European Radiology Experimental最新文献

Background: Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom.

Methods: A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric.

Results: On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively.

Conclusion: One system outperformed others in DES algorithms, providing higher CNR at lower doses.

Relevance statement: This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose.

Key points: DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose.

Background: The use of two-dimensional (2D) ultrasound for guiding radiofrequency ablation (RFA) of benign thyroid nodules presents limitations, including the inability to monitor the entire treatment volume and operator dependency in electrode positioning. We compared three-dimensional (3D)-guided RFA using a matrix ultrasound transducer with conventional 2D-ultrasound guidance in an anthropomorphic thyroid nodule phantom incorporated additionally with temperature-sensitive albumin.

Methods: Twenty-four phantoms with 48 nodules were constructed and ablated by an experienced radiologist using either 2D- or 3D-ultrasound guidance. Postablation T2-weighted magnetic resonance imaging scans were acquired to determine the final ablation temperature distribution in the phantoms. These were used to analyze ablation parameters, such as the nodule ablation percentage. Further, additional procedure parameters, such as dominant/non-dominant hand use, were recorded.

Results: Nonsignificant trends towards lower ablated volumes for both within (74.4 ± 9.1% (median ± interquartile range) versus 78.8 ± 11.8%) and outside of the nodule (0.35 ± 0.18 mL versus 0.45 ± 0.46 mL), along with lower variances in performance, were noted for the 3D-guided ablation. For the total ablation percentage, 2D-guided dominant hand ablation performed better than 2D-guided non-dominant hand ablation (81.0% versus 73.2%, p = 0.045), while there was no significant effect in the hand comparison for 3D-guided ablation.

Conclusion: 3D-ultrasound-guided RFA showed no significantly different results compared to 2D guidance, while 3D ultrasound showed a reduced variance in RFA. A significant reduction in operator-ablating hand dependence was observed when using 3D guidance. Further research into the use of 3D ultrasound for RFA is warranted.

Relevance statement: Using 3D ultrasound for thyroid nodule RFA could improve the clinical outcome. A platform that creates 3D data could be used for thyroid diagnosis, therapy planning, and navigational tools.

Key points: Twenty-four in-house-developed thyroid nodule phantoms with 48 nodules were constructed. RFA was performed under 2D- or 3D-ultrasound guidance. 3D- and 2D ultrasound-guided RFAs showed comparable performance. Real-time dual-plane imaging may offer an improved overview of the ablation zone and aid electrode positioning. Dominant and non-dominant hand 3D-ultrasound-guided RFA outcomes were comparable.

Background: Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients.

Methods: In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan-Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses.

Results: Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03-5.48, p = 0.043).

Conclusion: Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS.

Relevance statement: The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation.

Key points: This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death.

The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA data. In this retrospective study, CTA data from multi-institutional cohorts were utilized to annotate facemasks (n = 100) and train an ANN model, subsequently tested on an external institution's dataset (n = 50) and compared to a publicly available defacing algorithm. Face detection (MTCNN) and verification (FaceNet) networks were applied to measure the similarity between the original and defaced CTA images. Dice similarity coefficient (DSC), face detection probability, and face similarity measures were calculated to evaluate model performance. The CTA-DEFACE model effectively segmented soft face tissue in CTA data achieving a DSC of 0.94 ± 0.02 (mean ± standard deviation) on the test set. Our model was benchmarked against a publicly available defacing algorithm. After applying face detection and verification networks, our model showed substantially reduced face detection probability (p < 0.001) and similarity to the original CTA image (p < 0.001). The CTA-DEFACE model enabled robust and precise defacing of CTA data. The trained network is publicly accessible at www.github.com/neuroAI-HD/CTA-DEFACE . RELEVANCE STATEMENT: The ANN model CTA-DEFACE, developed for automatic defacing of CT angiography images, achieves significantly lower face detection probabilities and greater dissimilarity from the original images compared to a publicly available model. The algorithm has been externally validated and is publicly accessible. KEY POINTS: The developed ANN model (CTA-DEFACE) automatically generates facemasks for CT angiography images. CTA-DEFACE offers superior deidentification capabilities compared to a publicly available model. By means of graphics processing unit optimization, our model ensures rapid processing of medical images. Our model underwent external validation, underscoring its reliability for real-world application.

