Spin-echo Research Articles

Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels.

Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences. To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance. Retrospective. Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa). 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image. CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed. Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons. For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61). AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists. 3 TECHNICAL EFFICACY: Stage 2.

3D printing of the brachial plexus and its osseous landmarks using magnetic resonance neurography for thoracic outlet syndrome evaluation.

Patient-specific three-dimensional (3D) printed anatomic models are valuable clinical tools that facilitate enhanced visualization of pertinent anatomic structures and have demonstrated benefits of reduced surgical times, increased surgeon confidence, and improved operative results and subsequent patient outcomes. Medical image-based 3D printed anatomic models are generally created from computed tomography (CT), however magnetic resonance imaging (MRI), which offers exquisite soft tissue characterization and flexible contrast avoiding the use of ionizing radiation, is an attractive alternative. Herein, the application of 3D printing incorporating both MR neurography and zero-echo time (ZTE) MRI for visualization of the brachial plexus anatomy in a subject with thoracic outlet syndrome (TOS) is described. A 28-year-old man presented with chronic right upper limb discomfort and paresthesias extending from the shoulder region to the third and fourth digits. The subject underwent evaluation with a unilateral brachial plexus MR neurography protocol at 3.0 Tesla for suspicion of TOS. The protocol included T2-weighted, 3D fast spin echo short-tau inversion recovery (STIR-FSE) and 3D radial ZTE sequences for depiction of the nerves and bones, respectively. The first rib and its synostosis impinged upon the inferior aspect of the T1 nerve root (T1NR), with accompanying mild enlargement of the T1NR. A 3D printed anatomic model was created and included: (1) bone (spine, ribs, clavicle, scapula, and humerus), (2) brachial plexus, and (3) costal cartilage. The 3D printed model clearly demonstrated a T1NR impingement from the synostosis, confirming the diagnosis of neurologic thoracic outlet syndrome (TOS) and guided the treatment approach in prescribing TOS-specific physical therapy, which led to significant improvements in the patient's condition. To our knowledge, this is the first in-vivo human 3D printed case for TOS using MRI-only data. The 3D printed model allowed for improved visualization and understanding of the spatial relationships between the nerves of the brachial plexus and surrounding osseous structures responsible for the patient's symptoms. Not applicable.

TMOD-11. ADVANCED MRI ANALYSIS TO ASSESS CHEMO-RADIATION RESPONSE IN PEDIATRIC EPENDYMOMA MODELS

Abstract Pediatric Ependymoma (EPN) is an aggressive brain tumor for which the benefits of chemo-radiation (CR) therapy have yet to be established. Our team has previously reported behavior patterns of EPN, including high tumor cellularity, cytological anaplasia, high mitotic index, tumor necrosis, and the presence of inflammatory cells such as M2-type macrophages. Recently, we have established advanced MRI protocols and computational algorithms for cell- and vessel-size imaging. The goal of this study is to develop advanced MRI analysis for CR response biomarkers (including tumor size, cell- and vessel-size correlates, and texture analysis) in mice with patient-derived xenografts (PDX) of pediatric EPN. Female severely immune deficient (SCID) mice were used for intracranial orthotopical inoculation of disaggregated tumors from pediatric EPN patients. All multi-parametric MRI sequences (fast spin echo, FLAIR, diffusion weighted, quantitative T2- and r2*-maps) were acquired on a Bruker 9.4 Tesla MRI scanner prior to CR, followed by 2 days and 2 weeks after CR treatment. All radiation treatment was performed using an animal image-guided precision XRAD irradiator (2Gyx5days), using MRI and CT guided EPN localization with added 30 mg/kg 5-fluoracil. All MRI analysis was performed using Bruker ParaVision software and MATLAB based modeling. The sensitivity of T2w-MRI scans was 0.2 mm for the smallest tumor detected; the median tumor volume at baseline was 2.5 mm3. Untreated mice showed rapid progression of tumor growth from small to intermediate to large EPN. The increased tumor sizes in untreated EPN were characterized by low ADC, highly elevated blood vessel density and large tumor cell size. A significant decrease in vessel size density and an increase in inflammatory cells were seen as soon as 2 days after CR therapy. The late response (2 weeks post CR) is characterized by increased ADC values and decreased cell size, resulting in significantly decreased tumor volumes.

