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Purpose
To qualitatively and quantitatively compare unenhanced ultra-low-dose chest computed tomography (ULD-CT) acquired at 80 kVp and 135 kVp.Materials and methods
Fifty-one patients referred for unenhanced chest CT were prospectively included. There were 29 men and 22 women, with a mean age of 64.7 ± 11.6 (SD) years (range: 35–91 years) and a mean body mass index of 26.2 ± 6.3 (SD) (range: 17–54.9). All patients underwent two different ULD-CT protocols (80 kVp-40 mA and 135 kVp-10 mA). Image quality of both ULD-CT examinations using a 5-level scale as well as assessability of 6 predetermined lung parenchyma lesions were blindly evaluated by three radiologists and compared using a logistic regression model. Image noise of the two protocols was compared with Wilcoxon signed-rank test.Results
The mean dose-length product at 80 kVp and at 135 kVp were 14.7 ± 1.8 (SD) mGy.cm and 15.6 ± 1.9 (SD) mGy.cm, respectively (P < 0.001). Image noise was significantly lower at 135 kVp (58.9 ± 12.4) than at 80 kVp (74.7 ± 14.5) (P < 0.001). For all readers and for all examinations, the 135 kVp protocol yielded better image quality than 80 kVp protocol, with a mean qualitative score of 4.5 ± 0.7 versus 3.9 ± 0.8 (P < 0.001). The 135 kVp protocol was significantly more often of diagnostic quality than the 80 kvp protocol (92.3% versus 77.8%, respectively) (P < 0.001) and was less prone to image quality deterioration in obese patients. Parenchymal lesions were never better depicted on the 80 kVp protocol than with the 135 kVp protocol.Conclusion
Unenhanced chest ULD-CT should be acquired at a high kilovoltage and low current, such as 135 kVp-10 mA, over a low kilovoltage and high current protocol. 相似文献Purpose
The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions.Materials and methods
We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores.Results
We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set.Conclusion
This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort. 相似文献Material and Methods: This retrospective study included 365 eyes that underwent PK using corneas from 231 donors between June 2010 and June 2015. Patients were divided into three groups (group 1: primary endothelial diseases; group 2: iatrogenic endothelial disorders; and group 3: other pathologies with a healthy endothelium) according to PK indications. The primary outcome measure was corneal graft survival at the last visit (clear or opaque). Graft clarity was assessed using Kaplan–Meier survival analysis.
Results: The most frequent PK indication was keratoconus (KC) (20.5%) followed by pseudophakic bullous keratopathy (PBK) (18.9%). Donor age had a negative impact on endothelial cell density (ECD) measured by an eye bank specular microscope (p < 0.001). Median best-corrected visual acuity in logarithm of the minimum angle of resolution units increased from 2.1 to 0.8 at 1 year after PK (p < 0.001). The clear graft rate was 96.7% at year 1, 88.8% at year 2, and 85.5% at year 3. Overall graft survival was 84.9% during a median of 39 months (range: 24–79 months) of follow-up. A higher graft survival rate (67.2%) was observed in KC compared to PBK during 6 years (p < 0.001). Recipients younger than 50 years of age showed a better graft survival rate than those older than 70 years of age (p = 0.037). Donor ECD, time between excision and death, and preservation time had no significant effect on graft survival. Frequent graft rejection episodes (GREs) and additional procedures during surgery had a negative impact on graft survival (p < 0.001 and p = 0.014, respectively). A worse graft survival was observed in group 2 compared to groups 1 and 3 (p = 0.042).
Conclusions: Young recipient age and KC were associated with a better graft survival. Graft endothelial density and preservation time had no impact on graft survival. PBK, low vision at baseline and year 1, frequent GREs, and additional interventions during surgery had a negative impact on graft survivals. 相似文献