Investigative Radiology最新文献

Objectives: Some renal masses remain indeterminate after both contrast-enhanced CT (CE-CT) and contrast-enhanced MRI (CE-MRI), with uncertainty concerning their cystic or solid composition, raising an issue in patient management. The aim of this article was to assess the diagnostic performance of contrast-enhanced ultrasound (CEUS) in the characterization of indeterminate renal masses in this specific context.

Materials and methods: Starting from CEUS examinations investigating renal masses, we retrospectively identified patients with renal masses that remained indeterminate after both unenhanced and enhanced CT and MRI. CEUS examinations were performed in a single center between February 2009 and September 2019. Cross-sectional imaging and nonenhanced US images were individually reviewed to confirm each lesion's indeterminate nature. CEUS was performed to differentiate solid and cystic lesions. CEUS findings were correlated to pathologic analysis or follow-up (minimum 3 y) to assess diagnostic performance. Inter-reader agreement was also analyzed.

Results: Sixty-four patients [mean age: 60.5±12.1 (SD), 49 men; 15 women] with 73 indeterminate renal masses (median: 24 mm, range: 10 to 122 mm) were identified. CEUS enabled further characterization of 71 out of the 73 indeterminate lesions (97.3%). To establish the solid nature of a renal mass, CEUS had a sensitivity of 81.3% (95% CI: 54.5%-95.9%), a specificity of 98.2% (95% CI: 90.3%-99.9%), a positive predictive value of 92.9% (95% CI: 64.8%-98.9%), a negative predictive value of 94.7% (95% CI: 86.6%-98.0%), and an accuracy of 94.4% (95% CI: 86.2%-98.4%), with excellent inter-reader agreement.

Conclusion: CEUS can accurately distinguish solid from cystic lesions in renal masses indeterminate after CE-CT and CE-MRI.

Objective: To investigate the objective performance and subjective image quality of lower extremity CT angiography (CTA) in peripheral artery disease (PAD) through comparison of the first-generation photon-counting CT (PCCT) technology and the third-generation dual source energy-integrating detector CT (DECT) technology.

Materials and methods: Patients who underwent a CTA either on a PCCT or on a DECT were included in this retrospective analysis. All included patients received a digital subtraction angiography (DSA) as reference standard for stenosis grading. Virtual monoenergetic image data sets were reconstructed at 40, 45, 50, 55, and 60 keV. The noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) of vascular structures, as well as the subjective image quality using a standardized 5-point Likert Scale, were determined. Finally, the sensitivity, specificity, and accuracy of the stenotic disease detection for either technology (DECT and PCCT) were analyzed.

Results: PCCT angiography was performed in 50 PAD patients (31 males, mean age 76.16 ± 10.26), and DECT angiography was pursued in 50 PAD patients as well (29 males, mean age 74.0 ± 14.26). PCCT reached significantly higher CNR compared with DECT in all assessed arterial territories [eg, 27.84 (IQR: 22.57 to 34.66) vs 17.25 (IQR: 12.12 to 23.71), at the iliac arterial vasculature at 40 keV, P < 0.001]. Image quality and contrast were rated significantly higher for PCCT compared with DECT [eg, mean vessel contrast 5 (IQR: 4 to 5) vs 4 (IQR: 4 to 4)], at the calf arterial vasculature at 40 keV, P <0.001. Overall sensitivity, specificity, and accuracy for PCCT were 96%, 97%, and 97%, respectively, in comparison to 93%, 96%, and 94%, respectively, for DECT image data sets at 55 keV.

Conclusion: PCCT offers superior objective performance and better subjective image quality compared with DECT. Hence, PCCT angiography is improving cross-sectional PAD imaging.

Introduction: Atherosclerosis is the underlying cause of multiple cardiovascular pathologies. The present-day clinical imaging modalities do not offer sufficient information on plaque composition or rupture risk. A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) is a strongly upregulated proteoglycan-cleaving enzyme that is specific to cardiovascular diseases, inter alia, atherosclerosis.

