Automatic exposure control (AEC) for CT based on neural network-driven patient-specific real-time assessment of dose distributions and minimization of the effective dose

• Applicants: Professor Dr. Marc Kachelrieß; Professor Dr. Michael Lell; Professor Dr.-Ing. Andreas Maier
• Subject Area: Medical Physics, Biomedical Technology
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 428660931

Modern diagnostic CT systems comprise a variety of measures to keep patient dose at a minimum. Of particular importance is the tube current modulation (TCM) technique that automatically adapts the tube current for each projection in a way to minimize the tube current time product (mAs-product) for a given image quality. This can also be regarded as maximizing the image quality for a given mAs-product. TCM performs a modulation depending on the angular position of the x-ray tube and depending on the z-position of the scan. Measures related to TCM are the automated choice of the mean tube current and of the optimal tube voltage. These three dose reduction methods are also known under the term automatic exposure control (AEC).As of today, however, the AEC does not minimize the actual patient dose and thereby the actual patient risk. It rather minimizes surrogates thereof. The surrogate of TCM is the mAs-product. The surrogate used to automatically select the tube voltage is the CTDI value or the dose length product (DLP). A direct minimization of the weighted summed organ dose values and thereby the patient risk is currently not practicable due to a) the very high computation times of the Monte Carlo dose calculation algorithms and b) due to the lack of a reliable segmentation of the radiation sensitive and risk-relevant organs.We therefore plan to use artificial neural networks to solve the above-mentioned problems and to realize a new AEC which is capable of directly minimizing patient dose and risk instead of using surrogate parameters. A first neural net will convert the patient topogram(s) into a coarse estimate of the CT volume. In cases where only a single topogram is available information of the table height will be used for this estimation. A second neural net will segment the relevant organs. We can here partially use prior work of a previous DFG project (KA 1678/20, LE 2763/2, MA 4898/5). A third network will use further scan parameters (table increment, pitch value, rotation time, collimation, tube voltage, …) to compute the expected dose distribution per projection. This, together with the segmentation of the organs, shall be used to compute the effective dose (or risk) or the patient per projection. A minimization algorithm will then find the optimal tube current curve that minimizes patient risk at a given image quality or that maximizes image quality at a given patient risk.To evaluate our deep AEC algorithm diagnostic CT data will be collected. The data will be retrospectively converted to the desired tube current curve by adding noise to the rawdata followed by another reconstruction. Experienced radiologists will then perform a blinded study where they read images produced without AEC, with the conventional AEC as of today, and with our new deep AEC algorithm.

• DFG Programme: Research Grants