Diagnostic accuracy and image quality evaluation of ultrashort echo time MRI in the lungs.

This study evaluates the diagnostic accuracy of ultrashort echo time (UTE)-MRI for detecting pulmonary nodules and image quality. A total of 46 patients at our hospital underwent unenhanced computed tomography (CT) and UTE-MRI. The image quality and number of nodules detected using CT were used as the gold standards. Three diagnostic radiologists independently recorded the image quality (visibility and sharpness of normal anatomical structures) of the CT and UTE images and the number of pulmonary nodules detected. The diagnostic accuracy, subjective image quality, and consistency between observations were statistically analyzed. Among 46 patients, 36 (78.2%) had pulmonary nodules on CT images, whereas 10 patients (21.7%) had no pulmonary nodules. A total of 48 lung nodules were detected, 3 of which were ground-glass opacities. UTE-MRI revealed 46 lung nodules. Compared with CT, the sensitivity of all MRI readers for detecting lung lesions was 95.8%, and the 3-observer agreement was nearly perfect (P < .001, Kendall Wa [Kender Harmonious Coefficient] = 0.913). The overall image quality score of the observers was high, ranging from good to excellent, and the consistency of the subjective UTE-MRI image quality was good (Kendall Wa = 0.877, P < .001). For tracheal display, the subsegment of the bronchus was displayed, and the wall of the tube was clearly displayed. The difference in the Wa values between the observers was 0.804 (P < .001), indicating strong consistency. For blood vessels, subsegment blood vessels could also be displayed with clear walls and uniform signals (Kendal Wa = 0.823, P < .001), indicating strong consistency. Compared to CT, UTE-MRI can detect pulmonary nodules with a high detection rate, relatively good image quality, and strong consistency between observers. The development of UTE-MRI can provide a novel imaging method for the detection and follow-up of pulmonary nodules and diagnosis of pneumonia by reducing ionizing radiation.

The contribution of methyl groups to electron spin decoherence of nitroxides in glassy matrices.

Long electron spin coherence lifetimes are crucial for high sensitivity and resolution in many pulse electron paramagnetic resonance (EPR) experiments aimed at measuring hyperfine and dipolar couplings, as well as in potential quantum sensing applications of molecular spin qubits. In immobilized systems, methyl groups contribute significantly to electron spin decoherence as a result of methyl torsional quantum tunneling. We examine the electron spin decoherence dynamics of the nitroxide radical 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) in both a methyl-free solvent and a methyl-containing solvent at cryogenic temperature. We model nitroxide and solvent methyl effects on decoherence using cluster correlation expansion (CCE) simulations extended to include methyl tunneling and compare the calculations to experimental data. We show that by using the methyl tunneling frequency as a fit parameter, experimental Hahn echo decays can be reproduced fairly well, allowing structural properties to be investigated in silico. In addition, we examine the Hahn echo of a hypothetical system with an unpaired electron and a single methyl to determine the effect of geometric configuration on methyl-driven electron spin decoherence. The simulations show that a methyl group contributes the most to electron spin decoherence if it is located between 2.5 and 6-7Å from the electron spin, with its orientation being of secondary importance.

MRI Tomoelastography to Assess the Combined Status of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.

Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking. To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC. Retrospective. One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months). 3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T. Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features. Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant. Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort. Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC. 3 TECHNICAL EFFICACY: Stage 2.

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs). Variable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting. A single-site liver CSE-MRI dataset (188 subjects, 4146 axial slices) was considered, which was split into training (150 subjects), validation (18), and testing (20) subsets. Testing subjects were scanned using several protocols with different TEs, which we then used to measure the PDFF reproducibility coefficient (RDC) at two regions of interest (ROIs): the right posterior and left hepatic lobes. An open-source multi-site and multi-vendor fat-water phantom dataset was also used for PDFF bias assessment. VET-Net showed RDCs of 1.71% and 1.04% on the right posterior and left hepatic lobes, respectively, across different TEs, which was comparable to a reference graph cuts-based method (RDCs = 1.71% and 0.86%). VET-Net also showed a smaller PDFF bias (-0.55%) than graph cuts (0.93%) when tested on a multi-site phantom dataset. Reproducibility (1.94% and 1.59%) and bias (-2.04%) were negatively affected when the auxiliary TE input was not considered. VET-Net provided unbiased and precise PDFF estimations using CSE-MR images from different hardware vendors and different TEs, outperforming conventional DL approaches. Question Reproducibility of liver PDFF DL-based approaches on different scan protocols or manufacturers is not validated. Findings VET-Net showed a PDFF bias of -0.55% on a multi-site phantom dataset, and RDCs of 1.71% and 1.04% at two liver ROIs. Clinical relevance VET-Net provides efficient, in terms of scan and processing times, and unbiased PDFF estimations across different MR scanners and scan protocols, and therefore it can be leveraged to expand the use of MRI-based liver fat quantification to assess hepatic steatosis.