Materials and methods: Male apolipoprotein E-deficient mice received a high-fat diet for 2 (n = 11) or 4 months (n = 11). Additionally, a group (n = 11) receiving pravastatin by drinking water for 4 months alongside the high-fat diet was examined. The control group (n = 10) consisted of C57BL/6J mice on standard chow. Molecular magnetic resonance imaging was performed prior to and after administration of the gadolinium (Gd)-based ADAMTS4-specific probe, followed by ex vivo analyses of the aortic arch, brachiocephalic arteries, and carotid arteries. A P value <0.05 was considered to indicate a statistically significant difference.

Results: With advancing atherosclerosis, a significant increase in the contrast-to-noise ratio was measured after intravenous application of the probe (mean precontrast = 2.25; mean postcontrast = 11.47, P < 0.001 in the 4-month group). The pravastatin group presented decreased ADAMTS4 expression. A strong correlation between ADAMTS4 content measured via immunofluorescence staining and an increase in the contrast-to-noise ratio was detected ( R2 = 0.69). Microdissection analysis revealed that ADAMTS4 gene expression in the plaque area was significantly greater than that in the arterial wall of a control mouse ( P < 0.001). Laser ablation-inductively coupled plasma-mass spectrometry confirmed strong colocalization of areas positive for ADAMTS4 and Gd.

Conclusions: Magnetic resonance imaging using an ADAMTS4-specific agent is a promising method for characterizing atherosclerotic plaques and could improve plaque assessment in the diagnosis and treatment of atherosclerosis.

Objectives: Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence.

Materials and methods: In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used.

Results: Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise ( P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences ( P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses.

Conclusions: In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.

Objectives: Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.

Methods: Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models.

Results: The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes.

Conclusions: The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.

Objective: Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients. The study aimed to validate a dedicated triggering algorithm for initiating respiratory-triggered high-pitch computed tomography (RT-HPCT) scans in end inspiration and end expiration in complex and irregular respiratory patterns using an anthropomorphic dynamic chest phantom. Additionally, a patient study was conducted to compare the image quality and lung expansion between RT-HPCT and standard HPCT.

Materials and methods: The study utilized an algorithm that processes the patient's breathing motion in real-time to determine the appropriate time to initiate a scan. This algorithm was tested on a dynamic, tissue-equivalent chest motion phantom to replicate and simulate 3-dimensional target motion using 28 breathing motion patterns taken from patient with irregular breathing. To evaluate the performance on human patients, prospective RT-HPCT was performed in 18 free-breathing patients. As a reference, unenhanced HPCT of the chest was performed in 20 patients without respiratory triggering during free-breathing. The mean CTDI was 1.73 mGy ± 0.1 mGy for HPCT and 1.68 mGy ± 0.1 mGy for RT-HPCT. For phantom tests, the deviation from the target position of the phantom inlay is known. Image quality is approximated by evaluating stationary versus moving acquisitions. For patient scans, respiratory motion artifacts and inspiration depth were analyzed using expert knowledge of lung anatomy and automated lung volume estimation. Statistical analysis was performed to compare image quality and lung volumes between conventional HPCT and RT-HPCT.

Results: In phantom scans, the average deviation from the desired excursion phase was 1.6 mm ± 4.7 mm or 15% ± 24% of the phantom movement range. In patients, the overall image quality significantly improved with respiratory triggering compared with conventional HPCT ( P < 0.001). Quantitative average lung volume was 4.0 L ± 1.1 L in the RT group and 3.6 L ± 1.0 L in the control group.

Conclusions: This study demonstrated the feasibility of using a patient-adaptive respiratory triggering algorithm for high-pitch lung CT in both phantom and patients. Respiratory-triggered high-pitch CT scanning significantly reduces breathing artifacts compared with conventional nontriggered free-breathing scans.

