European Radiology Experimental最新文献

Background: To demonstrate that 7-T magnetic resonance imaging (MRI) provides a surrogate for histopathology of fresh ex vivo liver tissue, using the case study of liver fibrosis.

Methods: We prospectively enrolled 20 patients undergoing surgical liver resection between November 2021 and April 2023. Each ex vivo fresh liver tissue specimen (~ 1 cm³) was sectioned in half. The first half, stained using Masson's Trichrome and Perls, was assessed by three pathologists using the METAVIR score (reference standard). The second half was imaged with 7-T MRI using a cryoprobe (fat-suppressed T2-weighted turbo/fast spin-echo sequence, spatial resolution 75 × 75 × 200 µm³) and assessed by three radiologists and the same three pathologists, using a newly developed MRI-METAVIR score.

Results: Five patients were excluded from the final analysis (one patient due to poor specimen quality, two due to surgery cancellation, and two previously published used for reader training). Of the remaining 15 patients, 10 (67%) presented with chronic liver diseases and 8/15 (53%) with advanced (F3 or F4) fibrosis. Radiologists achieved 88% sensitivity, 100% specificity, 93% accuracy (95% confidence interval 68-100%) and 0.94 Harrell's c-index (0.86-1.00). Pathologists achieved 88% sensitivity, 86% specificity, 87% accuracy (60-98%) and 0.87 Harrell's c-index (0.74-0.99). There were no statistically significant differences between MRI-based and pathologic reference standard stage (p ≥ 0.655).

Conclusion: With an in-plane spatial resolution of ~ 75 × 75 µm², MRI paralleled low-magnification histology, enabling the assessment of micro-architectural liver changes, and provided a surrogate for histopathology examination of fresh ex vivo liver tissue samples at a microscopic level.

Relevance statement: 7-T MRI provides a surrogate for histopathology visualisation of fresh ex vivo liver tissue, opening new research perspectives for clinical high-field MRI of the liver.

Key points: Using the newly developed MRI-METAVIR score, 7-T MRI data strongly correlated with histopathology, achieving excellent agreement and accuracy. 7-T MRI accurately differentiated advanced from minimal liver fibrosis. 7-T MRI visualises liver micro-architecture, enabling pathology-like, noninvasive three-dimensional imaging.

Background: Pelvimetry is essential in obstetrics for delivery planning. While computed tomography (CT) is the standard, magnetic resonance imaging (MRI) offers a radiation-free alternative with zero echo time (ZTE) and black bone (BB) sequences providing high bone-to-soft tissue contrast within short scan times. This proof-of-concept study evaluates the reliability of these sequences and the agreement with CT for pelvimetry in a predominantly elderly population.

Methods: This retrospective study included 21 female patients who underwent 3-T whole-body MRI including ZTE and BB sequences and ¹⁸fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT with optimized low-dose whole-body CT. Obstetric conjugate diameter (OCD), interspinous diameter (ISD), and median transverse diameter (MTD) were measured by five radiologists. Intra-reader, inter-reader, and inter-technique agreement were assessed using intraclass correlation coefficient (ICC) and repeatability/reproducibility coefficients.

Results: Intra-reader agreement was good regardless of diameter or reader: all ICC ≥ 0.90, repeatability ranging from ± 0.26 to ± 0.48 cm (CT), ± 0.30 to ± 0.52 cm (BB), and ± 0.29 to ± 0.67 cm (ZTE). The inter-reader agreement was good regardless of sequence: all ICC ≥ 0.88, reproducibility ranging from ± 0.39 to ± 0.42 (OCD), ± 0.26 to ± 0.51 cm (ISD), and ± 0.53 to ± 0.58 cm (MTD). ZTE and BB showed similar agreement with CT: ± 0.57 to ± 0.81 cm when including inter-reader variability; ± 0.34 to ± 0.47 cm for only intra-reader variability.

Conclusion: ZTE and BB sequences provided reliable measurements with good agreement with CT, for obstetric pelvimetry. Further validation in the context of pregnancy is needed.

Relevance statement: MRI-based pseudo-CT sequences are a promising radiation-free alternative to CT for obstetric pelvimetry, offering the prospect of accurate, reliable measurements of pelvic diameters in pregnant women.

Trial registration: The population included female patients with suspected multiple myeloma from a previous prospective oncology trial (ClinicalTrials.gov: NCT05381077).

Key points: This study explores pseudo-CT MRI sequences for radiation-free non-invasive obstetric pelvimetry. Pseudo-CT zero echo time and black bone sequences provide repeatable and reproducible measurements of pelvic diameters. Pseudo-CT MRI sequences show good inter-technique agreement with the reference CT.

