life). No associations were observed between AR and both pre- and post-natal exposure to PM1-2.5, indicating that PM1 rather than PM1-2.5 contributed to the association between PM2.5 and childhood AR. In trimester-stratified analysis, childhood AR was only found to be associated with exposure to PM1 (OR = 1.077, 95% CI: 1.027-1.128), PM2.5 (OR = 1.048, 95% CI: 1.018-1.078), and PM10 (OR = 1.032, 95% CI: 1.007-1.058) during the third trimester of pregnancy. Subgroup analysis suggested stronger PM-AR associations among younger (<5 years old) and winter-born children.Conclusions: Prenatal and postnatal exposures to ambient PM1 and PM2.5 were associated with an increased risk of childhood AR, and PM2.5-related hazards could be predominantly attributed to PM1. These findings highlighted public health significance of formulating air quality guideline for ambient PM1 in mitigating children's AR burden caused by particulate air pollution.
  •  
9.
  • Teixeira, Joao P.V., et al. (author)
  • Novel Perlin-based phantoms using 3D models of compressed breast shapes and fractal noise
  • 2022
  • In: Medical Imaging 2022 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510649378 ; 12031
  • Conference paper (peer-reviewed)abstract
    • Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval, respectively. DBT projections and reconstructions of the phantoms were simulated using two acquisition geometries of our DBT prototype. The performance of both acquisition geometries was compared using breast volume segmentations of the Perlin phantoms. Results show that breast volume estimates are improved with the introduction of posterior-anterior motion of the x-ray source in DBT acquisitions. The breast volume is overestimated in DBT, varying substantially with the acquisition geometry; segmentation errors are more evident for thicker and larger breasts. These results provide additional evidence and suggest that custom acquisition geometries can improve the performance and accuracy in DBT. Perlin phantoms help to identify limitations in acquisition geometries and to optimize the performance of the DBT prototypes.
  •  
10.
  • Tomic, Hanna, et al. (author)
  • Assessment of a tumour growth model for virtual clinical trials of breast cancer screening
  • 2021
  • In: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640191 ; 11595
  • Conference paper (peer-reviewed)abstract
    • Image-based analysis of breast tumour growth rate may help optimize breast cancer screening and diagnosis. It may improve the identification of aggressive tumours and suggest optimal screening intervals. Virtual clinical trial (VCT) is a simulation-based method used to evaluate and optimize medical imaging systems and design clinical trials. Our work is motivated by desire to simulate multiple screening rounds with growing tumours. We have developed a model to simulate tumours with various growth rates; this study aims at evaluating the model. We used clinical data on tumour volume doubling times (TVDT) from our previous study, to fit a probability distribution ("clinical fit"). Growing tumours were inserted into 30 virtual breasts ("simulated cohort"). Based on the clinical fit we simulated two successive screening rounds for each virtual breast. TVDT from clinical and simulated images were compared. Tumour size was measured from simulated mammograms by a radiologist in three repeated sessions, to estimate TVDT ("estimated TVDT"). Reproducibility of measured sizes decreased slightly for small tumours. The mean TVDT from the clinical fit was 297±169 days, whereas the simulated cohort had 322±217 days, and the average estimated TVDT 340 ± 287 days. The median difference between the simulated and estimated TVDT was 12 days (4% of the mean clinical TVDT). Comparisons between other data sets suggest no significant difference (p>0.5). The proposed tumour growth model suggested close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.
  •  
Skapa referenser, mejla, bekava och länka
  • Result 1-10 of 13

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view