Towards Achilles Tendon Injury Prevention in Athletes with Structural MRI Biomarkers: A Machine Learning Approach.

Recent advancements in artificial intelligence have proven their effectiveness in orthopaedic settings, especially in tasks like medical image analysis. This study compares human musculoskeletal radiologists to artificial intelligence in a novel, detailed, short, and cost-effective examination of Achilles tendon magnetic resonance images to uncover potential disparities in their reasoning approaches. Aiming to identify relationships between the structured assessment of the Achilles tendon and its function that could support injury prevention. We examined 72 athletes to investigate the link between Achilles tendon structure, as visualised in magnetic resonance images using a precise T2*-weighted gradient echo sequence with very short echo times, and its functional attributes. The acquired data were analysed using advanced artificial intelligence techniques and reviewed by radiologists. Additionally, we conducted statistical assessments to explore relationships with functional studies in four meaningful groups: dynamic strength, range of motion, muscle torque and stabilography. The results show notable linear or non-linear relationships between functional indicators and structural alterations (maximal obtained Spearman correlation coefficients ranged from 0.3 to 0.36 for radiological assessment and from 0.33 to 0.49 for artificial intelligence assessment, while maximal normalised mutual information ranged from 0.52 to 0.57 for radiological assessment and from 0.42 to 0.6 for artificial intelligence assessment). Moreover, when artificial intelligence-based magnetic resonance assessment was utilised as an input, the associations consistently proved more robust, or the count of significant relationships surpassed that derived from radiological assessment. Ultimately, utilising only structural parameters as inputs enabled us to explain up to 59% of the variance within specific functional groups. This analysis revealed that structural parameters influence four key functional aspects related to the Achilles tendon. Furthermore, we found that relying solely on subjective radiologist opinions limited our ability to reason effectively, in contrast to the structured artificial intelligence assessment. Cross-sectional studies.

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explored. To evaluate whether integrating preoperative dual-plane MRI-based DL features with clinical features can assess 1-year outcomes in TMD for LDH. Retrospective. The study involved 548 patients who underwent TMD between January 2016 and January 2021. Training set (N = 305, mean age 51.85 ± 13.84 years, 56.4% male). Internal validation set (N = 131, mean age 51.85 ± 13.84 years, 54.2% male). External validation set (N = 112, mean age 51.54 ± 14.43 years, 50.9% male). 3 T MRI with sagittal and transverse T2-weighted sequences (Fast Spin Echo). Ground truth labels were based on improvement rate in 1-year Japanese Orthopaedic Association (JOA) scores. Information on 42 preoperative clinical features was collected. The largest protrusions were identified from T2 MRI by three clinicians and were used to train deep learning models (ResNet50, ResNet101, and ResNet152) to extract DL features. After feature selection, three models were built, namely, clinical, DL, and combined models. Chi-square or Fisher's exact tests was used for group comparisons. Quantitative differences were analyzed using the t-test or Mann-Whitney U test. P-values <0.05 were considered significant. Models were validated on internal and external datasets using metrics such as the area under the curve (AUC). The AUCs of the clinical models achieved 0.806 (internal) and 0.779 (external). ResNet152 performed best in three DL models, with AUCs of 0.858 (internal) and 0.834 (external). The combined model achieved AUCs of 0.889 (internal) and 0.857 (external). A model combining preoperative dual-plane MRI DL features and clinical features can assess 1-year outcomes of TMD for LDH. 4 TECHNICAL EFFICACY: Stage 2.

Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times.

To address key issues of low SNR and image distortions in prostate diffusion MRI (dMRI) by means of using strong gradients, single-shot spiral readouts and an expanded encoding model for image reconstruction. Diffusion-weighted spin echo imaging with EPI and spiral readouts is performed on a whole-body system equipped with strong gradients (up to 250 mT/m). An expanded encoding model including static off-resonance, coil sensitivities, and magnetic field dynamics is employed for image reconstruction. The acquisitions are performed on a phantom and in vivo (one healthy volunteer and one patient with prostate cancer). The resulting images are compared to conventional dMRI EPI with navigator-based image reconstruction and assessed in terms of their congruence, SNR, tissue contrast, and quantitative parameters. Using the expanded encoding model, high-quality images of the prostate gland are obtained across all b-values (up to 3 ms/μm2), clearly outperforming the results obtained with conventional image reconstruction. Compared to EPI, spiral imaging provides an SNR gain up to 45% within the gland and even higher in the lesion. In addition, prostate dMRI with single-shot spirals at submillimeter in-plane resolution (0.85 mm) is accomplished. The combination of strong gradients and an expanded encoding model enables imaging of the prostate with unprecedented image quality. Replacing the commonly used EPI with spirals provides the inherent benefit of shorter echo times and superior readout efficiency and results in higher SNR, which is in particular relevant for considered applications.

Cerebrovascular reactivity and response times describe recent ischemic symptomatology in patients with moyamoya

Abstract Moyamoya is a non-atherosclerotic intracranial steno-occlusive condition that places patients at high risk for ischemic stroke. Randomized trials of surgical revascularization demonstrating efficacy in ischemic moyamoya have not been performed, and as such, biomarkers of parenchymal hemodynamic impairment are needed to assist with triage and evaluate post-surgical response. In this prospective study, we test the hypothesis that parenchymal cerebrovascular reactivity (CVR) metrics in response to a fixed-inspired 5% carbon dioxide challenge correlate with recent focal ischemic symptoms. Hypercapnic reactivity blood oxygenation level-dependent (BOLD) MRI (echo time=35ms; spatial resolution=3.5x3.5x3.5mm) and catheter angiography assessments of cortical reserve capacity and vascular patency, respectively, in moyamoya disease and syndromic participants (n=73) were performed in sequence. Cerebrovascular reactivity uncorrected for response time (CVRRAW) was quantified, and time regression analyses were applied to quantify maximum cerebrovascular reactivity (CVRMAX) and cerebrovascular reactivity response time (CVRDELAY). Symptomatology was categorized by a stroke neurologist by hemisphere: symptomatic (lateralizing ischemic symptoms &amp;lt; 6 months) or asymptomatic (no ischemic symptom history). Values are presented as median [interquartile range]; logistic regression assessed the association of cerebrovascular reactivity metrics with symptoms, controlling for age and sex. A total of 109 hemispheres, including 39 symptomatic and 70 asymptomatic hemispheres, met inclusion criteria. Symptomatic hemispheres displayed reduced CVRRAW (p&amp;lt;0.01) (symptomatic=0.45 [0.28-0.70] z-statistic/ΔEtCO2 vs. asymptomatic=0.67 [0.44-0.98] z-statistic/ΔEtCO2), lengthened CVRDELAY (p&amp;lt;0.001) (symptomatic=47.6 [37.7-57.0] seconds vs. asymptomatic=37.7 [30.4-46.4] seconds), and reduced CVRMAX (p=0.037) (symptomatic=1.31 [0.99-1.94] z-statistic/ΔEtCO2 vs. asymptomatic=1.64 [1.29-2.12] z-statistic/ΔEtCO2). CVRDELAY (p&amp;lt;0.001) was found to be significantly related to age in asymptomatic hemispheres (0.33-unit increase/year). Of assessed measures, the receiver operating characteristic curves suggest that CVRDELAY is associated most closely with recent ischemic symptoms (p&amp;lt;0.001). Findings support that cerebrovascular reactivity metrics are uniquely altered in hemispheres with recent ischemic symptoms, further motivating their utilization as biomarkers of ischemic symptomatology and potential treatment efficacy in moyamoya disease and syndrome.

