Spin-echo MRI Research Articles

Quantification of phase separation in high moisture soy protein extrudates by NMR and MRI

High-moisture (HM) extrusion is the dominant industrial process to create structured plant-based protein products that can be used for animal-free meat alternatives. Yet, the underlying mechanisms, such as phase separation, that govern structure formation in plant-protein extrudates, are still poorly understood. Current hypotheses require experimental data in order to be verified, but measurement techniques able to quantify phase-separated anisotropic protein extrudates are lacking, or have yet to be validated. In this study, Low-Field Time Domain (LF TD)-NMR and High-Field (HF) MRI techniques have been employed to unravel phase separation in HM extrudates of soy proteins. Results show that swelling with water enhances the 1H NMR/MRI signal-to-noise ratio in the measurements and unveils the presence of lamellar regions, while freeze-thawing enhances phase separation due to freeze concentration. Phase separation could be quantified by the observation of two distinct populations by LF TD-NMR T2 measurements. MRI images of dead-stop ribbon samples from HM extrusion revealed how the thickness of the aligned lamellar regions increases during passage of the protein melt through the cooling die. We conclude that TD-NMR can quantify phase separation, while spin-echo MRI can spatially resolve the lamellar structure conformation of HM extrudates. Thus, NMR and MRI are powerful techniques for non-invasively characterizing ex situ structure formation during HM extrusion, and for validating hypotheses on shear- and temperature-induced phase separation.

Predicting incident radiographic knee osteoarthritis through quantitative meniscal lesion parameters: data from the osteoarthritis initiative

BackgroundThis study investigates the potential of novel meniscal parameters as predictive factors for incident radiographic knee osteoarthritis (ROA) over a span of four years, as part of the Osteoarthritis Initiative (OAI) study.ObjectivesQuantitative measurements of meniscal parameters alteration could serve as predictors of OA’s occurrence and progression.Methods and materialsA nested matched case-control study design was used to select participants from OAI study. Case knees (n = 178) were defined as those with incident ROA (Kellgren Lawrence Grade (KLG) 0 or 1 at baseline (BL), evolving into KLG 2 or above by year 4). Control knees were matched one-to-one by sex, age and radiographic status with case knees. The mean distance from medial-to-lateral meniscal lesions [Mean(MLD)], mean value of tibial plateau width [Mean(TPW)] and the mean of the relative percentage of the medial-to-lateral meniscal lesions distance [Mean(RMLD)] were evaluated through coronal T2-weighted turbo spin echo (TSE) MRI at P-0 (visit when incident ROA was found on radiograph), P-1(one year prior to P-0) and baseline, respectively. Using the imaging data of one patient, the mechanism was investigated by finite element analysis.ResultsParticipants were on average 60.22 years old, predominantly female (66.7%) and overweight (mean BMI: 28.15). Mean(MLD) and Mean(RMLD) were significantly greater for incident knees compared to no incident knees at baseline, P-1 and P-0. [Mean(MLD), Mean(RMLD); (42.56–49.73) mean ± (7.70–9.52) mm SD vs. (38.14–40.78) mean ± (5.51–7.05)mm SD; (58.61–68.95) mean ± (8.52–11.40) mm SD vs. (52.52–56.35) mean ± (6.53–7.85)mm SD, respectively]. Baseline Mean(MLD) and Mean(RMLD), [Adjusted OR, 95%CI: 1.11(1.07 to 1.16) and 1.13(1.09 to 1.17), respectively], were associated with incident ROA during 4 years, However, Mean(TPW) [Adjusted OR, 95%CI: 0.98(0.94 to 1.02)] was not associated with incident ROA during 4 years. While Mean(TPW) at P-1 and P-0 was not associated with the risk of incident ROA, Mean(MLD) and Mean(RMLD) at P-1 and P-0 were significantly positively associated with the risk of incident ROA.ConclusionsThe meniscal parameters alteration could be an important imaging biomarker to predict the occurrence of ROA.

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T.

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. 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 mm3 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. 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). 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.

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear

IntroductionTraumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans. MethodsA multi-task learning approach was used to segment both bone and BML from T2 fat‐suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions. ResultsThe 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures. ConclusionAutomated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI.