Background: Conventional magnetic resonance diffusion-weighted imaging (DWI) and morphological features have limitations in distinguishing benign from metastatic retropharyngeal lymph nodes (RLNs). We aimed to compare the value of continuous-time random walk (CTRW), fractional-order calculus (FROC), stretched-exponential model (SEM), and conventional DWI, in combination with morphological features, for differentiating between the two groups.

Methods: Fifty-nine patients with 68 RLNs (23 benign and 45 metastatic) were enrolled. All patients underwent DWI with 12 b-values. Diffusion data were reconstructed using conventional DWI, SEM, FROC, and CTRW models, yielding nine parameters: apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC)_SEM, α_SEM, D_FROC, β_FROC, μ_FROC, D_CTRW, α_CTRW, and β_CTRW. Diffusion parameters and morphological features were compared using Mann-Whitney U, independent sample t, or χ² tests. Logistic regression analysis was performed to identify the best diffusion indicator for classification and to develop a multiparameter model combining morphological features. Area under the receiver operating curve (AUC) and DeLong tests were used.

Results: Significant differences in diffusion parameters were found between benign and metastatic RLNs, except for α_CTRW (p ≤ 0.022). Benign RLNs exhibited higher ADC, DDC_SEM, D_FROC, and D_CTRW, while metastatic RLNs had higher α_SEM, β_FROC, μ_FROC, and β_CTRW. Multivariate logistic regression analysis identified β_CTRW as the optimal single diffusion indicator (AUC = 0.913). The combined model of β_CTRW with morphological features further improved diagnostic performance and yielded an AUC of 0.948.

Conclusion: β_CTRW is an effective noninvasive biomarker for distinguishing between benign and metastatic RLNs. Thus, combining β_CTRW with morphological features enhances diagnostic efficiency.

Relevance statement: This study demonstrates that β_CTRW, derived from the continuous-time random walk diffusion model, when integrated with morphological features, offers a reliable, noninvasive diagnostic approach for differentiating between benign and metastatic retropharyngeal lymph nodes.

Key points: Non-Gaussian diffusion metrics outperformed conventional DWI. β_CTRW was the best indicator for distinguishing benign from metastatic lymph nodes. Combining β_CTRW with minimal axial diameter further improved diagnostic efficiency.

Background: ³¹P-magnetic resonance spectroscopy (MRS) saturation transfer (ST) allows for noninvasive investigation of liver energy metabolism by assessing flux rates of adenosine triphosphate (ATP) synthesis. However, this technique has rarely been applied at clinical field strengths because of long examination times and contamination from muscle tissue. Our aim was to establish a new method to robustly assess ATP synthesis using a clinical scanner.

Methods: A prospective single-center study was performed (January 2023-August 2024) within the German Diabetes Study. We established a suitable ³¹P-MRS ST protocol, tested it in vitro and in vivo and assessed its reproducibility. We assessed the hepatic apparent spin-lattice relaxation time of inorganic phosphate ( $T_{1,Pi}^{\text{'}}$ ), equilibrium forward rate constant ( $k_f$ ), and forward ATP synthesis rate ( $F_{ATP}$ ) in nine control volunteers (CON) (six females) and eight patients (five females) with type 1 diabetes (T1D) and compared differences by ANOVA.

Results: Reproducibility assessment in nine CON, aged 27 ± 4 years (mean ± standard deviation), yielded coefficients of variation for repeated measurements of 7.1% and 21.3% for $T_{1,Pi}^{\text{'}}$ and $k_f$ , respectively. Group comparison revealed higher hepatic $k_f$ (0.34 ± 0.03 s^-1 versus 0.16 ± 0.03 s^-1; p = 0.001) and $F_{ATP}$ (35.3 ± 3.5 mM/min versus 16.4 ± 3.5 mM/min; p = 0.002) in CON than in T1D, aged 42 ± 15 years, respectively.

Conclusion: This ³¹P-MRS ST method allowed for robust assessment of hepatic ATP synthesis at clinical field strength and was sensitive enough to detect differences between CON and T1D volunteers.

Relevance statement: Noninvasive methods to investigate hepatic energy metabolism are urgently needed to evaluate liver health while preventing unnecessary biopsies. For broad clinical applicability, the robustness shown by the proposed method at clinical field strength is crucial.

Trial registration: ClinicalTrials.gov: NCT01055093-Prospective study on diabetes mellitus and its complications in newly diagnosed adult patients (GDC), NCT01055093, Registered: 01/22/2010, https://clinicaltrials.gov/study/N

Background: Gunshot deaths due to homicide or military encounters are a major health concern. Noninvasive bullet characterization is of major importance for patients with lodged bullets or in mass disasters with multiple cadavers, which must be prioritized for autopsy. Therefore, the aim of this study was to investigate whether brass and lead bullets can be differentiated using photon-counting CT (PCCT).

