Detection of Low Blood Hemoglobin Levels on Pulmonary CT Angiography: A Feasibility Study Combining Dual-Energy CT and Machine Learning.

OBJECTIVES: To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. METHODS: Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The best model was evaluated in a separate test set (n = 24). RESULTS: Blood pool attenuation was significantly lower in cases than controls (p-values < 0.01), except in the right atrium (p = 0.06). The machine learning model had sensitivity, specificity, and accuracy of 83%, 92%, and 88%, respectively. Measurements at the descending aorta had the highest relative importance among all features; a threshold of 43 HU yielded sensitivity, specificity, and accuracy of 68%, 76%, and 72%, respectively. CONCLUSION: VNC imaging and machine learning shows good diagnostic performance for detecting anemia on DECT CTPA.

Clinical validation of CT image reconstruction with interior tomography.

Active x-ray collimation is well adopted in radiography and fluoroscopy for radiation dose reduction and image quality improvement. The application of this concept in computed tomography (CT) is significantly limited due to the truncation of projection data. Generally, an internal field of view (FOV) inside an imaging object cannot be exactly reconstructed only from the truncated projection data. Recent research shows that given some prior information of the FOV image, interior tomography can provide a unique and stable solution for image reconstruction of an internal FOV. The objective of this study is to evaluate the performance of interior reconstruction based on patient datasets obtained from a clinical CT scanner with dual x-ray tubes, which simultaneously gives full projections and truncated projections. Image reconstructions are performed from full and truncated projection data for the comparison of image quality, respectively. The reconstructed CT images were reviewed by a radiologist and a resident. The evaluation results of two observers showed that CT images reconstructed with truncated projections met clinically diagnostic requirements and were comparable to clinical images. This study demonstrates that with the development of interior tomography, active x-ray collimation in the imaging plane can be readily employed in CT imaging to further reduce patient radiation and improve image quality.