European Radiology Experimental最新文献

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

Background: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies.

Methods: This prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used.

Results: Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081).

Conclusions: TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.

Relevance statement: Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.

Key points: • Lumbar spine MRI with deep learning reconstruction has broad application prospects. • Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality. • When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.

Background: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference.

Methods: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1-4), slice thicknesses (0.4-8 mm), and virtual monoenergetic imaging levels (VMI, 40-90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland-Altman plots, and Lin's concordance correlation coefficient (CCC) were used.

Results: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1-1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV).

Conclusions: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV.

Relevance statement: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Key points: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Background: Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques.

Methods: Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software).

Results: When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software.

Conclusions: Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques.

Relevance statement: Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation.

Key points: • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.

Background: New immunotherapies activate tumor-associated macrophages (TAMs) in the osteosarcoma microenvironment. Iron oxide nanoparticles (IONPs) are phagocytosed by TAMs and, therefore, enable TAM detection on T2*- and T2-weighted magnetic resonance images. We assessed the repeatability and reproducibility of T2*- and T2-mapping of osteosarcomas in a mouse model.

Methods: Fifteen BALB/c mice bearing-murine osteosarcomas underwent magnetic resonance imaging (MRI) on 3-T and 7-T scanners before and after intravenous IONP infusion, using T2*-weighted multi-gradient-echo, T2-weighted fast spin-echo, and T2-weighted multi-echo sequences. Each sequence was repeated twice. Tumor T2 and T2* relaxation times were measured twice by two independent investigators. Repeatability and reproducibility of measurements were assessed.

Results: We found excellent agreement between duplicate acquisitions for both T2* and T2 measurements at either magnetic field strength, by the same individual (repeatability), and between individuals (reproducibility). The repeatability concordance correlation coefficient (CCC) for T2* values were 0.99 (coefficients of variation (CoV) 4.43%) for reader 1 and 0.98 (CoV 5.82%) for reader 2. The reproducibility of T2* values between the two readers was 0.99 (CoV 3.32%) for the first acquisitions and 0.99 (CoV 6.30%) for the second acquisitions. Regarding T2 values, the repeatability of CCC was similar for both readers, 0.98 (CoV 3.64% for reader 1 and 4.45% for reader 2). The CCC of the reproducibility of T2 was 0.99 (CoV 3.1%) for the first acquisition and 0.98 (CoV 4.38%) for the second acquisition.

Conclusions: Our results demonstrated high repeatability and reproducibility of quantitative T2* and T2 mapping for monitoring the presence of TAMs in osteosarcomas.

Relevance statement: T2* and T2 measurements of osteosarcomas on IONP-enhanced MRI could allow identifying patients who may benefit from TAM-modulating immunotherapies and for monitoring treatment response. The technique described here could be also applied across a wide range of other solid tumors.

Key points: • Optimal integration of TAM-modulating immunotherapies with conventional chemotherapy remains poorly elucidated. • We found high repeatability of T2* and T2 measurements of osteosarcomas in a mouse model, both with and without IONPs contrast, at 3-T and 7-T MRI field strengths. • T2 and T2* mapping may be used to determine response to macrophage-modulating cancer immunotherapies.

Background: Dual-energy computed tomography (DECT) is useful for detecting gouty tophi. While iodinated contrast media (ICM) might enhance the detection of monosodium urate crystals (MSU), higher iodine concentrations hamper their detection. Calculating virtual noncontrast (VNC) images might improve the detection of enhancing tophi. The aim of this study was to evaluate MSU detection with VNC images from DECT acquisitions in phantoms, compared against the results with standard DECT reconstructions.

Methods: A grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were scanned with sequential single-source DECT using an ascending order of tube current time product at 80 kVp (16.5-220 mAs) and 135 kVp (2.75-19.25 mAs). VNC images were equivalently reconstructed at 80 and 135 kVp. Two-material decomposition analysis for MSU detection was applied for the VNC and conventional CT images. MSU detection and attenuation values were compared in both modalities.

Results: For 0, 0.25, 0.5, 1, and 2% ICM, the average detection indices (DIs) for all MSU concentrations (35-50%) with VNC postprocessing were respectively 25.2, 36.6, 30.9, 38.9, and 45.8% for the grid phantom scans and 11.7, 9.4, 5.5, 24.0, and 25.0% for the porcine phantom scans. In the conventional CT image group, the average DIs were respectively 35.4, 54.3, 45.4, 1.0, and 0.0% for the grid phantom and 19.4, 17.9, 3.0, 0.0, and 0.0% for the porcine phantom scans.

