Investigative Radiology最新文献

Purpose: Intraoral coils (IOCs) in magnetic resonance imaging (MRI) significantly improve the signal-to-noise ratio compared with conventional extraoral coils. To assess the safety of IOCs, we propose a 2-step procedure to evaluate radiofrequency-induced heating of IOCs and compare maximum temperature increases in 3 different types of IOCs.

Methods: The 2-step safety assessment consists of electric field measurements and simulations to identify local hotspots followed by temperature measurements during MRI. With this method, 3 different coil types (inductively coupled IFC, transmit/receive tLoop, and receive-only tLoopRx) were tested at 1.5 T and 3 T for both tuned and detuned coil states. High SAR and regular MRI protocols were applied for 2 coil positions.

Results: The measured E field maps display distinct hotspots for all tuned IOCs, which were reduced by at least 40-fold when the IOCs were detuned. Maximum temperature rise was higher when the coils were positioned at the periphery of the phantom with the coil planes parallel to B 0 . When neither active nor passive detuning was applied, maximum temperature increase of ΔT = 1.3/0.5/1.8 K was found for IFC/tLoop/tLoopRx coils. Hotspots detected by E field measurements, and simulations were consistent. In the simulations, the results were different for homogeneous phantoms compared with full anatomical models. The 2-step test procedure is applicable to different coil types.

Conclusions: The results indicate that a risk for radiofrequency-induced heating exists for tuned IOCs, so that adequate detuning circuits need to be integrated in the coils to ensure safe operation.

Objectives: Ferumoxytol is a superparamagnetic iron-oxide product that is increasingly used off-label for contrast-enhanced magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA). With the recent regulatory approval of generic ferumoxytol, there may be an opportunity to reduce cost, so long as generic ferumoxytol has similar imaging performance to brand name ferumoxytol. This study aims to compare the relaxation-concentration dependence and MRI performance of brand name ferumoxytol with generic ferumoxytol through phantom and in vivo experiments. The secondary purpose was to determine the optimal flip angle and optimal weight-based dosing.

Materials and methods: Phantom experiments were performed using both brand name (AMAG Pharmaceuticals) and generic (Sandoz Pharmaceuticals) ferumoxytol products. Each ferumoxytol product was diluted in saline, and separately in adult bovine whole blood, at 5 iron concentrations ranging from 0.3 to 2.1 mM. Vials were placed in an MR-compatible water bath at 37°C and imaged at both 1.5 T and 3.0 T. Longitudinal and transverse relaxation rate constants (R1, R2, R2*) were measured for each ferumoxytol concentration, and relaxation-concentration curves were estimated. An in vivo dose accumulation study with flip angle optimization was also implemented using a cross-over design, in healthy volunteers. Cumulative doses of 1, 3, 5, and 7 mg/kg diluted ferumoxytol were administered prior to MRA of the chest on a 3.0 T clinical MRI system. For each incremental dose, the flip angle was varied from 40° to 10° in -10° increments over 5 breath-holds followed by a repeated 40° flip angle acquisition. Regions of interest were drawn in the aortic arch, paraspinous muscles, and a noisy area outside of the patient, free from obvious artifact. Signal-to-noise ratio (SNR) was calculated as the quotient of the average signal in the aortic arch and the standard deviation of the noise, corrected for a Rician noise distribution. Contrast-to-noise ratio was calculated as the difference in SNR between the aorta and paraspinous muscles. Absolute SNR and contrast-to-noise ratio values were compared between products for different flip angles and doses.

Results: There were no statistically significant or clinically relevant differences in relaxation-concentration curves between AMAG and Sandoz products in phantom experiments. Six healthy volunteers (38.8 ± 11.5 years, 3 female, 3 male) were successfully recruited and completed both imaging visits. No clinically relevant differences in image quality were observed between ferumoxytol products. The optimal flip angle range and dose for both products was 20°-30° and 5 mg/kg, respectively.

Conclusions: Brand name and generic ferumoxytol products can be used interchangeably for MRA.

Objectives: This study sought to elucidate the diagnostic performance of 0.55 T magnetic resonance imaging (MRI) for pediatric dental imaging, specifically in terms of the image quality (IQ) for detecting ectopic and/or supernumerary teeth, compared with routine ultra-low-dose computed tomography (ULD-CT) of the jaw.

