Investigative Radiology最新文献

Objectives: The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.

Methods: One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings.

Results: X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema (P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans (P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45).

Conclusions: The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.

Objectives: Gadopiclenol is a q = 2 pyclen gadolinium-based contrast agent (GBCA) recently approved by the Food and Drug Administration, European Medicines Agency, and other European countries. The aim of this report is to demonstrate its stability in multiple stressed in vitro conditions and in vivo, in rat kidney, while maintaining its higher relaxivity compared with conventional GBCAs on the market.

Materials and methods: Both gadopiclenol and its chemical precursor Pi828-Gd were characterized and compared with q = 1 gadolinium (Gd) complexes. The number of water molecules coordinated to the Gd (the hydration number, q) was determined by luminescence. 17O NMR (Nuclear Magnetic Resonance) measurements gave access to the water residence time τM. These parameters were used for the fitting of the nuclear magnetic relaxation dispersion profiles in water. Proton relaxivities of the complexes were determined in different media at 60 MHz (1.4 T), at different pH and temperature. The kinetic inertness was investigated in human serum, acidic media, under zinc competition in the presence of phosphate, and under ligand competition. The in vivo stability was evaluated in rat kidneys 12 months after repeated injections.

Results: The presence of 2 inner-sphere water molecules per Gd complex was confirmed for both pyclen derivatives. The high relaxivity of the complexes in water is maintained under physiological conditions, even under stressed conditions (ionic media, extreme pH, and temperature), which guarantees their efficiency in a large range of in vivo situations. Gd release from the q = 2 complexes was investigated in different potentially destabilizing conditions. Either no Gd release or a slower one than with "q = 1" stable macrocyclic GBCA (acidic conditions) was observed. Their kinetic inertness was demonstrated in physiological conditions, and the Gd release was below the lower limit of quantification of 0.1 μM after 12 days at 37°C in human serum. It was also demonstrated that gadopiclenol is stable in vivo in rat kidney 12 months after repeated injections.

Conclusions: Thanks to its optimized structural design, gadopiclenol is a highly stable and effective macrocyclic q = 2 GBCA.

Abstract: The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases. Age-related brain volume changes encompass a decrease in gray matter and an increase in ventricular volume, associated with cognitive decline. White matter hyperintensities, detected by FLAIR, are common and linked to cognitive impairments and increased risk of stroke and dementia. Tissue relaxometry reveals age-related changes in relaxivity, aiding the distinction between normal aging and pathological conditions. Myelin content, measurable by MRI, changes with age and is associated with cognitive and motor function alterations. Iron accumulation, detected by susceptibility-sensitive MRI, increases in certain brain regions with age, potentially contributing to neurodegenerative processes. Diffusion MRI provides detailed insights into microstructural changes such as neurite density and orientation. Neurofluid imaging, using techniques like gadolinium-based contrast agents and diffusion MRI, reveals age-related changes in cerebrospinal and interstitial fluid dynamics, crucial for brain health and waste clearance. This review offers a comprehensive overview of age-related brain changes revealed by various MRI techniques. Understanding these changes helps differentiate between normal aging and pathological conditions, aiding the development of interventions to mitigate age-related cognitive decline and other symptoms. Recent advances in machine learning and artificial intelligence have enabled novel methods for estimating brain age, offering also potential biomarkers for neurological and psychiatric disorders.

Purpose: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.

Materials and methods: The study utilized 2 datasets: the publicly accessible RANZCR CLiP dataset and a bespoke in-house dataset of 1006 annotated supine chest x-rays. Three deep learning models were trained: a classification network, a segmentation network, and a combination of both. These models were evaluated using receiver operating characteristic analysis, area under the curve, DICE similarity coefficient, and Hausdorff distance.

Results: The combined model demonstrated superior performance with an area under the curve of 0.99 for correctly positioned CVCs and 0.95 for misplacements. The model maintained high efficacy even with reduced training data from the local dataset. Sensitivity and specificity rates were high, and the model effectively managed the segmentation and classification tasks, even in images with multiple CVCs and other support materials.