Background: In liver computed tomography (CT), tailoring the contrast injection to the patient's specific characteristics is relevant for optimal imaging and patient safety. We evaluated a novel algorithm engineered for personalized contrast injection to achieve reproducible liver enhancement centered on 50 HU.

Methods: From September 2020 to August 31, 2022, CT data from consecutive adult patients were prospectively collected at our multicenter premises. Inclusion criteria consisted of an abdominal CT referral for cancer staging or follow-up. For all examinations, a web interface incorporating data from the radiology information system (patient details and examination information) and radiographer-inputted data (patient fat-free mass, imaging center, kVp, contrast agent details, and imaging phase) were used. Calculated contrast volume and injection rate were manually entered into the CT console controlling the injector. Iopamidol 370 mgI/mL or Iohexol 350 mgI/mL were used, and kVp varied (80, 100, or 120) based on patient habitus.

Results: We enrolled 384 patients (mean age 61.2 years, range 21.1-94.5). The amount of administered iodine dose (gI) was not significantly different across contrast agents (p = 0.700), while a significant increase in iodine dose was observed with increasing kVp (p < 0.001) and in males versus females (p < 0.001), as expected. Despite the differences in administered iodine load, image quality was reproducible across patients with 72.1% of the examinations falling within the desirable range of 40-60 HU.

Conclusion: This study validated a novel algorithm for personalized contrast injection in adult abdominal CT, achieving consistent liver enhancement centered at 50 HU.

Relevance statement: In healthcare's ongoing shift towards personalized medicine, the algorithm offers excellent potential to improve diagnostic accuracy and patient management, particularly for the detection and follow-up of liver malignancies.

Key points: The algorithm achieves reproducible liver enhancement, promising improved diagnostic accuracy and patient management in diverse clinical settings. The real-world study demonstrates this algorithm's adaptability to different variables ensuring high-quality liver imaging. A personalized algorithm optimizes liver CT, improving the visibility, conspicuity, and follow-up of liver lesions.

Background: Renal quantitative measurements are important descriptors for assessing kidney function. We developed a deep learning-based method for automated kidney measurements from computed tomography (CT) images.

Methods: The study datasets comprised potential kidney donors (n = 88), both contrast-enhanced (Dataset 1 CE) and noncontrast (Dataset 1 NC) CT scans, and test sets of contrast-enhanced cases (Test set 2, n = 18), cases from a photon-counting (PC)CT scanner reconstructed at 60 and 190 keV (Test set 3 PCCT, n = 15), and low-dose cases (Test set 4, n = 8), which were retrospectively analyzed to train, validate, and test two networks for kidney segmentation and subsequent measurements. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). The quantitative measurements' effectiveness was compared to manual annotations using the intraclass correlation coefficient (ICC).

Results: The contrast-enhanced and noncontrast models demonstrated excellent reliability in renal segmentation with DSC of 0.95 (Test set 1 CE), 0.94 (Test set 2), 0.92 (Test set 3 PCCT) and 0.94 (Test set 1 NC), 0.92 (Test set 3 PCCT), and 0.93 (Test set 4). Volume estimation was accurate with mean volume errors of 4%, 3%, 6% mL (contrast test sets) and 4%, 5%, 7% mL (noncontrast test sets). Renal axes measurements (length, width, and thickness) had ICC values greater than 0.90 (p < 0.001) for all test sets, supported by narrow 95% confidence intervals.

Conclusion: Two deep learning networks were shown to derive quantitative measurements from contrast-enhanced and noncontrast renal CT imaging at the human performance level.

Relevance statement: Deep learning-based networks can automatically obtain renal clinical descriptors from both noncontrast and contrast-enhanced CT images. When healthy subjects comprise the training cohort, careful consideration is required during model adaptation, especially in scenarios involving unhealthy kidneys. This creates an opportunity for improved clinical decision-making without labor-intensive manual effort.

