Radiation dose and cancer risks from radiation exposure during abdominopelvic computed tomography (CT) scans: comparison of diagnostic and radiotherapy treatment planning CT scans

Assessment of tissue-air ratios in epoxy resin and PMMA phantoms for radiation dosimetry: findings from experimental measurements and Monte Carlo simulations

This study assesses radiation doses in multi-slice computed tomography (CT) using epoxy resin and PMMA phantoms, focusing on the relationship between TAR (tissue air ratio) and kilovoltage peak (kVp). The research was conducted using a Hitachi Supria 16-slice CT scanner. An epoxy resin phantom was fabricated from commercially available materials, to simulate human tissue. The phantom contained four peripheral inserts and one central insert for dose measurement, with optically stimulated luminescent dosimeters positioned at various depths (2 to 10 cm). Monte Carlo simulations were executed using the Geant4 Application for Tomographic Emission toolkit (GATE) to model photon transport, with the x-ray spectrum generated using SpekPy software. A non-linear fitting model was developed to describe the TAR-kVp relationship across different depths for epoxy resin and PMMA. Results indicated that TAR values were higher at low depths (2 cm) and decreased with increasing depth, reflecting the x-ray beam's attenuation. For instance, at 80 kVp and 2 cm depth, the experimental TAR for PMMA was 1.102 ± 0.011, closely matching the MC simulation value of 1.110 ± 0.036, resulting in a small difference of 0.7%. At a depth of 10 cm, the experimental TAR for PMMA decreased to 0.245 ± 0.006, while the MC TAR was 0.248 ± 0.016, with a relative difference of 1.2%. Similar trends were observed for epoxy resin, where the experimental TAR ranged from 1.070 ± 0.014 at 2 cm to 0.235 ± 0.009 at 10 cm, while MC simulation values ranged from 1.080 ± 0.038 to 0.238 ± 0.017. Bland-Altman analysis confirmed these results, with mean differences of 0.008 for PMMA and 0.006 for epoxy resin, indicating high agreement between the experimental and simulated TAR values. This study highlights the importance of phantom material selection in dose assessment and the implications of TAR in dose correction within the context of diagnostic radiology.

Reconstruction and Validation of Arterial Geometries for Computational Fluid Dynamics Using Multiple Temporal Frames of 4D Flow-MRI Magnitude Images

PURPOSE

Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data.

METHODS

For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier-Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries.

RESULTS

Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar.

CONCLUSION

This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast.

Peritoneal Carcinosis: What the Radiologist Needs to Know

Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.

Estimation of cancer risks due to chest radiotherapy treatment planning computed tomography (CT) simulations

The objective of our study was to determine organ doses to estimate the lifetime attributable risk (LAR) of cancer incidence related to chest tomography simulations for Radiotherapy Treatment Planning (RTTP) using patient-specific information. Patient data were used to calculate organ doses and effective dose. The effective dose (E) was calculated by two methods. First, to calculate effective dose in a standard phantom, the collected dosimetric parameters were used with the ImPACT CT Patient Dosimetry Calculator and E was calculated by applying related correction factors. Second, using the scanner-derived Dose Length Product, LARs were computed using the US National Academy of Sciences (BEIR VII) model for age- and sex-specific risks at each exposure. DLP, CTDI_vol, and scan length were 507 ± 143 mGy.cm, 11 ± 4 mGy, and 47 ± 7 cm, respectively. The effective dose was 10 ± 3 mSv using ImPACT patient dosimetry calculator software and 9 ± 2 mSv using the scanner-derived Dose Length Product. The LAR of cancer incidence for all cancers, all solid cancers and leukemia were 65 ± 29, 62 ± 27, 7 ± 2 cases per 100,000 individuals, respectively. Radiation exposure from the usage of CT for radiotherapy treatment planning (RTTP) causes non-negligible increases in lifetime attributable risk. The results of this study can be used as a guide by physicians to implement strategies based on the As Low As Reasonably Achievable (ALARA) principle that lead to a reduction dose without sacrificing diagnostic information.

Patient effective dose and radiation biological risk in the chest and abdominopelvic computed tomography

The incidence and mortality (per 100,000) rates in chest CT are highest for the lungs and breasts (incidence: lung = 116, breast = 98.64; mortality: lung = 113.43, breast = 49.72). Abdominopelvic CT scans showed the highest incidence for stomach (79.57), colon (62.86), bladder (48.69), and liver (28.63), respectively. Mortality is highest for the bladder (80.44), stomach (72.43), colon (69.02), and liver (63.78), respectively. This study helps to better understand the concept of radiation dose and the numbers reported as organ dose and effective dose and identify the probability of the stochastic effect.