To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1mm) T2-weighted spin-echo (T2SE) prostate MRI. Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests. Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred. The deep learning reconstruction method provides significant SNR improvement in thin-slice (1mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.

Mapping cerebral perfusion in mice under various anesthesia levels using highly sensitive BOLD MRI with transient hypoxia.

Cerebral perfusion is critical for the early detection of neurological diseases and for effectively monitoring disease progression and treatment responses. Mouse models are widely used in brain research, often under anesthesia, which can affect vascular physiology. However, the impact of anesthesia on regional cerebral blood volume and flow in mice has not been thoroughly investigated. In this study, we have developed a whole-brain perfusion MRI approach by using a 5-second nitrogen gas stimulus under inhalational anesthetics to induce transient BOLD dynamic susceptibility contrast (DSC). This method proved to be highly sensitive, repeatable within each imaging session, and across four weekly sessions. Relative cerebral blood volumes measured by BOLD DSC agree well with those by contrast agents. Quantitative cerebral blood volume and flow metrics were successfully measured in mice under dexmedetomidine and various isoflurane doses using both total vasculature-sensitive gradient-echo and microvasculature-sensitive spin-echo BOLD MRI. Dexmedetomidine reduces cerebral perfusion, while isoflurane increases cerebral perfusion in a dose-dependent manner.

Prospective Comparison of Standard and Deep Learning-reconstructed Turbo Spin-Echo MRI of the Shoulder.

Background Deep learning (DL)-based MRI reconstructions can reduce imaging times for turbo spin-echo (TSE) examinations. However, studies that prospectively use DL-based reconstructions of rapidly acquired, undersampled MRI in the shoulder are lacking. Purpose To compare the acquisition time, image quality, and diagnostic confidence of DL-reconstructed TSE (TSEDL) with standard TSE in patients indicated for shoulder MRI. Materials and Methods This prospective single-center study included consecutive adult patients with various shoulder abnormalities who were clinically referred for shoulder MRI between February and March 2023. Each participant underwent standard TSE MRI (proton density- and T1-weighted imaging; conventional TSE sequence was used as reference for comparison), followed by a prospectively undersampled accelerated TSEDL examination. Six musculoskeletal radiologists evaluated images using a four-point Likert scale (1, poor; 4, excellent) for overall image quality, perceived signal-to-noise ratio, sharpness, artifacts, and diagnostic confidence. The frequency of major pathologic features and acquisition times were also compared between the acquisition protocols. The intergroup comparisons were performed using the Wilcoxon signed rank test. Results Overall, 135 shoulders in 133 participants were evaluated (mean age, 47.9 years ± 17.1 [SD]; 73 female participants). The median acquisition time of the TSEDL protocol was lower than that of the standard TSE protocol (288 seconds [IQR, 288-288 seconds] vs 926 seconds [IQR, 926-950 seconds], respectively; P < .001), achieving a 69% lower acquisition time. TSEDL images were given higher scores for overall image quality, perceived signal-to-noise ratio, and artifacts (all P < .001). Similar frequency of pathologic features (P = .48 to > .99), sharpness (P = .06), or diagnostic confidence (P = .05) were noted between images from the two protocols. Conclusion In a clinical setting, TSEDL led to reduced examination time and higher image quality with similar diagnostic confidence compared with standard TSE MRI in the shoulder. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang and Chow in this issue.

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.

To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL). DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists. Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913). The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists. The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed. •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.

Physics-informed deep learning for T2-deblurred superresolution turbo spin echo MRI.

Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k-space regions. To fill this gap, we developed a T2 -deblurred deep learning SR method for the SR of 3D-TSE images. A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images. The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation. The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.