Key points: Trained 3D UNet models quantify renal measurements from contrast and noncontrast CT. The models performed interchangeably to the manual annotator and to each other. The models can provide expert-level, quantitative, accurate, and rapid renal measurements.

Background: Longitudinal T1 relaxation time is a key imaging biomarker. In addition, T1 values are modulated by the administration of T1 contrast agents used in patients with tumors and metastases. However, in clinical practice, dedicated T1 mapping sequences are often not included in brain MRI protocols. The aim of this study is to address the absence of dedicated T1 mapping sequences in imaging protocol by deriving T1 maps from standard T1-weighted sequences.

Methods: A phantom, composed of 144 solutions of paramagnetic agents at different concentrations, was imaged with a three-dimensional (3D) T1-weighed turbo spin-echo (TSE) sequence designed for brain imaging. The relationship between the T1 values and the signal intensities was established using this phantom acquisition. T1 mapping derived from 3D T1-weighted TSE acquisitions in four healthy volunteers and one patient with brain metastases were established and compared to reference T1 mapping technique. The concentration of Gd-based contrast agents in brain metastases were assessed from the derived T1 maps.

Results: Based on the phantom acquisition, the relationship between T1 values and signal intensity (SI) was found equal to T1 = 0.35 × SI^-1.11 (R² = 0.97). TSE-derived T1 values measured in white matter and gray matter in healthy volunteers were equal to 0.997 ± 0.096 s and 1.358 ± 0.056 s (mean ± standard deviation), respectively. Mean Gd³⁺ concentration value in brain metastases was 94.7 ± 30.0 μM.

Conclusion: The in vivo results support the relevance of the phantom-based approach: brain T1 maps can be derived from T1-weighted acquisitions.

Relevance statement: High-resolution brain T1 maps can be generated, and contrast agent concentration can be quantified and imaged in brain metastases using routine 3D T1-weighted TSE acquisitions.

Key points: Quantitative T1 mapping adds significant value to MRI diagnostics. T1 measurement sequences are rarely included in routine protocols. T1 mapping and concentration of contrast agents can be derived from routine standard scans. The diagnostic value of MRI can be improved without additional scan time.

Background: To assess thoracic vascular computed tomography (CT) contrast enhancement of a novel intravenous tantalum oxide nanoparticle contrast agent (carboxybetaine zwitterionic tantalum oxide, TaCZ) compared to a conventional iodinated contrast agent (Iopamidol) in a rabbit multiphase protocol.

Methods: Five rabbits were scanned inside a human-torso-sized encasement on a clinical CT system at various scan delays after intravenous injection of 540 mg element (Ta or I) per kg of bodyweight of TaCZ or Iopamidol. Net contrast enhancement of various arteries and veins, as well as image noise, were measured. Randomized scan series were reviewed by three independent readers on a clinical workstation and assessed for vascular conspicuity and image artifacts on 5-point Likert scales.

Results: Overall, net vascular contrast enhancement achieved with TaCZ was superior to Iopamidol (p ≤ 0.036 with the exception of the inferior vena cava at 6 s (p = 0.131). Vascular contrast enhancement achieved with TaCZ at delays of 6 s, 40 s, and 75 s was superior to optimum achieved Iopamidol contrast enhancement at 6 s (p ≤ 0.036. Vascular conspicuity was higher for TaCZ in 269 of 300 (89.7%) arterial and 269 of 300 (89.7%) venous vessel assessments, respectively (p ≤ 0.005), with substantial inter-reader reliability (κ = 0.61; p < 0.001) and strong positive monotonic correlation between conspicuity scores and contrast enhancement measurements (ρ = 0.828; p < 0.001).

Conclusion: TaCZ provides absolute and relative contrast advantages compared to Iopamidol for improved visualization of thoracic arteries and veins in a multiphase CT protocol.

Relevance statement: The tantalum-oxide nanoparticle is an experimental intravenous CT contrast agent with superior cardiovascular and venous contrast capacity per injected elemental mass in an animal model, providing improved maximum contrast enhancement and prolonged contrast conspicuity. Further translational research on promising high-Z and nanoparticle contrast agents is warranted.

