Dataset Information

Preoperative evaluation of the segmental artery by three-dimensional image reconstruction vs. thin-section multi-detector computed tomography.

ABSTRACT: Background:"Exoview" is a three-dimensional (3D) image reconstruction software developed by our medical team independently. The aim of this retrospective study was to compare the use of 3D image reconstruction, and thin-section multi-detector computed tomography (MDCT) in the preoperative evaluation of the segmental artery (SA). Methods:From May 2018 to May 2019, 52 patients received anatomical segmentectomy in our department. All patients received computed tomography pulmonary angiography (CTPA) by use of a 64-slice MDCT before operation. Then the 2D CT data were converted into 3D format by use of Exoview. We compared the intraoperative findings of the SA branches with 3D images and thin-section MDCT. Results:The study cohort of 52 patients included 31 women and 21 men and the operative factors include operation time (148.75±53.56 min), blood loss (57.31±79.68 mL), postoperative hospitalization days (6.42±3.48 days), lymph node sampling (3.00±1.50 stations) and postoperative complications (5 patients, 10%). The adenocarcinoma in situ with microinvasion was the predominant type (25 cases, 48%). There were 7 patients accepted for video-assisted thoracoscopic surgery (VATS) lobectomy with radical lymph nodes dissection because invasive adenocarcinoma was confirmed by intraoperative frozen-section analysis. One other patient was confirmed for conversion from VATS segmentectomy to an open operation because of bleeding of the bronchial artery. According to intraoperative findings, 95.7% (132 of 138) and 100% (138 of 138) of these SA branches were precisely identified on preoperative 3D image reconstruction and thin-section MDCT images. The 6 missed branches were less than 1.4 mm in actual diameter. Conclusions:Both 3D image reconstruction and thin-section MDCT provided precise preoperative information about SA. The 3D image reconstruction software "Exoview" could visualize SA for surgeons. However, the thin-section MDCT provided a better evaluation of small SA branches.

SUBMITTER: Cui Z

PROVIDER: S-EPMC7475566 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Preoperative evaluation of the segmental artery by three-dimensional image reconstruction <i>vs.</i> thin-section multi-detector computed tomography.

Cui Zihan Z Ding Cheng C Li Chang C Song Xinyu X Chen Jun J Chen Tengfei T Xu Chun C Zhao Jun J

Journal of thoracic disease 20200801 8

<h4>Background</h4>"Exoview" is a three-dimensional (3D) image reconstruction software developed by our medical team independently. The aim of this retrospective study was to compare the use of 3D image reconstruction, and thin-section multi-detector computed tomography (MDCT) in the preoperative evaluation of the segmental artery (SA).<h4>Methods</h4>From May 2018 to May 2019, 52 patients received anatomical segmentectomy in our department. All patients received computed tomography pulmonary an ...[more]

PMID: 32944331

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Project description:ObjectivesDeep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.MethodsThis prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.ResultsReduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3.ConclusionDLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.Key points•Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

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Dataset Information

Preoperative evaluation of the segmental artery by three-dimensional image reconstruction vs. thin-section multi-detector computed tomography.

Publications

Preoperative evaluation of the segmental artery by three-dimensional image reconstruction <i>vs.</i> thin-section multi-detector computed tomography.

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