Investigative Radiology最新文献

Objectives: The T1-weighted GRE (gradient recalled echo) sequence with the Dixon technique for water/fat separation is an essential component of abdominal MRI (magnetic resonance imaging), useful in detecting tumors and characterizing hemorrhage/fat content. Unfortunately, the current implementation of this sequence suffers from several problems: (1) low resolution to maintain high pixel bandwidth and minimize chemical shift; (2) image blurring due to respiratory motion; (3) water/fat swapping due to the natural ambiguity between fat and water peaks; and (4) off-resonance fat blurring due to the multipeak nature of the fat spectrum. The goal of this study was to evaluate the image quality of water/fat separation using a high-resolution 3-point Dixon golden angle radial acquisition with retrospective motion compensation and multipeak fat modeling in children undergoing abdominal MRI.

Materials and methods: Twenty-two pediatric patients (4.2 ± 2.3 years) underwent abdominal MRI on a 3 T scanner with routine abdominal protocol and with a 3-point Dixon radial-VIBE (volumetric interpolated breath-hold examination) sequence. Field maps were calculated using 3D graph-cut optimization followed by fat and water calculation from k-space data by iteratively solving an optimization problem. A 6-peak fat model was used to model chemical shifts in k-space. Residual respiratory motion was corrected through soft-gating by weighting each projection based on the estimated respiratory motion from the center of the k-space. Reconstructed images were reviewed by 3 pediatric radiologists on a PACS (picture archiving and communication systems) workstation. Subjective image quality and water/fat swapping artifact were scored by each pediatric radiologist using a 5-point Likert scale. The VoL (variance of Laplacian) of the reconstructed images was used to objectively quantify image sharpness.

Results: Based on the overall Likert scores, the images generated using the described method were significantly superior to those reconstructed by the conventional 2-point Dixon technique ( P < 0.05). Water/fat swapping artifact was observed in 14 of 22 patients using 2-point Dixon, and this artifact was not present when using the proposed method. Image sharpness was significantly improved using the proposed framework.

Conclusions: In smaller patients, a high-quality water/fat separation with sharp visualization of fine details is critical for diagnostic accuracy. High-resolution golden angle radial-VIBE 3-point Dixon acquisition with 6-peak fat model and soft-gated motion correction offers improved image quality at the expense of an additional ~1-minute acquisition time. Thus, this technique offers the potential to replace the conventional 2-point Dixon technique.

Objectives: The aim of this study was to compare deep learning reconstructed (DLR) 0.55 T magnetic resonance imaging (MRI) quality, identification, and grading of structural anomalies and reader confidence levels with conventional 3 T knee MRI in patients with knee pain following trauma.

Materials and methods: This prospective study of 26 symptomatic patients (5 women) includes 52 paired DLR 0.55 T and conventional 3 T MRI examinations obtained in 1 setting. A novel, commercially available DLR algorithm was employed for 0.55 T image reconstruction. Four board-certified radiologists reviewed all images independently and graded image quality, noted structural anomalies and their respective reporting confidence levels for the presence or absence, as well as grading of bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05, significant), and MRI findings were correlated between 0.55 T and 3 T MRI using Cohen kappa (κ).

Results: In reader's consensus, good image quality was found for DLR 0.55 T MRI and 3 T MRI (3.8 vs 4.1/5 points, P = 0.06). There was near-perfect agreement between 0.55 T DLR and 3 T MRI regarding the identification of structural anomalies for all readers (each κ ≥ 0.80). Substantial to near-perfection agreement between 0.55 T and 3 T MRI was reported for grading of cartilage (κ = 0.65-0.86) and meniscus lesions (κ = 0.71-1.0). High confidence levels were found for all readers for DLR 0.55 T and 3 T MRI, with 3 readers showing higher confidence levels for reporting cartilage lesions on 3 T MRI.

Conclusions: In conclusion, new-generation 0.55 T DLR MRI provides good image quality, comparable to conventional 3 T MRI, and allows for reliable identification of internal derangement of the knee with high reader confidence.

Objectives: Clinical experience regarding the use of dedicated photon-counting breast CT (PC-BCT) for diagnosis of breast microcalcifications is scarce. This study systematically compares the detection and classification of breast microcalcifications using a dedicated breast photon-counting CT, especially designed for examining the breast, in comparison with digital breast tomosynthesis (DBT).

Materials and methods: This is a prospective intraindividual study on women with DBT screening-detected BI-RADS-4/-5 microcalcifications who underwent PC-BCT before biopsy. PC-BCT images were reconstructed with a noninterpolated spatial resolution of 0.15 × 0.15 × 0.15 mm (reconstruction mode 1 [RM-1]) and with 0.3 × 0.3 × 0.3 mm (reconstruction mode 2 [RM-2]), plus thin-slab maximum intensity projection (MIP) reconstructions. Two radiologists independently rated the detection of microcalcifications in direct comparison with DBT on a 5-point scale. The distribution and morphology of microcalcifications were then rated according to BI-RADS. The size of the smallest discernible microcalcification particle was measured. For PC-BCT, the average glandular dose was determined by Monte Carlo simulations; for DBT, the information provided by the DBT system was used.

