Paper
18 March 2015 Region of interest processing for iterative reconstruction in x-ray computed tomography
Felix K. Kopp, Radin A. Nasirudin, Kai Mei, Andreas Fehringer, Franz Pfeiffer, Ernst J. Rummeny M.D., Peter B. Noël
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Abstract
The recent advancements in the graphics card technology raised the performance of parallel computing and contributed to the introduction of iterative reconstruction methods for x-ray computed tomography in clinical CT scanners. Iterative maximum likelihood (ML) based reconstruction methods are known to reduce image noise and to improve the diagnostic quality of low-dose CT. However, iterative reconstruction of a region of interest (ROI), especially ML based, is challenging. But for some clinical procedures, like cardiac CT, only a ROI is needed for diagnostics. A high-resolution reconstruction of the full field of view (FOV) consumes unnecessary computation effort that results in a slower reconstruction than clinically acceptable. In this work, we present an extension and evaluation of an existing ROI processing algorithm. Especially improvements for the equalization between regions inside and outside of a ROI are proposed. The evaluation was done on data collected from a clinical CT scanner. The performance of the different algorithms is qualitatively and quantitatively assessed. Our solution to the ROI problem provides an increase in signal-to-noise ratio and leads to visually less noise in the final reconstruction. The reconstruction speed of our technique was observed to be comparable with other previous proposed techniques. The development of ROI processing algorithms in combination with iterative reconstruction will provide higher diagnostic quality in the near future.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Felix K. Kopp, Radin A. Nasirudin, Kai Mei, Andreas Fehringer, Franz Pfeiffer, Ernst J. Rummeny M.D., and Peter B. Noël "Region of interest processing for iterative reconstruction in x-ray computed tomography", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94122E (18 March 2015); https://doi.org/10.1117/12.2081911
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Cited by 3 scholarly publications.
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KEYWORDS
Reconstruction algorithms

Signal to noise ratio

X-ray computed tomography

Image quality

CT reconstruction

Diagnostics

Visualization

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