Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction.

RATIONALE AND OBJECTIVE: To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions. MATERIALS AND METHODS: This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared. RESULT: The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05). CONCLUSION: DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT.

Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia.

PURPOSE: To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS: Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images. RESULTS: The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists. CONCLUSIONS: 3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.

Influence of Monoenergetic Images at Different Energy Levels in Dual-Energy Spectral CT on the Accuracy of Computer-Aided Detection for Pulmonary Embolism.

PURPOSE: To investigate the influence of monoenergetic images of different energy levels in spectral computed tomography (CT) on the accuracy of computer aided detection (CAD) for pulmonary embolism (PE). MATERIALS AND METHODS: CT images of 20 PE patients who underwent spectral CT pulmonary angiography were retrospectively analyzed. Nine sets of monochromatic images from 40 to 80 keV at 5 keV interval were reconstructed and then independently analyzed for detecting PE using a commercially available CAD software. Two experienced radiologists reviewed all images and recorded the number of emboli manually, which was used as the reference standard. The CAD findings for the number of PE at different energies were compared with the reference standard to determine the number of true positives and false positives with CAD and to calculate the sensitivity and false positive rate at different energies. RESULT: There were 120 true emboli. The total numbers of CAD-detected PE at 40-80 keV were 48, 67, 63, 87, 106, 115, 138, 157, and 226. Images at low energies had low sensitivities and low false positive rates; images at high energies had high sensitivities and high false positive rates. At 60 keV and 65 keV, CAD achieved sensitivity at 81.67% and 84.17%, respectively and false positive rate at 7.55% and 12.17%, respectively to provide the optimum combination of high sensitivity and low false positive rate. CONCLUSION: Monochromatic images of different energies in dual-energy spectral CT affect the accuracy of CAD for PE. The combination of CAD with images at 60-65 keV provides the optimum combination of high sensitivity and low false positive rate in detecting PE.

Subtraction CT angiography in head and neck with low radiation and contrast dose dual-energy spectral CT using rapid kV-switching technique.

OBJECTIVE: To investigate the application of low radiation and contrast dose spectral CT angiology using rapid kV-switching technique in the head and neck with subtraction method for bone removal. METHODS: This prospective study was approved by the local ethics committee. 64 cases for head and neck CT angiology were randomly divided into Groups A (n = 32) and B (n = 32). Group A underwent unenhanced CT with 100 kVp, 200 mA and contrast-enhanced CT with spectral CT mode with body mass index-dependent low dose protocols. Group B used conventional helical scanning with 120 kVp, auto mA for noise index of 12 HU (Hounsfield unit) for both the unenhanced and contrast-enhanced CT. Subtraction images were formed by subtracting the unenhanced images from enhanced images (with the 65 keV-enhanced spectral CT image in Group A). CT numbers and their standard deviations in aortic arch, carotid arteries, middle cerebral artery and air were measured in the subtraction images. The signal-to-noise ratio and contrast-to-noise ratio for the common and internal carotid arteries and middle cerebral artery were calculated. Image quality in terms of bone removal effect was evaluated by two experienced radiologists independently and blindly using a 4-point system. Radiation dose and total iodine load were recorded. Measurements were statistically compared between the two groups. RESULTS: The two groups had same demographic results. There was no difference in the CT number, signal-to-noise and contrast-to-noise ratio values for carotid arteries and middle cerebral artery in the subtraction images between the two groups (p > 0.05). However, the bone removal effect score [median (min-max)] in Group A [4 (3-4)] was rated better than in Group B [3 (2-4)] (p < 0.001), with excellent agreement between the two observers (κ > 0.80). The radiation dose in Group A (average of 2.64 mSv) was 57% lower than the 6.18 mSv in Group B (p < 0.001). The total iodine intake in Group A was 13.5g, 36% lower than the 21g in Group B. CONCLUSION: Spectral CT imaging with rapid kV-switching in the subtraction angiography in head and neck provides better bone removal with significantly reduced radiation and contrast dose compared with conventional subtraction method. Advances in knowledge: This novel method provides better bone removal with significant radiation and contrast dose reduction compared with the conventional subtraction CT, and maybe used clinically to protect the thyroid gland and ocular lenses from unnecessary high radiation.