Results: Between September 2022 and July 2023, 22 participants (mean age, 61; range, 42-85 years) with microcalcifications (16 malignant; 6 benign) were included. In 2/22 with microcalcifications in the posterior region, microcalcifications were not detectable on PC-BCT, likely because they were not included in the PC-BCT volume. In the remaining 20 participants, microcalcifications were detectable. With high between-reader agreement (κ > 0.8), conspicuity of microcalcifications was rated similar for DBT and MIPs of RM-1 (mean, 4.83 ± 0.38 vs 4.86 ± 0.35) ( P = 0.66), but was significantly lower ( P < 0.05) for the remaining PC-BCT reconstructions: 2.11 ± 0.92 (RM-2), 2.64 ± 0.80 (MIPs of RM-2), and 3.50 ± 1.23 (RM-1). Identical distribution qualifiers were assigned for PC-BCT and DBT in 18/20 participants, with excellent agreement (κ = 0.91), whereas identical morphologic qualifiers were assigned in only 5/20, with poor agreement (κ = 0.44). The median size of smallest discernible microcalcification particle was 0.2 versus 0.6 versus 1.1 mm in DBT versus RM-1 versus RM-2 ( P < 0.001), likely due to blooming effects. Average glandular dose was 7.04 mGy (PC-BCT) versus 6.88 mGy (DBT) ( P = 0.67).

Conclusions: PC-BCT allows reliable detection of in-breast microcalcifications as long as they are not located in the posterior part of the breast and allows assessment of their distribution, but not of their individual morphology.

Objectives: To investigate the signal-enhancement properties of the tetrameric gadolinium-based contrast agent (GBCA) gadoquatrane in relation to the administered dose and compare its properties to those of a standard dose of gadobutrol, as a representative of the currently established macrocyclic GBCAs for magnetic resonance imaging.

Materials and methods: In this randomized, single-blind, 4 × 4 crossover study, 43 healthy adults (19-50 years of age) received 3 single IV injections of gadoquatrane (0.01, 0.03, and 0.06 mmol gadolinium/kg body weight) and 1 injection of gadobutrol (0.1 mmol gadolinium/kg body weight) in randomized sequence with 1-week washout periods between administrations. The relative signal enhancement (RSE) was determined in predefined areas of interest in magnetic resonance image sets of the head-neck region. RSE-vs-dose curves (dose-response curves) were established by linear regression, and comparator-equivalent doses were determined by Bayesian inverse regression analysis. Further, 3 blood samples were taken after each injection for pharmacokinetic analyses, and safety data were assessed.

Results: The RSE increased with gadoquatrane dose. A linear function adequately fitted this relationship. In line with the more than 2-fold higher r1 relaxivity of gadoquatrane per gadolinium ion, gadobutrol-equivalent RSE was achieved with gadoquatrane at less than half the gadolinium dose and less than one eighth of the molecule dose.Administration of gadoquatrane and gadobutrol resulted in very similar dose-normalized gadolinium concentrations in plasma, indicating that the pharmacokinetic profiles are essentially the same. Both contrast agents were well tolerated. Adverse events were rare and not dependent on the dose administered.

Conclusions: Gadoquatrane has the potential to be an effective GBCA that can be used at substantially lower doses in clinical routine than the currently established macrocyclic GBCAs.

Objectives: Fibrosis is the final common pathway for chronic kidney disease and the best predictor for disease progression. Besides invasive biopsies, biomarkers for its detection are lacking. To address this, we used hyperpolarized 13 C-pyruvate MRI to detect the metabolic changes associated with fibrogenic activity of myofibroblasts.

Materials and methods: Hyperpolarized 13 C-pyruvate MRI was performed in 2 pig models of kidney fibrosis (unilateral ureteral obstruction and ischemia-reperfusion injury). The imaging data were correlated with histology, biochemical, and genetic measures of metabolism and fibrosis. The porcine experiments were supplemented with cell-line experiments to inform the origins of metabolic changes in fibrogenesis. Lastly, healthy and fibrotic human kidneys were analyzed for the metabolic alterations accessible with hyperpolarized 13 C-pyruvate MRI.

Results: In the 2 large animal models of kidney fibrosis, metabolic imaging revealed alterations in amino acid metabolism and glycolysis. Conversion from hyperpolarized 13 C-pyruvate to 13 C-alanine decreased, whereas conversion to 13 C-lactate increased. These changes were shown to reflect profibrotic activity in cultured epithelial cells, macrophages, and fibroblasts, which are important precursors of myofibroblasts. Importantly, metabolic MRI using hyperpolarized 13 C-pyruvate was able to detect these changes earlier than fibrosis-sensitive structural imaging. Lastly, we found that the same metabolic profile is present in fibrotic tissue from human kidneys. This affirms the translational potential of metabolic MRI as an early indicator of fibrogenesis associated metabolism.