T2 mapping magnetic resonance imaging of cartilage in hemophilia

BackgroundIn hemophilia, recurrent hemarthrosis may lead to irreversible arthropathy. T2 mapping MRI may reflect cartilage changes at an earlier reversible stage of arthropathy as opposed to structural MRI. ObjectivesTo evaluate interval changes of T2 mapping compared with the International Prophylaxis Study Group (IPSG) structural MRI scores of ankle cartilage in boys with hemophilia receiving prophylaxis. MethodsEight boys with hemophilia A (median age, 13; range, 9-17 years), 7 age- and sex-matched healthy boys (controls, median age, 15; range, 7-16 years). A multiecho spin-echo T2-weighted MRI sequence at 3.0T was used to obtain T2 maps of cartilage of boys with hemophilia and controls. Structural joint status was evaluated using the IPSG MRI score. ResultsT2 relaxation times of ankle cartilage increased significantly over time in both persons with hemophilia and controls (P = .002 and P = .00009, respectively). Changes in T2 relaxation time strongly correlated with changes in IPSG cartilage scores (rs = 0.93 to rs = 0.78 [P = .0007 to P = .023]), but not with changes in age (P = .304 to P = .840). Responsiveness of T2 relaxation times were higher than that of IPSG cartilage scores, with standardized response means >1.4 for T2 mapping in all regions-of-interest compared with 0.84 for IPSG cartilage scores. Baseline T2 relaxation time strongly correlated with timepoint 2 IPSG cartilage score (rs = 0.93 to rs = 0.82 [P = .001 to P = .012]) and T2 relaxation time (rs = 0.98 to rs = 0.88 [P = .00003 to P = .004]) changes in most regions-of-interest. ConclusionT2 mapping shows sensitivity to biochemical changes in cartilage prior to detectable damage using conventional MRI, offering potential for early detection of bleed-related cartilage damage in boys with hemophilia.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study

ObjectivesTo evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans.MethodsThis prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong’s test. The threshold for statistical significance was set at p < 0.05.ResultsA total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75–0.98) and between protocols (κ = 0.73–0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05).ConclusionsCompared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half.Clinical relevance statementArtificial intelligence–assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients.Key Points• The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found.• Reduced scan time, sharper delineation, and less noise with ACS reconstruction.• Improved efficiency of the clinical knee MRI examination by the ACS acceleration.

A multiparametric alternative to short inversion-time inversion recovery for imaging inflammation: T2water and fat fraction measurement using chemical shift-encoded turbo spin-echo MRI.

Short-inversion-time inversion-recovery MRI is used widely for imaging bone and soft-tissue inflammation in rheumatic inflammatory diseases, but there is no widely available quantitative equivalent of this sequence. This limits our ability to objectively assess inflammation and distinguish it from other processes. To address this, we investigate the use of the widely available Dixon turbo spin echo (TSE Dixon) sequence as a practical approach to simultaneous water-specific T2 (T2water ) and fat fraction (FF) measurement. We use a series of TSE Dixon acquisitions with varying effective TEs (TEeff ) to quantify T2water and FF. The validity of this approach is assessed in a series of phantom and in vivo experiments, with reference values provided by Carr-Purcell-Meiboom-Gill acquisitions, MRS, and phantoms. The effect of inflammation on parameter values is evaluated in patients with spondyloarthritis. The T2water estimates obtained from TSE Dixon were accurate compared with the reference values from Carr-Purcell-Meiboom-Gill and spectroscopy in both fat-free environments and in the presence of fat. FF measurements with T2water correction from TSE Dixon were accurate from 0% to 60% FF and were not confounded by T2water variations. In vivo imaging produced good quality images that were artifact free, produced plausible T2 values, separating and quantifying the effect of inflammation on T2water and FF. The T2water and FF measurements based on TSE Dixon with effective TE increments are accurate over a range of T2 and FF values and could provide a widely available quantitative alternative to the short-inversion-time inversion-recovery sequence for imaging inflamed tissue.

Carotid Artery Thrombus in Antiphospholipid Antibody Syndrome.