Objectives: Carotid plaque vulnerability is a strong predictor of recurrent ipsilateral stroke, but differentiation of plaque components using conventional computed tomography (CT) is suboptimal. The aim of our study was to evaluate the ability of dual-energy CT (DECT) to characterize atherosclerotic carotid plaque components based on the effective atomic number and effective electron density using magnetic resonance imaging (MRI) and, where possible, histology as the reference standard.

Materials and methods: Patients with recent cerebral ischemia and a ≥2-mm carotid plaque underwent computed tomography angiography and MRI. A subgroup underwent carotid endarterectomy. Trained observers delineated plaque components on histology or MRI, independent of computed tomography angiography. DECT was coregistered with MRI and/or histology. Intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous tissue, and calcifications were delineated on DECT, and ρ eff and Z eff values were determined in the derivation cohort (n = 55). Spatial separation of these components was evaluated in a ρ eff -Z eff -cluster plot. Ranges that optimally differentiate plaque features were determined. For validation, plaque components were quantified in the validation cohort (n = 29) using these ρ eff -Z eff ranges and literature-based Hounsfield unit (HU) ranges and correlated to MRI volumes.

Results: Eighty-four participants (68 ± 8 years; 55 male) were evaluated. In the derivation cohort, plaque components were well separated on the cluster plot, resulting in the following ranges: IPH:ρ eff < 1.15, Z eff < 7.5, LRNC:ρ eff < 1.15, Z eff :7.5-8.75, fibrous tissue:ρ eff < 1.15, Z eff > 8.75, and calcifications: ρ eff > 1.15, Z eff > 0. In the validation cohort, significant correlations were found between ρ eff -Z eff -based and MRI plaque volumes for fibrous tissue ( r = 0.69, P < 0.001), LRNC ( r = 0.94, P < 0.001), IPH ( r = 0.35, P = 0.03), and calcifications ( r = 0.70, P < 0.001). Lower correlations were found between HU-based and MRI plaque volumes for fibrous tissue ( r = 0.40, P = 0.02), LRNC ( r = 0.86, P < 0.001), and calcifications ( r = 0.47, P = 0.005), with no correlation for IPH ( r = 0.02, P = 0.45).

Conclusions: We determined ρ eff -Z eff ranges for plaque assessment. ρ eff -Z eff -based volumes showed strong-to-very strong correlations with MRI for LRNC, fibrous tissue, and calcifications and a weak correlation for IPH. ρ eff -Z eff -based volumes demonstrated superior agreement with MRI for all plaque components compared with HU-based volumes, highlighting the potential of DECT for the identification of patients with vulnerable plaques.

Objectives: This study aimed to investigate the association between the use of linear and macrocyclic gadolinium-based contrast agents (GBCAs) and the subsequent development of Parkinson disease (PD).

Methods: In this retrospective cohort study, data were extracted from the Korean National Health Insurance Service database, comprising 1,038,439 individuals. From this population, 175,125 adults aged 40 to 60 years with no history of brain disease were identified. All patients including 3835 who were administered GBCA at least once were monitored until 2022 for the onset of PD. Propensity score (PS) matching was employed to compare the incidence of PD between those exposed to GBCAs (either linear or macrocyclic) and those not exposed (no-GBCA group).

Results: The final cohort consisted of 1175 subjects exposed to linear GBCAs, 2334 exposed to macrocyclic GBCAs, and 171,616 unexposed to any GBCA (no-GBCA group). After PS matching, PD incidence was significantly higher in the linear GBCA group compared with the no-GBCA group (0.9% vs 0.0%, P = 0.002) and was also significantly higher in the macrocyclic GBCA group than in the no-GBCA group (0.5% vs 0.04%, P = 0.003). No significant difference in PD incidence was observed between the linear and macrocyclic GBCA groups.

Conclusions: Exposure to GBCAs was linked to an increased risk of developing PD in this large population-based study. The risk of PD did not differ significantly between linear and macrocyclic GBCAs.