Methods: Nine different lead (n = 6) or brass (n = 3) bullets were investigated on a state-of-the-art PCCT using a clinically unavailable research mode. Here, four image sets were reconstructed for different energy thresholds (20, 55, 72, 90 keV). Three circular regions of interest were placed on the 20-keV threshold images by two readers and automatically copied to the three other threshold images. Based on measured HU mean and max values, dual-energy indices (DEI) were calculated for the low/high energy threshold pairs of 20/90, 55/90, and 72/90 keV.

Results: Significant differences of DEIs between lead and brass projectiles were observed for the 20/90 keV DEI for HU mean ± standard deviation values (Qr40 kernel, lead: -0.085 ± 0.021, brass: 0.024 ± 0.048) and HU max values (Qr40 kernel, lead: -0.093 ± 0.011, brass: 0.023 ± 0.057) (p < 0.001 for both). Differences decreased for the 55/90 and 72/90 keV DEIs between the two projectile materials but remained statistically significant.

Conclusion: In this PCCT phantom study, significant differences were observed between lead and brass bullets in the different energy threshold images.

Relevance statement: Photon-counting CT could be a promising tool for bullet identification as significant differences were found in the different energy threshold images for lead and brass bullets, with application in clinical and forensic radiology.

Key points: In emergency settings, noninvasive bullet characterization is of importance for law enforcement. Bullet material characterization can be performed using photon-counting CT. These characteristics can be quantified in the four different energy threshold images.

Background: The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.

Methods: In this single-center study, patients with nodules and ≤ 2 chest computed tomography (CT) examinations were retrospectively selected. An AI-based algorithm was used for automated nodule detection and matching. The matching rate and the causes for incorrect matching were evaluated for the ten largest lesions (5-30 mm in diameter) registered on baseline CT. The dependence of the matching rate on nodule number and localization was also analyzed.

Results: One hundred patients (46 females), with a median age of 62 years (interquartile range 57-69), and 253 CTs were included. Focusing on the ten largest lesions, 1,141 lesions were identified, of which 36 (3.2%) were other structures incorrectly identified as nodules (false-positives). Of the 1,105 identified nodules, 964 (87.2%) were correctly detected and matched. The matching rate for nodules registered in both baseline and follow-up scans was 97.8%. The matching rate per case ranged 80.0-100.0% (median 90.0%). Correct matching rate decreased in follow-up examinations to over 50 nodules (p = 0.003), with an overrepresentation of missed matching. Matching rates were higher in parenchymal (91.8%), peripheral (84.4%), and juxtavascular (82.4%) nodules than in juxtaphrenic nodules (71.1%) (p < 0.001). Missed matching was overrepresented in juxtavascular, and incorrect assignment in juxtaphrenic nodules.

Conclusion: The correct automated-matching rate of metastatic pulmonary nodules in follow-up examinations was high, but it depends on localization and a number of nodules.

Relevance statement: The algorithm enables precise follow-up matching of pulmonary nodules, potentially providing a solid basis for standardized and accurate evaluations. Understanding the algorithm's strengths and weaknesses based on nodule localization and number enhances the interpretation of AI-based results.

Key points: The AI algorithm achieved a correct nodule matching rate of 87.2% and up to 97.8% when considering nodules detected in both baseline and follow-up scans. Matching accuracy depended on nodule number and localization. This algorithm has the potential to support response evaluation criteria in solid tumor-based evaluations in clinical practice.

Background: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS (VirtuT2w) images from routine multiparametric breast MRI images.

Methods: This IRB-approved, retrospective study included 914 breast MRI examinations from January 2017 to June 2020. The dataset was divided into training (n = 665), validation (n = 74), and test sets (n = 175). The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. Quantitative metrics were used to evaluate the different input protocols. A qualitative assessment by two radiologists was used to evaluate the VirtuT2w images of the best input protocol.

Results: VirtuT2w images demonstrated the best quantitative metrics compared to original T2w-FS images for an input protocol using all of the available data. A high level of high-frequency error norm (0.87) indicated a strong blurring presence in the VirtuT2 images, which was also confirmed by qualitative reading. Radiologists correctly identified VirtuT2 images with at least 96% accuracy. Significant difference in diagnostic image quality was noted for both readers (p ≤ 0.015). Moderate inter-reader agreement was observed for edema detection on both T2w-FS images (κ = 0.49) and VirtuT2 images (κ = 0.44).

Conclusion: The 2D-U-Net generated virtual T2w-FS images similar to real T2w-FS images, though blurring remains a limitation. Investigation of other architectures and using larger datasets is necessary to improve potential future clinical applicability.

Relevance statement: Generating VirtuT2 images could potentially decrease the examination time of multiparametric breast MRI, but its quality needs to improve before introduction into a clinical setting.

Key points: Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks. Image blurring in virtual T2w-FS images currently limits their clinical applicability. Best quantitative performance could be achieved when using full dynamic-contrast-enhanced acquisition and DWI as input of the neural network.

