Eigenbin compression for reducing photon‐counting CT data size

• Published in: Medical physics (Lancaster) 2024-12, Vol.51 (12), p.8751-8760
• Main Authors: Schmidt, Taly Gilat, Yin, Zhye, Yao, Jingwu, Fan, Jiahua
• Format: Article
• Language: English
• Subjects: data compression; Data Compression - methods; Image Processing, Computer-Assisted - methods; material decomposition; Phantoms, Imaging; Photons; photon‐counting CT; Principal Component Analysis; Tomography, X-Ray Computed - methods
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.17409

Background Photon‐counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. Purpose This study proposes a lossy method to compress photon‐counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. Methods The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon‐count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M