Technical Note: Synthesizing of lung tumors in computed tomography images

2020 ◽  
Vol 47 (10) ◽  
pp. 5070-5076
Author(s):  
Teaghan B. O'Briain ◽  
Kwang Moo Yi ◽  
Magdalena Bazalova‐Carter
Keyword(s):  
Computed Tomography ◽  
Lung Tumors ◽  
Technical Note ◽  
Computed Tomography Images

Computer-assisted detection of metastatic lung tumors on computed tomography

2019 ◽  
Vol 27 (3) ◽  
pp. 199-207
Author(s):  
Yukihiro Yoshida ◽  
Tomoya Sakane ◽  
Jun Isogai ◽  
Yoshio Suzuki ◽  
Soichiro Miki ◽  
...  
Keyword(s):  
Computed Tomography ◽  
False Positive ◽  
Pulmonary Metastases ◽  
Lung Tumors ◽  
Computer Assisted ◽  
Positive Finding ◽  
Computed Tomography Images ◽  
Metastatic Lung ◽  
Computer Assisted Detection ◽  
Benign Nodules

Background This retrospective study examined the performance of computer-assisted detection in the identification of pulmonary metastases. Methods Fifty-five patients (41.8% male) who underwent surgery for metastatic lung tumors in our hospital from 2005 to 2012 were included. Computer-assisted detection software configured to display the top five nodule candidates according to likelihood was applied as the first reader for the preoperative computed tomography images. Results from the software were classified as “metastatic nodule”, “benign nodule”, or “false-positive finding” by two observers. Results Computer-assisted detection identified 85.3% (64/75) of pulmonary metastases that radiologists had detected, and 3 more (4%, 3/75) that radiologists had overlooked. Nodule candidates identified by computer-assisted detection included 86 benign nodules (median size 3.1 mm, range 1.2–18.7 mm) and 121 false-positive findings. Conclusions Computer-assisted detection identified pulmonary metastases overlooked by radiologists. However, this was at the cost of identifying a substantial number of benign nodules and false-positive findings.

scholarly journals Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
James H. Buffi ◽  
Joaquín Luis Sancho Bru ◽  
Joseph J. Crisco ◽  
Wendy M. Murray
Keyword(s):  
Computed Tomography ◽  
Motion Capture ◽  
Ct Images ◽  
Technical Note ◽  
Ct Scanner ◽  
Hand Motion ◽  
Computed Tomography Images ◽  
Accuracy And Precision ◽  
Principal Axes ◽  
Instrumented Glove

There has been a marked increase in the use of hand motion capture protocols in the past 20 yr. However, their absolute accuracies and precisions remain unclear. The purpose of this technical brief was to present a method for evaluating the accuracy and precision of the joint angles determined by a hand motion capture protocol using simultaneously collected static computed tomography (CT) images. The method consists of: (i) recording seven functional postures using both the motion capture protocol and a CT scanner; (ii) obtaining principal axes of the bones in each method; (iii) calculating the flexion angle at each joint for each method as the roll angle of the composite, sequential, roll-pitch-yaw rotations relating the orientation of the distal bone to the proximal bone; and (iv) comparing corresponding joint angle measurements. For demonstration, we applied the method to a Cyberglove protocol. Accuracy and precision of the instrumented-glove protocol were calculated as the mean and standard deviation, respectively, of the differences between the angles determined from the Cyberglove output and the CT images across the seven postures. Implementation in one subject highlighted substantial errors, especially for the distal joints of the fingers. This technical note both clearly demonstrates the need for future work and introduces a solid, technical approach with the potential to improve the current state of such assessments in our field.

scholarly journals Metal artifact reduction in computed tomography images based on developed generative adversarial neural network

2021 ◽  
Vol 24 ◽  
pp. 100573
Author(s):  
Goli Khaleghi ◽  
Mohammad Hosntalab ◽  
Mahdi Sadeghi ◽  
Reza Reiazi ◽  
Seied Rabi Mahdavi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images
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