Conclusions: Our findings demonstrate the promise of hyperpolarized 13 C-pyruvate MRI for noninvasive detection of fibrosis development, which could enable earlier diagnosis and intervention for patients at risk of kidney fibrosis.

Objectives: To evaluate the detectability of non-contrast-enhanced and contrast-enhanced spiral breast computed tomography ([non]-CE-SBCT) compared with mammography. Secondary objectives are to determine detectability depending on breast density and to evaluate appearance of breast malignancies according to BI-RADS descriptors.

Methods: This retrospective institutional review board-approved study included 90 women with 105 biopsy-proven malignant breast lesions. Breast density, BI-RADS descriptors, and detectability were evaluated by 2 independent readers. Diagnostic confidence was rated on a 4-point Likert scale.

Results: For readers 1 and 2, detectability was 83.8% and 80.0% for mammography, 99.1% and 99.1% for CE-SBCT ( P < 0.05), and 66.7% and 61.9% for non-CE-SBCT ( P < 0.05). With both readers, detectability in CE-SBCT was high for density A/B/C/D (both 100%/100%/100%/87.5%). Detectability of readers declined with increasing density for mammography (density A = 100%, B = 89.1% and 95.1%, C = 73.1%, D = 50.0% and 71.4%; P < 0.05) and for non-CE-SBCT (density A = 87.5% and 90.7%, B = 65.5% and 69.1%, C = 54.8% and 60.0%, D = 37.5%; P < 0.05). Mass lesions were detected with CT as often as with mammography, whereas architectural distortions and microcalcifications were detected less often with SBCT. Diagnostic confidence was very high or high in 97.2% for CE-SBCT, in 74.1% for non-CE-SBCT, and in 81.4% for mammography.

Conclusions: Detectability and diagnostic confidence were very high in CE-SBCT, regardless of breast density. The detectability of non-CE-SBCT was lower than that of mammography and declined with increasing breast density.

Objectives: The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms.

Materials and methods: This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm 3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria.

Results: The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938 ) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939 ). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions ( P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality ( P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration ( P = 0.087), image noise of T1-weighted images at 2-fold acceleration ( P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration ( P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions ( P ≥ 0.082).

Conclusions: This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

Objectives: Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI.

Materials and methods: We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS.

Results: Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05).

Conclusions: Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.

Objectives: Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics.

Materials and methods: We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models.

Results: Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign ( P < 0.001) and cortical thickening ( P < 0.001). In contrast, AI models were sensitive to abnormal gyration ( P < 0.01) or gray-white matter blurring ( P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs ( P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) ( P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17).

Conclusions: This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.

Objectives: The aim of this study was to evaluate the feasibility and efficacy of chronic pulmonary thromboembolism assessment using photon-counting detector computed tomography (PCD-CT) iodine maps of the lung parenchyma.

Materials and methods: This institutional review board-approved retrospective study included 83 subjects (49.4% male, aged 62.4 ± 13.4 years; 50.6% female, aged 59.9 ± 17.1 years) who underwent clinically indicated PCD-CT scan to rule out chronic thromboembolic pulmonary hypertension (CTEPH). Two blinded readers used iodine maps and corresponding sharp-kernel CT reconstructions in the lung window to evaluate perfusion defects and identify patients with chronic pulmonary thromboembolism (CTEPH, CTEPH overlap with other causes of pulmonary hypertension [PH], chronic thromboembolic disease [CTED]). No other clinical or imaging information was given. Discordance was resolved in a subsequent consensus read. The clinical diagnosis was reviewed in an interdisciplinary clinical setting. The accuracy, sensitivity, and specificity of radiologic evaluation and clinical diagnosis were calculated.

Results: Of the 83 subjects included, 32 were diagnosed with CTEPH, CTEPH overlap, or CTED, 35 were diagnosed with PH caused by other pathologic mechanisms, 10 had no PH, and 6 had suffered previous acute pulmonary embolism, which resolved. The interreader agreement was good (Cohen κ = 0.74). The consensus reached high accuracy (0.88), sensitivity (0.94), and specificity (0.84), as well as good agreement with interdisciplinary clinical diagnosis (Cohen κ = 0.75). No cases with confirmed CTEPH as the primary cause of PH or CTED were missed. Patients with pulmonary arterial hypertension were most frequently rated false-positive. The mean effective dose (±standard deviation) was 1.3 (±0.76) mSv.

Conclusions: Accurate, sensitive, and specific diagnosis of pulmonary chronic thromboembolism at low radiation dose is possible using iodine maps reconstructed from PCD-CT scans.