HomeRadiologyRecently Published PreviousNext Reviews and CommentaryFree AccessImages in RadiologyCarotid Artery Thrombus in Antiphospholipid Antibody SyndromeZongli Han, Hui Qi Zongli Han, Hui Qi Author AffiliationsFrom the Department of Neurosurgery, Peking University Shenzhen Hospital, 1120 Lianhua Rd, Shenzhen 518036, PR China.Address correspondence to H.Q. (email: [email protected]).Zongli HanHui Qi Published Online:Mar 14 2023https://doi.org/10.1148/radiol.221836MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In A 23-year-old previously healthy woman presented with sudden-onset right-sided hemiparesis and paresthesia. Brain MRI scan demonstrated acute left frontal lobe embolic infarcts. High-spatial-resolution vessel wall MRI scan revealed an isointense peripherally enhancing lesion in the left common carotid artery with resultant severe luminal narrowing, compatible with an intraluminal thrombus (Figure, A–C). Contrast-enhanced US image confirmed free-floating thrombus with proximal attachment to the vessel wall (Figure, D). Endarterectomy was performed, and an organized thrombus was confirmed with pathologic process. Antibody serology tests were positive for anticardiolipin immunoglobulin G, β-2 glycoprotein immunoglobulin G, and lupus anticoagulants. The diagnosis of antiphospholipid antibody syndrome was established according to the Miyakis criteria (1,2). The patient was treated with warfarin and showed no signs of clinical or radiologic relapse at 1-year follow-up.Images in a 23-year-old woman with sudden-onset right-sided hemiparesis and paresthesia. (A–C) T1-weighted three-dimensional isotropic variable flip-angle fast spin echo MRI scans with black-blood technique with a 3-T clinical scanner (uMR 780; United Imaging Healthcare) show vessel walls. (A) Axial and (B) coronal T1-weighted images without contrast material show an intraluminal T1 isointense lesion within the left common carotid artery with resultant severe luminal narrowing (arrows). (C) Coronal contrast-enhanced T1-weighted image shows peripheral linear contrast enhancement of the thrombus (arrow). (D) Contrast-enhanced US image demonstrates a free-floating thrombus with microbubbles between the thrombus and the carotid vessel (*) with proximal attachment to the vessel wall (arrowhead).Download as PowerPointOpen in Image Viewer Antiphospholipid antibody syndrome is an autoimmune disease characterized by recurrent arterial or venous thrombosis, pathologic pregnancy, and positive antiphospholipid antibodies (3). Neurologic manifestations, especially cerebral ischemia, are frequently present; however, presentation with carotid thrombus is uncommon (4).Disclosures of conflicts of interest: Z.H. No relevant relationships. H.Q. No relevant relationships.AcknowledgmentsThe authors thank Hui Zhang from the Department of Radiology, who helped with the interpretation of the high-resolution MRI. They also thank Liya Wei from the Department of Ultrasound, who helped with the interpretation of the contrast-enhanced US image.

Vascular space occupancy asymmetric spin echo (VASO-ASE) for non-invasive quantification of cerebral oxygen extraction fraction.

Asymmetric spin echo (ASE) MRI is a method for measuring regional oxygen extraction fraction (OEF); however, extravascular tissue models have been shown to under-estimate OEF. The hypothesis investigated here is that the addition of a vascular-space-occupancy (VASO) pre-pulse will more fully suppress blood water signal and provide global OEF values more consistent with physiological expectation and 15 O positron emission tomography (PET)-validated T2 -relaxation-under-spin-tagging (TRUST) OEF measures. Healthy adults (n= 14; age=27.7 ± 5.2y; sex=7/7 male/female) were scanned at 3.0T. Multi-echo ASE without inter-readout refocusing (ASERF- ), multi-echo ASE with inter-readout refocusing (ASERF+ ), and single-echo VASO-ASE were acquired twice each with common spatial resolution=3.44 × 3.44 × 3.0 mm and τ= 0-20 ms (interval=0.5ms). TRUST was acquired twice sequentially for independent global OEF assessment (τCPMG = 10ms; effective TEs=0, 40, 80, and 160 ms; spatial resolution=3.4 × 3.4 × 5 mm). OEF intraclass-correlation-coefficients (ICC), summary statistics, and group-wise differences were assessed (Wilcoxon rank-sum; significance: two-sided p< 0.05). ASERF+ (OEF=36.8 ± 1.9%) and VASO-ASE (OEF=34.4 ± 2.3%) produced OEF values similar to TRUST (OEF=36.5 ± 4.6%, human calibration model; OEF=32.7 ± 4.9%, bovine calibration model); however, ASERF- yielded lower OEF (OEF=26.1 ± 1.0%; p< 0.01) relative to TRUST. VASO-ASE (ICC=0.61) yielded lower ICC compared to other ASE variants (ICC >0.89). VASO-ASE and TRUST provide similar OEF values; however, VASO-ASE spatial coverage and repeatability improvements are required.

