To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases. Materials and methods:Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test. Results: Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001). Conclusion: ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.
Phase-unwrapping-free and iterative reconstruction methods for propagated refractive index (RI) tomography, which introduces lightwave backpropagation to a filtered back-projection method, are proposed. The phase-unwrapping-free method utilizes phase retrieval using a transport-ofintensity equation that does not require phase unwrapping. This method overcomes phase unwrapping problems of high calculation cost and phase unwrapping error when measuring millimeter-thick and/or large-RI-changes specimens. In the iterative method, a simultaneous iterative reconstruction technique in X-ray computed tomography is applied to the propagated RI tomography. This method leads to the reduction of the radial artifacts and the elimination of the need for a high-pass filter regardless of the shape and size of specimens. Hence, the accuracy of a reconstructed RI map is improved. Numerical simulations and optical experiments are carried out to prove the feasibility of the proposed methods.
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