Development and validation of a noise insertion algorithm for photon‐counting‐detector CT

Abstract

AbstractBackgroundInserting noise into existing patient projection data to simulate lower‐radiation‐dose exams has been frequently used in traditional energy‐integrating‐detector (EID)‐CT to optimize radiation dose in clinical protocols and to generate paired images for training deep‐learning‐based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD‐CT) also requires such a method to accomplish these tasks. However, clinical PCD‐CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log‐normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD‐CT.PurposeTo develop and validate a projection domain noise insertion algorithm for PCD‐CT that does not require access to the raw count data.Materials and MethodsA projection‐domain noise model developed originally for EID‐CT was adapted for PCD‐CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD‐CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment.ResultsThe NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges.ConclusionsA projection‐domain noise insertion algorithm was developed and validated for PCD‐CT to synthesize low‐dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep‐learning‐based methods.