Challenges estimating patient organs doses undergoing enhanced chest CT examination: exploratory study

Abstract

Purpose: Estimate organs doses (ODs) of patients subjected to unenhanced (S1) and enhanced (S2) chest CT studies relying on image parameters such as Hounsfield Units (HUs). Materials and Methods: CT scans and images of a total of 16 patients who underwent two series of chest CT studies were obtained and retrospectively examined. OD increments of liver and pancreas for both series (S1 & S2) were estimated using two different independent methods, namely simulation approach using CT-EXPO and Amato’s phantom-based fitting model (APFM). HUs were quantified for each organ by manually drawing fixed area-sized regions of interest (ROIs). The mean HUs were collected to obtain the ODs increments following APFM. Regression analysis was applied to find and assess the relationship between the HUs and the OD increments estimated using APFM and that using CT-EXPO. Spearman Coefficient and Wilcoxon Matched Paired t-test were conducted to show statistical correlation and difference between ODs increments using the two methods. Results: A strong significant difference was depicted between S1 and S2 scan series of liver and pancreas using CT-EXPO simulation. Mean HU values for S1 were lower than S2, resulting in statistically significant (p < 0.0001) HU changes. CT-EXPO simulation yielded significantly higher difference in ODs compared to the APFM for liver (p = 0.0455) and pancreas (p = 0.0031). Regression analysis revealed a strong relationship between HU of S1 and S2 and ODs increments using APFM in both organs (R 2 = 0.99), dissimilar to CT-EXPO (R 2 = 0.39 in liver and R 2 = 0.05 in pancreas). Conclusions: Although CT-EXPO allows for estimating ODs accounting for major acquisition scan parameters, it is not a reliable tool to evaluate the impact of contrast enhancement on ODs. On the other hand, the APFM accounts for contrast enhancement accumulation yet only provides relative OD increments, an information of limited clinical use.

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis

“…Moreover, the Pearson correlation coefficient (r) and the two-tailed p-value are calculated to assess the strength of the correlation between the input and output variables. The metrics R 2 , RMSE, MAE, MAPE and r are calculated as in the following Formulas (3)- (7) [19]:…”

“…The number of CT scans performed annually in the United States was projected to increase from 75 million to 84 million by 2022 [5], highlighting the need to understand and mitigate radiation-related risks. Dose indices vary between various X-ray-based imaging modalities such as mammography [6] and CT [7,8]. For instance, the volume CT dose index (CTDI vol ) and dose length product (DLP) are two commonly used dose indices in CT [7].…”

“…Dose indices vary between various X-ray-based imaging modalities such as mammography [6] and CT [7,8]. For instance, the volume CT dose index (CTDI vol ) and dose length product (DLP) are two commonly used dose indices in CT [7].…”

Applications of autonomous rover for radiometric surveillance

“…For example, exposure to highly radioactive contamination, such as nuclear accident, leads to radiation sickness, which permanently causes genetic damage, and/or death. On the other hand, low exposure to radiation over a long time has been scientifically proven to cause cancers [3,4]. This is why it has been a common practice in the industry to utilize autonomous robots and drones to avoid sending humans to perform measurements.…”

Studying the gender effect on apparent mass during whole-body vibration using ANN

“…Moreover, the usage of the machine learning could you help losing weight and improve the life quality [13][14][15][16][17][18][19][20][21][22][23]. Moreover, metaheuristic algorithms, integrated with machine learning techniques [24][25][26][27][28][29][30][31][32][33][34][35][36], can optimize the selection of input parameters for WBV studies on weight loss and quality of life improvements, overcoming challenges related to standardized protocols and diverse parameter settings, and providing more consistent and reliable outcomes [37][38][39][40][41][42][43][44][45][46][47][48][49]. In this work, the work of [1,12] is extended to cover the effect of the human gender on the apparent mass.…”