Fast and accurate T2 mapping using Bloch simulations and low-rank plus sparse matrix decomposition

PurposeMRI's T2 relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T2-maps from highly undersampled multi-echo spin-echo (MESE) MRI data. MethodsTwo new algorithms are presented: the first uses standard L + S approach, where both L and S are iteratively updated. The second technique, dubbed SPArse and fixed RanK (SPARK), uses a fixed-rank L, under the assumption that most MESE information is found in the L component and that this rank can be pre-calculated. The utility of these new techniques is demonstrated on in vivo brain and calf data at x2 to x6 acceleration factors. ResultsAccelerated T2 maps showed improved accuracy compared to fully sampled ground truth maps, when using L + S and SPARK techniques vis-à-vis standard GRAPPA acceleration. ConclusionSPARK provides accurate T2 maps with increased robustness to the selection of reconstruction parameters making it suitable to a wide range of applications and facilitating the use of quantitative T2 information in clinical settings.

AGREEMENT OF AUTOMATED LAMINAR FEMOROTIBIAL CARTILAGE TRANSVERSE RELAXATION TIME (T2) ANALYSIS WITH MANUAL EXPERT SEGMENTATION

Articular cartilage transverse relaxation time (T2) on MRI has been observed to reflect collagen integrity, orientation, and hydration, and to be associated with cartilage histological grading, mechanical properties, and early knee OA status. Previously, we reported high agreement of cartilage segmentations obtained from multi echo spin echo (MESE) MRI, using fully automated deep learning methods, in comparison with manual ones. As these results were based on 3T MRI (of the OAI), we here explored the agreement between fully automated segmentation of MESE cartilage at 1.5T. To analyze the agreement between automated, U-Net-based segmentation of femorotibial cartilage from MESE by convolutional neural networks vs. manual expert segmentation with quality control of the same images, and to compare laminar femorotibial cartilage T2 between both approaches. We studied 20 ACL-deficient patients with persistent dynamic knee instability (non-copers), 22 without instability (copers), 13 with surgical ACL reconstruction, and 16 healthy controls. Further patient characterizations are described in a parallel abstract at this conference. Sagittal MESE MRIs were acquired at 1.5T (Siemens Avanto; slice thickness/spacing 3/3.5mm, in-plane resolution 0.31mm, TR 1500ms, TE 9.7/19.4/29.1/38.8/48.5/58.2/67.9ms), at baseline (n=71) and 1 year later (n=55). Manual cartilage segmentation was done by experienced readers, with quality control by an expert (Fig. 1). The U-Net was trained using medial and lateral MRIs from the same scanner (training/ validation set n=50/9) obtained in volleyball athletes of different age groups, and in patients with posterior cruciate ligament (PCL) surgery, segmented by the above readers. Training of the U-net was performed for both all 7 echos and only the 1st echo. Automated U-Net segmentation was then applied to the current study MRIs, without manual intervention or correction. Yet, automated post-processing was employed to correct obvious segmentation errors. The agreement between manual and automated U-Net-based segmentations was evaluated using the Dice Similarly Coefficient (DSC), and by evaluating systematic differences and correlations in cartilage T2 of the 50% superficial and 50% deep layer. When all echoes were used for training, the overall DSC was 0.89 for MT/LT, and 0.83 for cMF/cLF. When only the 1st echo was used,DSCs were 0.87/0.88 and 0.81/0.79. Automated analysis overestimated the segmented tissue volume significantly in most regions, with correlations ranging from 0.93-0.96 for all echoes, and from 0.87-0.94 for the 1st echo. Deep layer T2 across the joint was 45.7ms for manual analysis, 45.7ms with all echoes, and 46.1ms with the 1st echo model; superficial layer T2 was 52.1, 53.2 and 54.4ms. There were statistically significant (albeit small) differences of automated vs. manual analysis across most regions for the superficial layer, and for LT and cLF in the deep layer. The correlation of deep layer T2 across the plates ranged from 0.91-0.99 for all echoes model, and from 0.85-0.98 for the 1st echo model, and that of the superficial T2 from 0.86-0.97, and from 0.73-0.81. Fully automated (U-Net-based) laminar analysis of femorotibial cartilage T2 appears feasible at 1.5T, albeit the agreement for T2 at 3T, and that for cartilage thickness was previously reported to be higher. The agreement with manual analysis was greater when training a model with 7 echoes than with the 1st echo only, and it was greater for the deep than for the superficial layer. No significant change in T2 was observed over 1 year; thus, longer intervals may be required for longitudinal validation.

