scholarly journals Anatomical analysis of inflammation in hand psoriatic arthritis by Dual-Energy CT Iodine Map

2021 ◽  
Vol 8 ◽  
pp. 100383
Author(s):  
Sho Ogiwara ◽  
Takeshi Fukuda ◽  
Reina Kawakami ◽  
Hiroya Ojiri ◽  
Kunihiko Fukuda
2016 ◽  
Vol 75 (Suppl 2) ◽  
pp. 103.2-103
Author(s):  
T. Fukuda ◽  
Y. Umezawa ◽  
S. Tojo ◽  
T. Yonenaga ◽  
A. Asahina ◽  
...  

2017 ◽  
Vol 27 (12) ◽  
pp. 5034-5040 ◽  
Author(s):  
Takeshi Fukuda ◽  
Yoshinori Umezawa ◽  
Akihiko Asahina ◽  
Hidemi Nakagawa ◽  
Kazuhiro Furuya ◽  
...  

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Risa Kanatani ◽  
Takashi Shirasaka ◽  
Tsukasa Kojima ◽  
Toyoyuki Kato ◽  
Masateru Kawakubo

AbstractIn this study, we investigated the influence of beam hardening on the dual-energy computed tomography (DECT) values of iodine maps, virtual monoenergetic (VME) images, and virtual non-contrast (VNC) images. 320-row DECT imaging was performed by changing the x-ray tube energy for the first and second rotations. DECT values of 5 mg/mL iodine of the multi-energy CT phantom were compared with and without a 2-mm-thick attenuation rubber layer (~700 HU) wound around the phantom. It was found that the CT density values UH, with/without the rubber layer had statistical differences in the iodine map (184 ± 0.7 versus 186 ± 1.8), VME images (125 ± 0.3 versus 110 ± 0.4), and VNC images (−58 ± 0.7 versus −76 ± 1.7) (p < 0.010 for all). This suggests that iodine mapping may be underestimated by DECT and overestimated by VME imaging because of x-ray beam hardening. The use of VNC images instead of plain CT images requires further investigation because of underestimation.


2021 ◽  
Vol 10 ◽  
Author(s):  
Lingyun Wang ◽  
Yang Zhang ◽  
Yong Chen ◽  
Jingwen Tan ◽  
Lan Wang ◽  
...  

ObjectivesThe aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC).MethodsA total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared.ResultThree radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p&lt;0.001) and testing cohorts (p&lt;0.001 and p=0.034, respectively).ConclusionsDual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.


Radiology ◽  
2017 ◽  
Vol 284 (1) ◽  
pp. 134-142 ◽  
Author(s):  
Takeshi Fukuda ◽  
Yoshinori Umezawa ◽  
Shinjiro Tojo ◽  
Takenori Yonenaga ◽  
Akihiko Asahina ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Reina Kayama ◽  
Takeshi Fukuda ◽  
Sho Ogiwara ◽  
Mami Momose ◽  
Tadashi Tokashiki ◽  
...  

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