Conclusions: VNC effectively reduces the suppression of information caused by high concentrations of ICM, thereby improving the detection of MSU.

Relevance statement: Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.

Key points: • Highly concentrated contrast media hinders monosodium urate crystal detection in CT imaging • Virtual noncontrast imaging redetects monosodium urate crystals in high-iodinated contrast media concentrations. • Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.

Background: To study the reproducibility of ²³Na magnetic resonance imaging (MRI) measurements from breast tissue in healthy volunteers.

Methods: Using a dual-tuned bilateral ²³Na/¹H breast coil at 3-T MRI, high-resolution ²³Na MRI three-dimensional cones sequences were used to quantify total sodium concentration (TSC) and fluid-attenuated sodium concentration (FASC). B₁-corrected TSC and FASC maps were created. Two readers manually measured mean, minimum and maximum TSC and mean FASC values using two sampling methods: large regions of interest (LROIs) and small regions of interest (SROIs) encompassing fibroglandular tissue (FGT) and the highest signal area at the level of the nipple, respectively. The reproducibility of the measurements and correlations between density, age and FGT apparent diffusion coefficient (ADC) values were evaluatedss.

Results: Nine healthy volunteers were included. The inter-reader reproducibility of TSC and FASC using SROIs and LROIs was excellent (intraclass coefficient range 0.945-0.979, p < 0.001), except for the minimum TSC LROI measurements (p = 0.369). The mean/minimum LROI TSC and mean LROI FASC values were lower than the respective SROI values (p < 0.001); the maximum LROI TSC values were higher than the SROI TSC values (p = 0.009). TSC correlated inversely with age but not with FGT ADCs. The mean and maximum FGT TSC and FASC values were higher in dense breasts in comparison to non-dense breasts (p < 0.020).

Conclusions: The chosen sampling method and the selected descriptive value affect the measured TSC and FASC values, although the inter-reader reproducibility of the measurements is in general excellent.

Relevance statement: ²³Na MRI at 3 T allows the quantification of TSC and FASC sodium concentrations. The sodium measurements should be obtained consistently in a uniform manner.

Key points: • ²³Na MRI allows the quantification of total and fluid-attenuated sodium concentrations (TSC/FASC). • Sampling method (large/small region of interest) affects the TSC and FASC values. • Dense breasts have higher TSC and FASC values than non-dense breasts. • The inter-reader reproducibility of TSC and FASC measurements was, in general, excellent. • The results suggest the importance of stratifying the sodium measurements protocol.

Background: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is a largely adopted non-invasive technique for assessing cerebrovascular diseases. We aimed to optimize the 7-T TOF-MRA acquisition protocol, confirm that it outperforms conventional 3-T TOF-MRA, and compare 7-T TOF-MRA with digital subtraction angiography (DSA) in patients with different vascular pathologies.

Methods: Seven-tesla TOF-MRA sequences with different spatial resolutions acquired in four healthy subjects were compared with 3-T TOF-MRA for signal-to-noise and contrast-to-noise ratios as well as using a qualitative scale for vessel visibility and the quantitative Canny algorithm. Four patients with cerebrovascular disease (primary arteritis of the central nervous system, saccular aneurism, arteriovenous malformation, and dural arteriovenous fistula) underwent optimized 7-T TOF-MRA and DSA as reference. Images were compared visually and using the complex-wavelet structural similarity index.

Results: Contrast-to-noise ratio was higher at 7 T (4.5 ± 0.8 (mean ± standard deviation)) than at 3 T (2.7 ± 0.9). The mean quality score for all intracranial vessels was higher at 7 T (2.89) than at 3 T (2.28). Angiogram quality demonstrated a better vessel border detection at 7 T than at 3 T (44,166 versus 28,720 pixels). Of 32 parameters used for diagnosing cerebrovascular diseases on DSA, 27 (84%) were detected on 7-T TOF-MRA; the similarity index ranged from 0.52 (dural arteriovenous fistula) to 0.90 (saccular aneurysm).

Conclusions: Seven-tesla TOF-MRA outperformed conventional 3-T TOF-MRA in evaluating intracranial vessels and exhibited an excellent image quality when compared to DSA. Seven-tesla TOF-MRA might improve the non-invasive diagnostic approach to several cerebrovascular diseases.