Low-rank iterative infilling for zero echo-time (ZTE) imaging.

A new referenceless low-rank reconstruction technique has been introduced to address the issue of missing samples within the Zero Echo Time (ZTE) dead-time gap. The proposed method reformulates the in-filling of the missing samples as an inverse problem subject to low-rank constraints. Its performance and robustness are evaluated through a comparative analysis that combines Monte Carlo computational simulations and data obtained from in vivo experiments. The proposed method is tested for dead-time gaps ranging up to 4.5 Nyquist dwells, across signal-to-noise ratio levels of 5, 10, 15, and 20 dB. Consistently superior performance is observed across all cases compared to algebraic and parallel imaging methods. The speed for convergence decreases exponentially as the dead-time gap expands. The proposed method enables artifact-free reconstruction up to dead-time gap of 4 Nyquist dwells and thereby supports ZTE imaging up to an imaging bandwidth of kHz (assuming transmit and receive switching less than 30 s). It demonstrates superior performance compared to algebraic and parallel imaging methods.

Added value of volumetric MRI pulse sequence 3D VISTA (Volume ISotopic Turbo spin echo Acquisition) in perianal fistula depiction and characterization

PurposeDiagnosis of perianal fistula represents a challenge for surgeons. It is well known that magnetic resonance imaging (MRI) plays an important role in that. The new 3D MRI sequence VISTA (Volume ISotopic Turbo spin echo Acquisition) can improve detection and characterization of perianal fistula compared with two-dimensional (2D) sequences. The aim of the study was to compare the diagnostic performance of the new 3D MRI sequence VISTA with the widely routinely used T2 FSE pulse sequence in depiction and characterization of perianal fistula by using the contrast-enhanced (CE) 3D T1 sequence THRIVE (T1-weighted high-resolution isotropic volume examination) as a reference standard.Material and methodsForty adult patients were enrolled in this prospective study. They underwent MRI perianal region examination using routine T2 TSE and CE 3D T1 sequence THRIVE with addition of the new 3D MRI sequence VISTA. T2, 3D VISTA and (CE) 3D T1 sequence THRIVE images were evaluated by two radiologists separately for detection and characterization of perianal fistula, then comparison between of T2 and 3D VISTA sequences was done using (CE) 3D T1 sequence THRIVE as a reference. Each sequence sensitivity, specificity and accuracy were calculated by both readers.ResultsFor reader 1, the sensitivity, specificity and accuracy were 92.5%, 90.5% and 93.6% for 3D VISTA and 84.1%, 83.7% and 87.3% for T2 FSE. For reader 2, the sensitivity, specificity and accuracy were 91.5%, 92.8% and 94.8% for 3D VISTA and 82.9%, 84.5% and 86.7% for T2 FSE.ConclusionsUsing CE 3D T1 sequence THRIVE as the reference standard, 3D VISTA pulse sequence on the perianal region has better diagnostic performance in the detection and characterization of perianal fistula as compared to the routinely used T2 FSE sequence.

Use of Ultra-Short Echo Time MRI to Improve Temporal Bone Imaging.

The short T2 nature of cortical bone causes it to appear similar to air on MR, forcing clinicians to rely on computed tomography imaging, with its attendant ionizing radiation exposure, to define temporal bone structures. Through the use of novel MR sequences with ultra-short echo times (UTE), short T2 structures are now able to be visualized, allowing for improved understanding of anatomical relationships. Eight patients (50% female) undergoing MR imaging of the skull base for diagnostic purposes (62.5% for vestibular schwannoma surveillance) at a tertiary care center were enrolled to evaluate the safety and efficacy of UTE imaging. CT scans were completed in 37.5% of the patients as part of their workup and used for comparison purposes. The repetition time, short echo time, and long echo time for the UTE sequence were 11, 0.032, and 2.2 msec, respectively. The protocol added 6 min to the total scanning time, and all patients tolerated the sequence without issue. The ossicles, mastoid air cells, antrum, and epitympanum were able to be seen and had a high Dice similarity coefficient when compared to CT (>0.5). UTE allowed for clear delineation of all segments of the facial nerve with a signal-to-noise ratio of 35 (although the BRAVO sequences had a superior ratio of 140). Vestibular schwannomas were able to be distinguished from normal brain parenchyma. UTE is safe and effective for visualizing anatomic structures not normally seen on traditional MRI, potentially allowing for improved surgical planning in patients. 3 Laryngoscope, 134:4691-4696, 2024.