Conclusions: This study illustrates the potential of AI-based models in accurately and reliably determining CVC placement in chest x-rays. The proposed method shows high accuracy and offers improved interpretability, important for clinical decision-making. The findings also highlight the importance of dataset quality and diversity in training AI models for medical image analysis.

Objective: Photon-counting detector computed tomography (PCD-CT) enables spectral data acquisition of CT angiographies allowing for reconstruction of virtual monoenergetic images (VMIs) in routine practice. Specifically, it has potential to reduce the blooming artifacts associated with densely calcified plaques. However, calcium blooming and iodine attenuation are inversely affected by energy level (keV) of the VMIs, creating a challenge for contrast media (CM) injection protocol optimization. A pragmatic and simple rule for calcium-dependent CM injection protocols is investigated and proposed for VMI-based coronary CT angiography with PCD-CT.

Materials and methods: A physiological circulation phantom with coronary vessels including calcified lesions (maximum CT value >700 HU) with a 50% diameter stenosis was injected into at iodine delivery rates (IDRs) of 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 2.5, and 3.0 g I/s. Images were acquired using a first-generation dual-source PCD-CT and reconstructed at various VMI levels (between 45 and 190 keV). Iodine attenuation in the coronaries was measured at each IDR for each keV, and blooming artifacts from the calcified lesions were assessed including stenosis grading error (as % overestimation vs true lumen). The IDR to achieve 300 HU at each VMI level was then calculated and compared with stenosis grading accuracy to establish a general rule for CM injection protocols.

Results: Plaque blooming artifacts and intraluminal iodine attenuation decreased with increasing keV. Fixed windowing (representing absolute worst case) resulted in stenosis overestimation from 77% ± 4% at 45 keV to 5% ± 2% at 190 keV, whereas optimized windowing resulted in overestimation from 29% ± 3% at 45 keV to 4% ± 1% at 190 keV. The required IDR to achieve 300 HU showed a strong linear correlation to VMI energy ( R2 = 0.98). Comparison of this linear plot versus stenosis grading error and blooming artifact demonstrated that multipliers of 1, 2, and 3 times the reference IDR for theoretical clinical regimes of no, moderate, and severe calcification density, respectively, can be proposed as a general rule.

Conclusions: This study provides a proof-of-concept in an anthropomorphic phantom for a simple pragmatic adaptation of CM injection protocols in coronary CT angiography with PCD-CT. The 1-2-3 rule demonstrates the potential for reducing the effects of calcium blooming artifacts on overall image quality.

Objectives: This phantom and animal pilot study aimed to compare image quality and radiation exposure between detector-dose-driven exposure control (DEC) and contrast-to-noise ratio (CNR)-driven exposure control (CEC) as functions of source-to-image receptor distance (SID) and collimation.

Materials and methods: First, an iron foil simulated a guide wire in a stack of polymethyl methacrylate and aluminum plates representing patient thicknesses of 15, 25, and 35 cm. Fluoroscopic images were acquired using 5 SIDs ranging from 100 to 130 cm and 2 collimations (full field of view, collimated field of view: 6 × 6 cm). The iron foil CNRs were calculated, and radiation doses in terms of air kerma rate were obtained and assessed using a multivariate regression. Second, 5 angiographic scenarios were created in 2 anesthetized pigs. Fluoroscopic images were acquired at 2 SIDs (110 and 130 cm) and both collimations. Two blinded experienced readers compared image quality to the reference image using full field of view at an SID of 110 cm. Air kerma rate was obtained and compared using t tests.

Results: Using DEC, both CNR and air kerma rate increased significantly at longer SID and collimation below the air kerma rate limit. When using CEC, CNR was significantly less dependent of SID, collimation, and patient thickness. Air kerma rate decreased at longer SID and tighter collimation. After reaching the air kerma rate limit, CEC behaved similarly to DEC. In the animal study using DEC, image quality and air kerma rate increased with longer SID and collimation ( P < 0.005). Using CEC, image quality was not significantly different than using longer SID or tighter collimation. Air kerma rate was not significantly different at longer SID but lower using collimation ( P = 0.012).

Conclusions: CEC maintains the image quality with varying SID and collimation stricter than DEC, does not increase the air kerma rate at longer SID and reduces it with tighter collimation. After reaching the air kerma rate limit, CEC and DEC perform similarly.