Relevance statement: An optimized TOF-MRA sequence at 7 T outperforms 3-T TOF-MRA, opening perspectives to its clinical use for noninvasive diagnosis of paradigmatic pathologies of intracranial vessels.

Key points: • An optimized 7-T TOF-MRA protocol was selected for comparison with clinical 3-T TOF-MRA for assessing intracranial vessels. • Seven-tesla TOF-MRA outperformed 3-T TOF-MRA in both quantitative and qualitative evaluation. • Seven-tesla TOF-MRA is comparable to DSA for the diagnosis and characterization of intracranial vascular pathologies.

Background: Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) contrast and additional parameter maps versus reference TSE sequences in an in situ model of human cartilage defects.

Methods: Standardized cartilage defects of 8, 5, and 3 mm in diameter were created in the lateral femora of ten human cadaveric knee specimens (81 ± 10 years old; nine males, one female). MIXTURE sequences providing proton density-weighted fat-saturated images and T2 maps or T1-weighted images and T1ρ maps as well as the corresponding two- and three-dimensional TSE reference sequences were acquired before and after defect creation (3-T scanner; knee coil). Defect delineability, bone texture, and cartilage relaxation times were quantified. Appropriate parametric or non-parametric tests were used.

Results: Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences (p ≤ 0.47). After defect creation, relaxation times significantly increased in the central femur (T2_pre = 51 ± 4 ms [mean ± standard deviation] versus T2_post = 56 ± 4 ms; p = 0.002) and all regions combined (T1ρ_pre = 40 ± 4 ms versus T1ρ_post = 43 ± 4 ms; p = 0.004).

Conclusions: MIXTURE permitted time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1ρ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defects and bone texture, were comparable between MIXTURE and reference sequences.

Relevance statement: Equally time-efficient and versatile, the MIXTURE sequence platform combines morphologic imaging using familiar contrasts, excellent image correspondence versus corresponding reference sequences and quantitative mapping information, thereby increasing the diagnostic value beyond mere morphology.

Key points: • Combined morphologic and quantitative MIXTURE sequences are based on three-dimensional TSE contrasts. • MIXTURE sequences were studied in an in situ human cartilage defect model. • Morphologic image features, i.e., defect delineabilty and bone texture, were investigated. • Morphologic image features were similar between MIXTURE and reference sequences. • MIXTURE allowed time-efficient simultaneous morphologic and quantitative knee joint assessment.

Background: Deuterium metabolic imaging (DMI) has emerged as a promising non-invasive technique for studying metabolism in vivo. This review aims to summarize the current developments and discuss the futures in DMI technique in vivo.

Methods: A systematic literature review was conducted based on the PRISMA 2020 statement by two authors. Specific technical details and potential applications of DMI in vivo were summarized, including strategies of deuterated metabolites detection, deuterium-labeled tracers and corresponding metabolic pathways in vivo, potential clinical applications, routes of tracer administration, quantitative evaluations of metabolisms, and spatial resolution.

Results: Of the 2,248 articles initially retrieved, 34 were finally included, highlighting 2 strategies for detecting deuterated metabolites: direct and indirect DMI. Various deuterated tracers (e.g., [6,6'-²H2]glucose, [2,2,2'-²H3]acetate) were utilized in DMI to detect and quantify different metabolic pathways such as glycolysis, tricarboxylic acid cycle, and fatty acid oxidation. The quantifications (e.g., lactate level, lactate/glutamine and glutamate ratio) hold promise for diagnosing malignancies and assessing early anti-tumor treatment responses. Tracers can be administered orally, intravenously, or intraperitoneally, either through bolus administration or continuous infusion. For metabolic quantification, both serial time point methods (including kinetic analysis and calculation of area under the curves) and single time point quantifications are viable. However, insufficient spatial resolution remains a major challenge in DMI (e.g., 3.3-mL spatial resolution with 10-min acquisition at 3 T).

Conclusions: Enhancing spatial resolution can facilitate the clinical translation of DMI. Furthermore, optimizing tracer synthesis, administration protocols, and quantification methodologies will further enhance their clinical applicability.

Relevance statement: Deuterium metabolic imaging, a promising non-invasive technique, is systematically discussed in this review for its current progression, limitations, and future directions in studying in vivo energetic metabolism, displaying a relevant clinical potential.

Key points: • Deuterium metabolic imaging (DMI) shows promise for studying in vivo energetic metabolism. • This review explores DMI's current state, limits, and future research directions comprehensively. • The clinical translation of DMI is mainly impeded by limitations in spatial resolution.