AGREEMENT AND PRECISION OF FEMOROTIBIAL CARTILAGE TRANSVERSE RELAXATION TIME (T2) OBTAINED AT FOUR DIFFERENT MAGNETIC FIELD STRENGTHS

Spin-spin (transverse) relaxation time (T2) is considered to reflect cartilage composition, such as hydration, collagen content and orientation. It can be computed from multi-echo spin-echo (MESE) MRI and is associated with age, sex, and structural joint pathology. Cartilage T2 is dependent on the magnetic field strength. Lower field strength is characterized by lower signal to noise ratios (SNR) compared to standard 1.5Tesla (T) and 3T systems, resulting in longer acquisition times, while access to ultra-high field 7T MRI is still limited. A direct comparative evaluation of cartilage T2 across different field strengths has not been performed to date. To analyze the agreement and the intra-reader test-retest precision of laminar femorotibial cartilage T2, acquired from four different scanner models with 0.55T,1.5T, 3T, and 7T. Six healthy volunteers (5 female, age 21.5±3.5 y) without knee pain had two MESE MRIs acquired each, with repositioning of the knee between test and retest scans, using a 1) MAGNETOM Free.Max 0.55T (acquisition matrix (AM) 304 × 248, slice thickness [ST] 3mm, TR 3020ms, 6 echoes from 16.1 to 96.6ms, acquisition time (AT) 14 min 56 sec) 2) MAGNETOM Sola 1.5T (AM 384 × 384, ST 3mm, TR 2700ms, 7 echoes from 11.7 to 81.9ms, AT 10 min 31 sec) 3) MAGNETOM Vida 3T (AM 384 × 384, ST 3mm, TR 3210ms, 9 echoes from 11.5 to 103.5ms, AT 8 min 17 sec) 4) MAGNETOM Terra 7T (AM 384 × 384, ST 3mm, TR 4490ms, 9 echoes from 8.1 to 72.9ms, AT 8 min 22 sec) (Fig. 1). The field of view was 12 × 12cm for all MESE MRIs. Segmentation of the medial and lateral femorotibial compartment cartilage (MFTC/LFTC) was performed manually by an expert reader; the T2 values were computed for each voxel using custom software. The cartilage layers were divided into the top 50% (superficial [sf]) and bottom 50% (deep) layer, based on the local distance between the cartilage surface and bone interface. The test-retest precision across the entire femorotibial joint (FTJ), the MFTC, and the LFTC was measured using the root mean square coefficient of variation (RMS CV in %). The absolute values of laminar T2 were compared between field strengths using ANOVA, with Bonferroni-Dunn post-hoc correction. The RMS CV across the FTJ was lowest at 3T (0.9%/1.4% sf/deep) and greatest at 0.55T (3.2%/4.0%). The RMS CV was 1.3%/3.4% at 1.5T and 2.3%/1.4% at 7T. Sf RMS CV at 0.55/1.5/3/7T was 3.9/1.7/1.0/3.5% for the MFTC, and 3.0/1.7/1.2/2.7% for the LFTC. The deep layer RMS CV was 5.4/3.4/3.1/2.6% for the MFTC and 5.0/4.1/1.1/2.9% for the LFTC. Sf and deep layer T2 differed significantly between field strengths for the FTJ, the MFTC, and the LFTC (all p<0.001). Both sf and deep layer T2 across the FTJ, the MFTC and also the LFTC were shortest at 7T and longest at 0.55T (Fig. 2). Interestingly, T2 was shorter at 1.5T than at 3T, but this difference reached statistical significance only for the deep layer in the FTJ and in the MFTC (Fig. 2). Based on the protocols implemented in this study, laminar cartilage T2 systematically varied with magnetic field strength. The shortest values were observed at 7T, and the longest at 0.55T. These findings suggest that cartilage T2 measurements in multicenter studies are not directly comparable when different field strengths (potentially also different manufacturer and scanner models) are included. The test-retest precision error was lowest at 3T and highest at 0.55T, suggesting that, in clinical studies, measurements should be performed at 3T. However, test-retest precision is highly dependent on the protocol and might be improved for lower field strengths, applying different acquisition parameters (e.g. longer acquisition times and higher resolution). Implementation on the 7T magnet may require further improvement to leverage the full potential benefit of the higher field strength.

