Annabel Matz
Papers
1
Total Citations
7
H-Index
1
About
Annabel Matz is a researcher at the forefront of computational imaging, with a primary focus on X-ray computed tomography (CT) and trajectory optimization. Her most significant contribution lies in redefining how CT scans are acquired by moving beyond traditional circular or helical paths. In her highly cited 2022 work, "Trajectory Optimization in Computed Tomography Based on Object Geometry," Matz pioneered a method for robot-based CT (RoboCT) that tailors the scan trajectory to the specific geometry of the object being imaged. This approach ensures that a predefined set of projections yields a 3D reconstruction with markedly fewer image artifacts than any alternative scanning path. By directly linking the scanning motion to the object's shape, her work addresses a fundamental limitation of conventional CT—poor image quality for complex or irregularly shaped components. With 7 citations already, this paper is establishing a new paradigm for adaptive, high-fidelity imaging in industrial and medical applications. Matz’s research is not only advancing the theoretical foundations of CT but also paving the way for more efficient, artifact-free diagnostics and non-destructive testing.
Research Focus
Key Achievements
Top Papers
- 1Trajectory Optimization in Computed Tomography Based on Object Geometry7 citations · 2022