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1.
Sci Rep ; 14(1): 11539, 2024 05 21.
Article in English | MEDLINE | ID: mdl-38773167

ABSTRACT

Blooming artifacts caused by calcifications appearing on computed tomography (CT) images lead to an underestimation of the coronary artery lumen size, and higher X-ray energy levels are suggested to reduce the blooming artifacts with subjective visual assessment. This study aimed to evaluate the effect of higher X-ray energy levels on the quantitative measurement of adjacent pixels affected by calcification using CT images. In this two-part study, CT images were acquired from dual-energy CT scanners by changing the X-ray energy levels such as kilovoltage peak (kVp) and kilo-electron volts (keV). Adjacent pixels affected by calcification were measured using the brightened length, excluding the actual calcified length, as determined by the full width at third maximum. In a separate clinical study, the adjacent affected pixels associated with 23 calcifications across 10 patients were measured using the same method as that used in the phantom study. Phantom and clinical studies showed that the change in kVp (field of view [FOV] 300 mm: p = 0.167, 0.494, and 0.861 for vendors 1, 2, and 3, respectively) and keV levels (p = 0.178 for vendor 2) failed to reduce the adjacent pixels affected by calcification, respectively. Moreover, the change in keV levels showed different aspects of adjacent pixels affected by calcification in the phantom study (FOV 300 mm: no significant difference [p = 0.191], increase [p < 0.001], and decrease [p < 0.001] for vendors 1, 2, and 3, respectively). Quantitative measurements revealed no significant relationship between higher X-ray energy levels and the adjacent pixels affected by calcification.


Subject(s)
Artifacts , Calcinosis , Phantoms, Imaging , Tomography, X-Ray Computed , Humans , Tomography, X-Ray Computed/methods , Calcinosis/diagnostic imaging , Male , Female , Middle Aged , Aged , Coronary Vessels/diagnostic imaging , X-Rays
2.
Int J Cardiovasc Imaging ; 40(6): 1269-1281, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38634943

ABSTRACT

Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to coronary artery disease (CAD). Accurate segmentation of the lumen or plaque region is one of the most important factors. This study aimed to analyze the performance of the coronary artery segmentation of a software platform with a deep learning-based location-adaptive threshold method (DL-LATM) against commercially available software platforms using CCTA. The dataset from intravascular ultrasound (IVUS) of 26 vessel segments from 19 patients was used as the gold standard to evaluate the performance of each software platform. Statistical analyses (Pearson correlation coefficient [PCC], intraclass correlation coefficient [ICC], and Bland-Altman plot) were conducted for the lumen or plaque parameters by comparing the dataset of each software platform with IVUS. The software platform with DL-LATM showed the bias closest to zero for detecting lumen volume (mean difference = -9.1 mm3, 95% confidence interval [CI] = -18.6 to 0.4 mm3) or area (mean difference = -0.72 mm2, 95% CI = -0.80 to -0.64 mm2) with the highest PCC and ICC. Moreover, lumen or plaque area in the stenotic region was analyzed. The software platform with DL-LATM showed the bias closest to zero for detecting lumen (mean difference = -0.07 mm2, 95% CI = -0.16 to 0.02 mm2) or plaque area (mean difference = 1.70 mm2, 95% CI = 1.37 to 2.03 mm2) in the stenotic region with significantly higher correlation coefficient than other commercially available software platforms (p < 0.001). The result shows that the software platform with DL-LATM has the potential to serve as an aiding system for CAD evaluation.


Subject(s)
Computed Tomography Angiography , Coronary Angiography , Coronary Artery Disease , Coronary Vessels , Deep Learning , Plaque, Atherosclerotic , Predictive Value of Tests , Radiographic Image Interpretation, Computer-Assisted , Software , Ultrasonography, Interventional , Humans , Coronary Artery Disease/diagnostic imaging , Coronary Angiography/methods , Reproducibility of Results , Coronary Vessels/diagnostic imaging , Female , Male , Middle Aged , Aged , Retrospective Studies
3.
Eur Radiol ; 33(6): 3839-3847, 2023 Jun.
Article in English | MEDLINE | ID: mdl-36520181

ABSTRACT

OBJECTIVE: To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique. METHODS: This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1-10, 11-100, 101-400, and > 400. We compared CACS from LDCTs with that from calcium CT. RESULTS: Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945-0.972) for DL, 0.969 (95% CI, 0.956-0.978) for IR, and 0.952 (95% CI, 0.932-0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893-0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866-0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780-0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). CONCLUSION: DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel. KEY POINTS: • Deep learning (DL)-based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10).


Subject(s)
Coronary Artery Disease , Deep Learning , Humans , Coronary Artery Disease/diagnostic imaging , Calcium , Retrospective Studies , Tomography, X-Ray Computed/methods , Algorithms , Radiation Dosage , Radiographic Image Interpretation, Computer-Assisted/methods
4.
Korean J Radiol ; 22(5): 706-713, 2021 05.
Article in English | MEDLINE | ID: mdl-33543844

ABSTRACT

OBJECTIVE: To evaluate the impact of surgical simulation training using a three-dimensional (3D)-printed model of tetralogy of Fallot (TOF) on surgical skill development. MATERIALS AND METHODS: A life-size congenital heart disease model was printed using a Stratasys Object500 Connex2 printer from preoperative electrocardiography-gated CT scans of a 6-month-old patient with TOF with complex pulmonary stenosis. Eleven cardiothoracic surgeons independently evaluated the suitability of four 3D-printed models using composite Tango 27, 40, 50, and 60 in terms of palpation, resistance, extensibility, gap, cut-through ability, and reusability of. Among these, Tango 27 was selected as the final model. Six attendees (two junior cardiothoracic surgery residents, two senior residents, and two clinical fellows) independently performed simulation surgeries three times each. Surgical proficiency was evaluated by an experienced cardiothoracic surgeon on a 1-10 scale for each of the 10 surgical procedures. The times required for each surgical procedure were also measured. RESULTS: In the simulation surgeries, six surgeons required a median of 34.4 (range 32.5-43.5) and 21.4 (17.9-192.7) minutes to apply the ventricular septal defect (VSD) and right ventricular outflow tract (RVOT) patches, respectively, on their first simulation surgery. These times had significantly reduced to 17.3 (16.2-29.5) and 13.6 (10.3-30.0) minutes, respectively, in the third simulation surgery (p = 0.03 and p = 0.01, respectively). The decreases in the median patch appliance time among the six surgeons were 16.2 (range 13.6-17.7) and 8.0 (1.8-170.3) minutes for the VSD and RVOT patches, respectively. Summing the scores for the 10 procedures showed that the attendees scored an average of 28.58 ± 7.89 points on the first simulation surgery and improved their average score to 67.33 ± 15.10 on the third simulation surgery (p = 0.008). CONCLUSION: Inexperienced cardiothoracic surgeons improved their performance in terms of surgical proficiency and operation time during the experience of three simulation surgeries using a 3D-printed TOF model using Tango 27 composite.


Subject(s)
Cardiac Surgical Procedures/education , Heart Defects, Congenital/surgery , Simulation Training/methods , Heart Ventricles/surgery , Humans , Infant , Male , Models, Cardiovascular , Printing, Three-Dimensional , Task Performance and Analysis
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