Resting-state functional MRI of the nose as a novel investigational window into the nervous system

Besides being responsible for olfaction and air intake, the nose contains abundant vasculature and autonomic nervous system innervations, and it is a cerebrospinal fluid clearance site. Therefore, the nose is an attractive target for functional MRI (fMRI). Yet, nose fMRI has not been possible so far due to signal losses originating from nasal air-tissue interfaces. Here, we demonstrated feasibility of nose fMRI by using novel ultrashort/zero echo time (TE) MRI. Results obtained in the resting-state from 13 healthy participants at 7T and in 5 awake mice at 9.4T revealed a highly reproducible resting-state nose functional network that likely reflects autonomic nervous system activity. Another network observed in humans involves the nose, major brain vessels and CSF spaces, presenting a temporal dynamic that correlates with heart rate and breathing rate. These resting-state nose functional signals should help elucidate peripheral and central nervous system integrations.

Post-Correctional Improvement of T2-Weighted Fast Spin Echo Magnetic Resonance Imaging Pulse Sequence for Detecting High Intensity Focused Ultrasound Thermal Lesions

Post-Correctional Improvement of T2-Weighted Fast Spin Echo Magnetic Resonance Imaging Pulse Sequence for Detecting High Intensity Focused Ultrasound Thermal Lesions

SPINEPS—automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation

Abstract Objectives Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images. Material and methods This local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones. Segmentation evaluation metrics included the Dice score and average symmetrical surface distance (ASSD). Statistical significance was assessed using the Wilcoxon signed-rank test. Results On the public dataset, SPINEPS outperformed a nnUNet baseline on every structure and metric (e.g., an average over vertebra instances: dice 0.933 vs 0.911, p &lt; 0.001, ASSD 0.21 vs 0.435, p &lt; 0.001). SPINEPS trained on automated annotations of the NAKO achieves an average global Dice score of 0.918 on the combined NAKO and in-house test split. Adding the training data from the public dataset outperforms this (average instance-wise Dice score over the vertebra substructures 0.803 vs 0.778, average global Dice score 0.931 vs 0.918). Conclusion SPINEPS offers segmentation of 14 spinal structures in T2w sagittal images. It provides a semantic mask and an instance mask separating the vertebrae and intervertebral discs. This is the first publicly available algorithm to enable this segmentation. Key Points QuestionNo publicly available automatic approach can yield semantic and instance segmentation masks for the whole spine (including posterior elements) in T2-weighted sagittal TSE images. FindingsSegmenting semantically first and then instance-wise outperforms a baseline trained directly on instance segmentation. The developed model produces high-resolution MRI segmentations for the whole spine. Clinical relevanceThis study introduces an automatic approach to whole spine segmentation, including posterior elements, in arbitrary fields of view T2w sagittal MR images, enabling easy biomarker extraction, automatic localization of pathologies and degenerative diseases, and quantifying analyses as downstream research.

A comparative analysis of the sensitivity and BOLD contamination of the VASO response at 3 Tesla: ME-DEPICTING vs. ME-EPI readouts

Abstract ‘Non-BOLD fMRI’ data acquired at non-zero echo time (TE) suffer from contamination by the Blood Oxygenation Level Dependent (BOLD) signal due to the unavoidable signal decay caused by transverse relaxation. This contamination further reduces their already low inherent functional sensitivities and makes their correction essential. The Slice-Saturation Slab-Inversion Vascular Space Occupancy (SS-SI–VASO), for instance, cancels out BOLD contributions from VASO data, reflecting cerebral blood volume (CBV) changes, via a dynamic division approach. Alternatively, multi-echo (ME) data provide the possibility of extrapolating to TE=0. Acquisitions at very short TE would minimize the need for such corrections. The center-out EPI variant (‘DEPICTING’) is one such readout which allows for short TE. The ME 2D DEPICTING was compared here against a traditional ME 2D EPI for its sensitivity to functional changes in the VASO signal. The two BOLD-correction schemes were also evaluated. Clear differences in functional sensitivity were observed for the uncorrected VASO data obtained from the first echo, TE1, of the two readouts. VASO data corrected by ME extrapolation were, however, found to be almost identical in their sensitivity for detecting CBV changes for both readouts. An excessively high increase in VASO signal sensitivity observed with the dynamic division correction for both readouts revealed a near-perfect linear dependence on TE of VASO signal changes. This could be attributed to the substantial intravascular BOLD contributions at 3 T. In the present data, extravascular ΔR2* fraction was found to be around ~50–60%. ME extrapolation is, hence, recommended to avoid overestimation of functional CBV changes at commonly used TEs.