Anti-Correlated Myelin-Sensitive MRI Levels in Humans Consistent with a Subcortical to Sensorimotor Regulatory Process-Multi-Cohort Multi-Modal Evidence.

Differential axonal myelination synchronises signalling over different axon lengths. The consequences of myelination processes described at the cellular level for the regulation of myelination at the macroscopic level are unknown. We analysed multiple cohorts of myelin-sensitive brain MRI. Our aim was to (i) confirm a previous report of anti-correlation between myelination in subcortical and sensorimotor areas in healthy subjects, (ii) and thereby test our hypothesis for a regulatory interaction between them. We analysed nine image-sets across three different human cohorts using six MRI modalities. Each image-set contained healthy controls (HC) and ME/CFS subjects. Subcortical and Sensorimotor regions of interest (ROI) were optimised for the detection of anti-correlations and the same ROIs were used to test the HC in all image-sets. For each cohort, median MRI values were computed in both regions for each subject and their correlation across the cohort was computed. We confirmed negative correlations in healthy controls between subcortical and sensorimotor regions in six image-sets: three T1wSE (p = 5 × 10-8, 5 × 10-7, 0.002), T2wSE (p =2 × 10-6), MTC (p = 0.01), and WM volume (p = 0.02). T1/T2 was the exception with a positive correlation (p = 0.01). This myelin regulation study is novel in several aspects: human subjects, cross-sectional design, ROI optimization, spin-echo MRI and reproducible across multiple independent image-sets. In multiple independent image-sets we confirmed an anti-correlation between subcortical and sensorimotor myelination which supports a previously unreported regulatory interaction. The subcortical region contained the brain's primary regulatory nuclei. We suggest a mechanism has evolved whereby relatively low subcortical myelination in an individual is compensated by upregulated sensorimotor myelination to maintain adequate sensorimotor performance.

MRI-Based Metastatic Nodal Number and Associated Nomogram Improve Stratification of Nasopharyngeal Carcinoma Patients: Potential Indications for Individual Induction Chemotherapy.

Metastatic lymph nodal number (LNN) is associated with the survival of nasopharyngeal carcinoma (NPC); however, counting multiple nodes is cumbersome. To explore LNN threshold and evaluate its use in risk stratification and induction chemotherapy (IC) indication. Retrospective. A total of 792 radiotherapy-treated NPC patients (N classification: N0 182, N1 438, N2 113, N3 59; training group: 396, validation group: 396; receiving IC: 390). T1-, T2- and postcontrast T1-weighted fast spin echo MRI at 1.5 or 3.0 T. Nomogram with (model B) or without (model A) LNN was constructed to evaluate the 5-year overall (OS), distant metastasis-free (DMFS), and progression-free survival (PFS) for the group as a whole and N1 stage subgroup. High- and low-risk groups were divided (above vs below LNN- or model B-threshold); their response to IC was evaluated among advanced patients in stage III/IV. Maximally selected rank, univariate and multivariable Cox analysis identified the optimal LNN threshold and other variables. Harrell's concordance index (C-index) and 2-fold cross-validation evaluated discriminative ability of models. Matched-pair analysis compared survival outcomes of adding IC or not. A P value < 0.05 was considered statistically significant. Median follow-up duration was 62.1 months. LNN ≥ 4 was independently associated with decreased 5-year DMFS, OS, and PFS in entire patients or N1 subgroup. Compared to model A, model B (adding LNN, LNN ≥ 4 vs <4) presented superior C-indexes in the training (0.755 vs 0.727) and validation groups (0.676 vs 0.642) for discriminating DMFS. High-risk patients benefited from IC with improved post-IC response and OS, but low-risk patients did not (P=0.785 and 0.690, respectively). LNN ≥ 4 is an independent risk stratification factor of worse survival in entire or N1 staging NPC patients. LNN ≥ 4 or the associated nomogram has potential to identify high-risk patients requiring IC. 4 TECHNICAL EFFICACY: 4.