No abstract

Reduction to the pole (RTP) is a standard part of magnetic data processing method, especially for large‐scale mapping. RTP operation can transform a magnetic anomaly caused by an arbitrary source into the anomaly that the same source would produce if it is located at the pole and magnetized by induction only. Interpretation of magnetic data can further be helped by RTP in order to remove the influence of magnetic latitude on the anomalies, which is significant for anomalies caused by crust. The solution of RTP in the wave number domain faces a long standing difficulty of instability when the observed data are acquired at low latitudes especially at the geomagnetic equator. We present a new solution to this problem that allows stable reconstruction of the RTP field with a high fidelity at the magnetic equator, combining the reduction to the equator (RTE) and the phase reversal interpretation method for ∆T anomalies at low latitudes. The operation with RTE can transform theoretical magnetic anomalies located at the pole and magnetized by induction only into the observed magnetic anomalies. RTE used in our RTP procedure named SRTE is an absolutely stable potential field transformation. The principle of method is very simple, it can transform the observed magnetic anomaly at geomagnetic equator into the anomaly that would have been measured if the magnetization and ambient field were both vertical. It is a stable RTP operation at geomagnetic equator and performs a high computation speed with a reasonable accuracy. Theoretical models and the practical magnetic field data located at the magnetic equator show that the operator of RTP solution is stable and accurate.

Three-dimensional reconstruction can be performed in many ways, among which photometric stereo is an established and intensively investigated method. In photometric stereo, geometric alignment or pixel-matching between two-dimensional images under different illuminations is crucial to the accuracy of three-dimensional reconstruction, and the dynamic of the scene makes the task difficult. In this work, we propose a single-pixel three-dimensional reconstructioning system utilizing structured illumination, which is implemented via a high-speed LED array. By performing 500 kHz structured illumination and capturing the reflected light intensity with detectors at different spatical locations, two-dimensional images of different shadows with 64 × 64 pixel resolution are reconstructed at 122 frame per second. Three-dimensional profiles of the scene are further reconstructed using the surface gradients derived by photometric stereo algorithm, achieving a minimum accuracy of 0.50 mm. Chromatic three-dimensional imaging via an RGB LED array is also performed at 40 frame per second. The demonstrated system significantly improves the dynamic performance of the single-pixel three-dimensional reconstruction system, and offers potential solutions to many applications, such as fast three-dimensional inspection.

Purpose: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise reduction algorithm on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM). Materials and Methods: After obtaining institutional review board approval and 8 written informed consents, 60 patients (35 men, 25 women; age range: 18 to 85 y) referred for aorta CTA examination were assigned to 2 groups at random. Group A underwent an 80 kVp protocol with 40 mL CM (320 mg I/mL). Group A reconstructed with iterative reconstruction was named as group A1 and further AI-based noise reduction was named as group A2. Group B was scanned with standard 120 kVp, 80 mL CM, and iterative reconstruction algorithm. The quantitative assessment of IQ included aorta CT attenuation, noise, signal-to-noise ratio, and contrast-to-noise ratio. A 5-point scale (5—excellent, 1—poor) was used by 2 radiologists independently for qualitative IQ analysis. Results: The image noise significantly decreased while signal-to-noise ratio and contrast-to-noise ratio significantly increased in the order of group A1, B, and A2 (all P<0.05). Compared with group B, the subjective IQ score of group A1 was significantly lower (P<0.05), while that of group A2 had no significant difference (P>0.05). The effective dose and CM volume of group A were reduced by 79.18% and 50%, respectively, than that of group B. Conclusions: The AI-based noise reduction could improve the IQ of aorta CTA with low kV and reduced CM, which achieved the potential of radiation dose and contrast media reduction compared with conventional aorta CTA protocol.