Key points: There have been no major advancements in intravenous CT contrast agents over decades. Iodinated CT contrast agents require optimal timing for angiography and phlebography. Tantalum-oxide demonstrated increased CT attenuation per elemental mass compared to Iopamidol. Nanoparticle contrast agent design facilitates prolonged vascular conspicuity.

Background: To explore an artificial intelligence (AI) technology employing YOLOv8 for quality control (QC) on elbow joint radiographs.

Methods: From January 2022 to August 2023, 2643 consecutive elbow radiographs were collected and randomly assigned to the training, validation, and test sets in a 6:2:2 ratio. We proposed the anteroposterior (AP) and lateral (LAT) models to identify target detection boxes and key points on elbow radiographs using YOLOv8. These identifications were transformed into five quality standards: (1) AP elbow positioning coordinates (X_A and Y_A); (2) olecranon fossa positioning distance parameters (S₁₇ and S₂₇); (3) key points of joint space (Y₃, Y₄, Y₅ and Y₆); (4) LAT elbow positioning coordinates (X₂ and Y₂); and (5) flexion angle. Models were trained and validated using 2,120 radiographs. A test set of 523 radiographs was used for assessing the agreement between AI and physician and to evaluate clinical efficiency of models.

Results: The AP and LAT models demonstrated high precision, recall, and mean average precision for identifying boxes and points. AI and physicians showed high intraclass correlation coefficient (ICC) in evaluating: AP coordinates X_A (0.987) and Y_A (0.991); olecranon fossa parameters S₁₇ (0.964) and S₂₇ (0.951); key points Y₃ (0.998), Y₄ (0.997), Y₅ (0.998) and Y₆ (0.959); LAT coordinates X₂ (0.994) and Y₂ (0.986); and flexion angle (0.865). Compared to manual methods, using AI, QC time was reduced by 43% for AP images and 45% for LAT images (p < 0.001).

Conclusion: YOLOv8-based AI technology is feasible for QC of elbow radiography with high performance.

Relevance statement: This study proposed and validated a YOLOv8-based AI model for automated quality control in elbow radiography, obtaining high efficiency in clinical settings.

Key points: QC of elbow joint radiography is important for detecting diseases. Models based on YOLOv8 are proposed and perform well in image QC. Models offer objective and efficient solutions for QC in elbow joint radiographs.

Background: Regular disease monitoring with low-dose high-resolution (LD-HR) computed tomography (CT) scans is necessary for the clinical management of people with cystic fibrosis (pwCF). The aim of this study was to compare the image quality and radiation dose of LD-HR protocols between photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) in pwCF.

Methods: This retrospective study included 23 pwCF undergoing LD-HR chest CT with PCCT who had previously undergone LD-HR chest CT with EID-CT. An intraindividual comparison of radiation dose and image quality was conducted. The study measured the dose-length product, volumetric CT dose index, effective dose and signal-to-noise ratio (SNR). Three blinded radiologists assessed the overall image quality, image sharpness, and image noise using a 5-point Likert scale ranging from 1 (deficient) to 5 (very good) for image quality and image sharpness and from 1 (very high) to 5 (very low) for image noise.

Results: PCCT used approximately 42% less radiation dose than EID-CT (median effective dose 0.54 versus 0.93 mSv, p < 0.001). PCCT was consistently rated higher than EID-CT for overall image quality and image sharpness. Additionally, image noise was lower with PCCT compared to EID-CT. The average SNR of the lung parenchyma was lower with PCCT compared to EID-CT (p < 0.001).

Conclusion: In pwCF, LD-HR chest CT protocols using PCCT scans provided significantly better image quality and reduced radiation exposure compared to EID-CT.

Relevance statement: In pwCF, regular follow-up could be performed through photon-counting CT instead of EID-CT, with substantial advantages in terms of both lower radiation exposure and increased image quality.

Key points: Photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) were compared in 23 people with cystic fibrosis (pwCF). Image quality was rated higher for PCCT than for EID-CT. PCCT used approximately 42% less radiation dose and offered superior image quality than EID-CT.