Comparison of Knowledge-based Iterative Model Reconstruction (IMR) with Hybrid Iterative Reconstruction (iDose4) Techniques for Evaluation of Hepatocellular Carcinomas Using Computed Tomography

How Safe Are Radiation Doses in Diagnostic Radiology? A Historical Perspective and Review of Current Evidence

The "no dose is safe" linear no-threshold (LNT) model forms the basis for radiation safety in radiology practice. This model has its origins in observations of germline mutations in fruit flies exposed to X-rays. After World War II, quantitative risk estimates of radiation injury are primarily derived from the atomic bomb survivor Life Span Study. Current understanding of tissue response to radiation has raised doubts about the validity of LNT model at low doses encountered in the practice of diagnostic radiology. This article traces the evolution of basic radiation safety concepts and provides a bird's eye view of the Life Span Study and other studies which throw light on the matter. The arguments for an alternative, threshold, or even hermetic models of dose response are examined. The relevance of these developments to the nuclear power industry is also outlined.

Prior-image-based CT reconstruction using attenuation-mismatched priors

Prior-image-based reconstruction (PIBR) methods are powerful tools for reducing radiation doses and improving the image quality of low-dose computed tomography (CT). Apart from anatomical changes, prior and current images can also have different attenuations because they originated from different scanners or from the same scanner but with different x-ray beam qualities (e.g., kVp settings, beam filters) during data acquisition. In such scenarios, with attenuation-mismatched priors, PIBR is challenging. In this work, we investigate a specific PIBR method, called statistical image reconstruction, using normal-dose image-induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation-mismatched priors and achieve quantitative low-dose CT imaging. We propose two corrective schemes for the original SIR-ndiNLM method, (1) a global histogram-matching approach and (2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validate the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to simulate attenuation mismatches. Meanwhile, we utilize different CT slices to simulate anatomical mismatches or changes between the prior and the current low-dose image. We observe that the original SIR-ndiNLM introduces artifacts to the reconstruction when an attenuation-mismatched prior is used. Furthermore, we find that a larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our two proposed corrective schemes enable SIR-ndiNLM to effectively handle the attenuation mismatch and anatomical changes between the two images and successfully eliminate the artifacts. We demonstrate that the proposed techniques permit SIR-ndiNLM to leverage the attenuation-mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.