3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T.

To develop a 3D distortion-free reduced-FOV diffusion-prepared gradient-echo sequence and demonstrate its application in vivo for diffusion imaging of the spinal cord in healthy volunteers. A 3D multi-shot reduced-FOV diffusion-prepared gradient-echo acquisition is achieved using a slice-selective tip-down pulse in the phase-encoding direction in the diffusion preparation, combined with magnitude stabilizers, centric k-space encoding, and 2D phase navigators to correct for intershot phase errors. The accuracy of the ADC values obtained using the proposed approach was evaluated in a diffusion phantom and compared to the tabulated reference ADC values and to the ADC values obtained using a standard spin echo diffusion-weighted single-shot EPI sequence (DW-SS-EPI). Five healthy volunteers were scanned at 3 T using the proposed sequence, DW-SS-EPI, and a clinical diffusion-weighted multi-shot readout-segmented EPI sequence (RESOLVE) for cervical spinal cord imaging. Image quality, perceived SNR, and image distortion were assessed by two expert radiologists. ADC maps were calculated, and ADC values obtained with the proposed sequence were compared to those obtained using DW-SS-EPI and RESOLVE. Consistent ADC estimates were measured in the diffusion phantom with the proposed sequence and the conventional DW-SS-EPI sequence, and the ADC values were in close agreement with the reference values provided by the manufacturer of the phantom. In vivo, the proposed sequence demonstrated improved image quality, improved perceived SNR, and reduced perceived distortion compared to DW-SS-EPI, whereas all measures were comparable against RESOLVE. There were no significant differences in ADC values estimated in vivo for each of the sequences. 3D distortion-free diffusion-prepared imaging can be achieved using the proposed sequence.

Stimulated echo acquisition mode (STEAM) diffusion tensor imaging with different diffusion encoding times in the supraspinatus muscle: Test-retest reliability and comparison to spin echo diffusion tensor imaging.

Diffusion tensor imaging (DTI) provides insight into the skeletal muscle microstructure and can be acquired using a stimulated echo acquisition mode (STEAM)-based approach to quantify time-dependent tissue diffusion. This study examined diffusion metrics and signal-to-noise ratio (SNR) in the supraspinatus muscle obtained with a STEAM-DTI sequence with different diffusion encoding times (Δ) and compared them to measures from a spin echo (SE) sequence. Ten healthy subjects (mean age 31.5 ± 4.7years; five females) underwent 3-Tesla STEAM and SE-DTI of the shoulder in three sessions. STEAM was acquired with Δ of 100/200/400/600 ms. The diffusion encoding time in SE scans was 19ms (b = 500s/mm2). Region of interest-based measurement of fractional anisotropy (FA), mean diffusivity (MD), and SNR was performed. Intraclass correlation coefficients (ICCs) were computed to assess test-retest reliability. ANOVA with post-hoc pairwise tests was used to compare measures between different Δ of STEAM as well as STEAM and SE, respectively. FA was significantly higher (FASTEAM: 0.38-0.46 vs. FASE: 0.26) and MD significantly lower (MDSTEAM: 1.20-1.33 vs. MDSE: 1.62×10-3mm2/s) in STEAM compared to SE (p < 0.001, respectively). SNR was significantly higher for SE (72.3 ± 8.7) than for STEAM (p < 0.001). ICCs were excellent for FA in STEAM (≥0.911) and SE (0.960). For MD, ICCs were good for STEAM100ms-600ms (≥0.759) and SE (0.752). STEAM and SE exhibited excellent reliability for FA and good reliability for MD in the supraspinatus muscle. SNR was significantly higher in SE compared to STEAM.