Background: To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI).

Methods: Fifty patients undergoing gadoxetate-enhanced MRI were recruited. Two T1-weighted Dixon sequences were utilized: a 17-s breath-hold acquisition and an accelerated 12-s breath-hold acquisition (reduced phase resolution), both reconstructed using neural network (NN) and iterative denoising (ID), NN-alone, ID-alone, and the standard method. Contrast-to-noise ratio (CNR) was assessed quantitatively for all series (ANOVA). Two blinded radiologists independently analyzed three image sets: 17-s acquisition reconstructed with NN and ID (17-s NN + ID), 12-s acquisition reconstructed with NN and ID (12-s NN + ID), and 17-s acquisition with standard reconstruction (17-s standard). Overall image quality, qualitative CNR, lesion edge sharpness, vessel edge sharpness, and respiratory motion artifacts were scored (4-point Likert scale) and compared (Friedman test). Lesion detection was compared between 12-s NN + ID and 17-s standard reconstructions (Wilcoxon signed-rank test).

Results: Quantitative liver-to-portal vein CNR was significantly higher for 17-s NN + ID than 17-s standard or 17-s NN-alone images (p = 0.001). Scores for overall image quality, qualitative CNR, vessel edge sharpness, and lesion edge sharpness were significantly higher for 17-s NN + ID and 12-s NN + ID than standard reconstruction (p < 0.001); there was no significant difference between 17-s and 12-s NN + ID. There was no significant difference in respiratory motion artifacts and number of lesions or diameter of the smallest detected lesion using 12-s NN + ID or 17-s standard reconstruction.

Conclusion: AI-augmented reconstructions can improve image quality while reducing breath-hold duration in T1-weighted hepatobiliary-phase gadoxetate-enhanced MRI, without compromising lesion detection.

Relevance statement: AI-augmented reconstruction of T1-weighted MRI improves image quality and lesion detection in hepatobiliary phase liver imaging, reducing breath-hold duration without compromising clinical lesion detection.

Key points: Liver-to-portal vein CNR was significantly higher for 17-s NN + ID. AI-augmented reconstructions scored higher for image quality, contrast-to-noise, vessel-edge, and lesion-edge sharpness. No significant difference in lesion detection between 12-s NN + ID and 17-s standard reconstructions.

Background: We compared photon-counting detector computed tomography (PCD-CT) polyenergetic images, PCD-CT virtual monoenergetic images (VMI), and energy-integrating detector computed tomography (EID-CT) polyenergetic images regarding bone visualization and metal artifacts in patients with titanium wrist prostheses.

Methods: After ethical approval, 15 patients were examined with PCD-CT and EID-CT. Polyenergetic images were reconstructed, as well as 130-keV VMI for PCD-CT. Five radiologists evaluated bone visualization, interpretability at metal-bone interface and metal artifacts using a 7-point ordinal scale. Streak artifacts and artifacts at the bone-metal interface were quantitatively assessed. Differences between image setups were analyzed using Friedman test and one-way ANOVA with post hoc tests.

Results: Bone visualization was superior in PCD-CT polyenergetic images (median rating 6, range 3-7) compared with VMI (5, 3-7; p < 0.001) and EID-CT (5, 3-7; p = 0.018). Streak artifacts were more pronounced with PCD-CT polyenergetic images (4, 3-6) compared with EID-CT (5, 4-6; p = 0.003) and PCD-CT VMI (5, 3-7; p = 0.002), with quantitative results showing least streak artifacts in PCD-CT VMI, followed by EID-CT and PCD-CT polyenergetic images (50 ± 7%, 70 ± 6%, and 79 ± 5%, respectively; p < 0.001). Interpretability at bone-metal interface was better with PCD-CT polyenergetic images (5, 2-7; p = 0.045) and EID-CT (5, 3-6; p = 0.018) compared with PCD-CT VMI (4, 2-6), without quantitative differences.

Conclusion: Streak artifacts from titanium wrist prostheses were reduced using 130-keV PCD-CT VMI, while bone visualization was highest using PCD-CT polyenergetic images.

Relevance statement: In patients with wrist implants, photon-counting detector CT allows for effective metal artifact reduction using virtual monoenergetic images and improved bone visualization using polyenergetic images. As polyenergetic images and VMI have different advantages, access to both image setups may benefit diagnostic evaluation.

Key points: Virtual monoenergetic images (VMI) presented a substantial reduction of metal streak artifacts. Polyenergetic images exhibited better image quality for bone imaging compared with VMI. A combination of image reconstructions should be preferred depending on the diagnostic task.

Background: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

Methods: This retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.

Results: The model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.

Conclusion: The DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%.

Relevance statement: DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning.

Key points: Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.