Materials and methods: A total of 16 pediatric patients (mean age: 12.4 ± 2.6 years, range: 9-17 years) with ectopic and/or supernumerary teeth screened from January 2023 to January 2024 were enrolled in this prospective, single-center study. All patients underwent ULD-CT as the clinical reference standard and 0.55 T MRI as the study scan on the same day. A 0.6-mm isotropic 3-dimensional T1w FLASH sequence was developed with a dedicated field of view of the upper and lower jaws. ULD-CT was performed using a new single-source computed tomography (CT) scanner equipped with a tin filter (Sn100, slice thickness: 1 mm, quality reference mAs: 24). The IQ for the tooth axis, the tooth length, the tooth root, root resorptions, cysts, the periodontal ligament space, and the mandibular canal was evaluated twice by 3 senior readers using a 5-point Likert scale (LS) (LS score of 1: insufficient, 3: reduced IQ but sufficient for clinical use, and 5: perfect) and compared between both methods. Subsequently, the results were dichotomized into nonvalid (LS score of ≤2) and valid (LS score of ≥3) for clinical use.

Results: A total of 49 ectopic and/or supernumerary teeth in 16 pediatric patients were investigated using ULD-CT (CTDI: 0.43 ± 0.09 mGy) and 0.55 T MRI. The mean MRI acquisition time was 9:45 minutes. Motion artifacts were nonsignificantly different between 0.55 T MRI and ULD-CT (P = 0.126). The IQ for the tooth axis, the tooth root, root resorptions, and cysts was similar between the methods. The IQ for the periodontal ligament space and tooth length favored ULD-CT by 14% (confidence interval [CI]: 4.3%-24%) and 7.5% (CI: 1.8%-13%), respectively, whereas that for the mandibular canal favored 0.55 T MRI by -35% (CI: -54%-16%). Sufficient IQ was found especially for cystic lesions (CT: 100% sufficient, MRI: 95% sufficient), the tooth root (CT: 100%, MRI: 98%), root resorptions (CT: 94%; MRI: 85%), the tooth axis (CT: 100%; MRI: 98%), and the tooth length (CT: 99%; MRI: 91%).

Conclusions: The findings indicate that 0.55 T MRI is a feasible, radiation-free technique for delineating ectopic and/or supernumerary teeth in pediatric patients. Nevertheless, to date, 0.55 T MRI has not yet been able to provide an optimal IQ for all anatomical tooth and jaw structures. In cases of advanced clinical indications that require optimal spatial resolution, high-resolution CT or cone-beam CT may still be necessary.

Purpose: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance.

Materials and methods: Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness. We also evaluated the cumulative effect of these parameters by combining the best- and the worst-performing parameters. A subanalysis compared Sybil's performance by CT manufacturer. We considered any LDCT positive if future lung cancer was subsequently confirmed by biopsy or surgical resection. The areas under the curve (AUCs) for each series pair were compared using DeLong's test.

Results: There was no difference in Sybil's performance between 1049 pairs of standard versus bone reconstruction filter (AUC at 1 year 0.84 [95% confidence interval (CI): 0.70-0.99] vs 0.86 [95% CI: 0.75-0.98], P = 0.87) and 1961 pairs of standard versus lung reconstruction filter (AUC at 1 year 0.98 [95% CI: 0.97-0.99] vs 0.98 [95% CI: 0.96-0.99], P = 0.81). Similarly, there was no difference in 1288 pairs comparing 2-mm versus 5-mm axial slice thickness (AUC at 1 year 0.98 [95% CI: 0.94-1.00] vs 0.99 [95% CI: 0.97-0.99], P = 0.68). The best-case scenario combining a lung reconstruction filter with 2-mm slice thickness compared with the worst-case scenario combining a bone reconstruction filter with 2.5-mm slice thickness uncovered a significantly different performance at years 2-4 (P = 0.03). Subanalysis showed no significant difference in performance between Siemens and Toshiba scanners.

Conclusions: Sybil's predictive performance for future lung cancer risk is robust across different reconstruction filters and axial slice thicknesses, demonstrating its versatility in various imaging settings. Combining favorable reconstruction parameters can significantly enhance predictive ability at years 2-4. The absence of significant differences between Siemens and Toshiba scanners further supports Sybil's versatility.

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.

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.

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.