Image denoising in photon-counting CT using PFGM++ with hijacked regularized sampling

Deep learning (DL) has proven to be an important tool for high quality image denoising in low-dose and photon-counting CT. However, DL models are usually trained using supervised methods, requiring paired data that may be difficult to obtain in practice. Physics-inspired generative models, such as score-based diffusion models, offer unsupervised means of solving a wide range of inverse problems via posterior sampling. The latest in this family are Poisson flow generative models (PFGM)++ which, inspired by electrostatics, treat the $N$-dimensional data as positive electric charges in a $N+D$-dimensional augmented space. The electric field lines generated by these charges are used to find an invertible mapping, via an ordinary differential equation, between an easy-to-sample prior and the data distribution of interest. In this work, we propose a method for CT image denoising based on PFGM++ that does not require paired training data. To achieve this, we adapt PFGM++ for solving inverse problems via posterior sampling, by hijacking and regularizing the sampling process. Our method incorporates score-based diffusion models (EDM) as a special case as $D\rightarrow \infty$, but additionally allows trading off robustness for rigidity by varying $D$. The network is efficiently trained on randomly extracted patches from clinical normal-dose CT images. The proposed method demonstrates promising performance on clinical low-dose CT images and clinical images from a prototype photon-counting system

CT acquisition parameter selection in the real world: impacts on radiation dose and variation amongst 155 institutions.

OBJECTIVE: Quantify the relationship between CT acquisition parameters and radiation dose, how often parameters are adjusted in real-world practice, and their degree of contribution to real-world dose distribution. Identify discrepancies between parameters that are impactful in theory and impactful in practice. METHODS: This study analyses 1.3 million consecutive adult routine abdomen exams performed between November 2015 and Jan 2021 included in the University of California, San Francisco International CT Dose Registry of 155 institutions. We calculated geometric standard deviation (gSD) for five parameters (kV, mAs, spiral pitch, number of phases, scan length) to assess variation in practice. A Gaussian mixed regression model was performed to predict the radiation dose-length product (DLP) using the parameters. Three conceptualizations of impact were computed for each parameter. To reflect the theoretical impact, we predict the increase in DLP per 10% (and 15%) increase in the parameter. To reflect the real-world practical impact, we predict the increase in DLP per gSD increase in the parameter. RESULTS: Among studied examinations, mAs, number of phases, and scan length were frequently manipulated (gSD 1.52-1.70); kV was rarely manipulated (gSD 1.07). Theoretically, kV is the most impactful parameter (29% increase in DLP per 10% increase in kV, versus 5-9% increase for other parameters). In real-world practice, kV is less impactful; for each gSD increase in kV, the DLP increases by 20%, versus 22-69% for other parameters. CONCLUSION: Despite the potential impact of kV on radiation dose, this parameter is rarely manipulated in common practice and this potential remains untapped. CLINICAL RELEVANCE STATEMENT: CT beam energy (kV) modulation has the potential to strongly reduce radiation over-dosage to the patient, theoretically more so than similar degrees of modulation in other CT acquisition parameters. Despite this, beam energy modulation rarely occurs in practice, leaving its potential untapped. KEY POINTS: • The relationship between CT acquisition parameter selection and radiation dose roughly coincided with established theoretical understanding. • CT acquisition parameters differ from each other in frequency and magnitude of manipulation, with beam energy (kV) being rarely manipulated. • Beam energy (kV) has the potential to substantially impact radiation dose, but because it is rarely manipulated, it is the least impactful CT acquisition parameter affecting radiation dose in practice

The effect of computed tomography parameters on sarcopenia and myosteatosis assessment: a scoping review

Abstract Computed tomography (CT) is a valuable assessment method for muscle pathologies such as sarcopenia, cachexia, and myosteatosis. However, several key underappreciated scan imaging parameters need consideration for both research and clinical use, specifically CT kilovoltage and the use of contrast material. We conducted a scoping review to assess these effects on CT muscle measures. We reviewed articles from PubMed, Scopus, and Web of Science from 1970 to 2020 on the effect of intravenous contrast material and variation in CT kilovoltage on muscle mass and density. We identified 971 articles on contrast and 277 articles on kilovoltage. The number of articles that met inclusion criteria for contrast and kilovoltage was 11 and 7, respectively. Ten studies evaluated the effect of contrast on muscle density of which nine found that contrast significantly increases CT muscle density (arterial phase 6–23% increase, venous phase 19–57% increase, and delayed phase 23–43% increase). Seven out of 10 studies evaluating the effect of contrast on muscle area found significant increases in area due to contrast (≤2.58%). Six studies evaluating kilovoltage on muscle density found that lower kilovoltage resulted in a higher muscle density (14–40% increase). One study reported a significant decrease in muscle area when reducing kilovoltage (2.9%). The use of contrast and kilovoltage variations can have dramatic effects on skeletal muscle analysis and should be considered and reported in CT muscle analysis research. These significant factors in CT skeletal muscle analysis can alter clinical and research outcomes and are therefore a barrier to clinical application unless better appreciated