T2-weighted Spin-echo Images 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.

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.

Asymmetric spin echo multi-echo echo planar imaging (ASEME-EPI) sequence for pre-clinical high-field fMRI.

In functional magnetic resonance imaging (fMRI) of the blood oxygen level-dependent (BOLD) contrast, gradient-recalled echo (GRE) acquisitions offer high sensitivity but suffer from susceptibility-induced signal loss and lack specificity to microvasculature. In contrast, spin echo (SE) acquisitions provide improved specificity at the cost of reduced sensitivity. This study introduces Asymmetric Spin Echo Multi-Echo Echo Planar Imaging (ASEME-EPI), a technique designed to combine the benefits of both GRE and SE for high-field preclinical fMRI. ASEME-EPI employs a spin echo readout followed by two asymmetric spin echo (ASE) GRE readouts, providing an initial T2-weighted SE image and subsequent T2 * -weighted ASE images. A feasibility study for the technique was implemented on a 9.4 T pre-clinical MRI system and tested using a visual stimulation in northern tree shrews. Comparing ASEME-EPI with conventional GRE echo planar imaging (GRE-EPI) and SE echo planar imaging (SE-EPI) acquisitions, results showed that ASEME-EPI achieved BOLD contrast-to-noise ratio (CNR) comparable to GRE-EPI while offering improved specificity in activation maps. ASEME-EPI activation was more confined to the primary visual cortex (V1), unlike GRE-EPI which showed activation extending beyond anatomical boundaries. Additionally, ASEME-EPI demonstrated the ability to recover signal in areas of severe field inhomogeneity where GRE-EPI suffered from signal loss. The performance of ASEME-EPI is attributed to its multi-echo nature, allowing for SNR-optimized combination of echoes, effectively denoising the data. The inclusion of the initial SE also contributes to signal recovery in areas prone to susceptibility artifacts. This feasibility study demonstrates the potential of ASEME-EPI for high-field pre-clinical fMRI, offering a promising compromise between GRE sensitivity and SE specificity while addressing challenges of T2 * decay at high field strengths.

Safety Clearance and Artifact Testing of a Nitinol Breast Biopsy Clip in an Ultra-High Resolution (7 Tesla) Magnetic Resonance Imaging Environment

Abstract Background The lack of safety clearance of several metallic breast biopsy clips in 7 Tesla (T) poses a significant hurdle to using advanced magnetic resonance imaging (MRI) techniques in clinical management or cancer research. Aims This article assesses the Ultracor Twirl clip for safety and imaging artifacts in a 7T MRI scanner. Setting and Design This study can be categorized as a phantom study. Materials and Methods Tests for magnetic susceptibility (translational attraction and torque), MRI-related heating, and artifacts were conducted based on the American Society for Testing and Materials standards. The magnetic susceptibility tests evaluated the scanner's magnetic force that can cause clip movement and rotation. The heating test was conducted with customized MRI parameters of short TR and maximum echo-train length, designed to induce temperature change. The artifact test, using T1-weighted spin and gradient echo imaging sequences, evaluated potential image misrepresentations (localized signal loss) caused by the clip's metallic properties. Statistical Tests None. Results and Conclusion The magnetic susceptibility tests indicated no noticeable translational or rotational force exerted by the MRI scanner. The heating test indicated no significant temperature change (&lt;0.3°C) in the testing gel when the clip was absent/present, both within the safety threshold (&lt;1°C). The artifact test's clip images all contained an artifact (largest radius = 10.7 mm). These cumulative results indicate that this clip is safe in 7T scanners. Scanning at least 10.7 mm away from the clip avoids potential signal loss in the region of interest.

Accuracy of Diagnosing Optic Neuritis Using DANTE T1-SPACE Imaging.

To evaluate the use of delay alternating with nutation for tailored excitation-prepared T1-weighted turbo spin echo (DANTE T1-SPACE) imaging for diagnosing optic neuritis and to analyze its correlation with clinical findings before and after treatment. Patients diagnosed with optic neuritis or non-arteritic anterior ischemic optic neuropathy (NA-AION) were evaluated at the Ophthalmology Department of Kyoto University Hospital. All patients underwent magnetic resonance (MR) studies before treatment initiation and ophthalmic examinations before and after treatment. Three ophthalmologists independently reviewed the MR scans for abnormalities. The magnetic resonance imaging (MRI) assessments included post-contrast DANTE T1-SPACE, post-contrast volumetric interpolated breath-hold examination (VIBE), and short T1 inversion recovery (STIR) scans. The presence of abnormalities in each sequence was determined. Of 36 eyes from 30 patients, 21 eyes from 17 patients were diagnosed with optic neuritis, and 15 eyes from 13 patients were diagnosed with NA-AION. DANTE T1-SPACE sequences showed better sensitivity for detecting optic neuritis than STIR sequences (100% vs 67%, p = 0.009). VIBE images did not confirm enhancement of lesions in some cases with optic neuritis. No differences were observed among the sequences for NA-AION. Lesion length evaluated by DANTE T1-SPACE sequences was associated with circumpapillary retinal nerve fiber layer thickness at the initial visit, eye pain, and the time interval from symptom onset to MRI scan. Contrast-enhanced DANTE T1-SPACE was better than other sequences of MRI for diagnosing optic neuritis.

Application of deep learning reconstruction in abdominal magnetic resonance cholangiopancreatography for image quality improvement and acquisition time reduction

PurposeTo compare deep learning (DL)-based and conventional reconstruction through subjective and objective analysis and ascertain whether DL-based reconstruction improves the quality and acquisition speed of clinical abdominal magnetic resonance imaging (MRI). MethodsThe 124 patients who underwent abdominal MRI between January and July 2021 were retrospectively studied. For each patient, two-dimensional axial T2-weighted single-shot fast spin-echo MRI images with or without fat saturation were reconstructed using DL-based and conventional methods. The subjective image quality scores and objective metrics, including signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were analysed. An explorative analysis was performed to compare 20 patients’ MRI images with site routine settings, high-resolution settings and high-speed settings. Paired t tests and Wilcoxon signed-rank tests were used for subjective and objective comparisons. ResultsA total of 144 patients were evaluated (mean age, 62.2 ± 14.1 years; 83 men). The MRI images reconstructed using DL-based methods had higher SNRs and CNRs than did those reconstructed using conventional methods (all p < 0.01). The subjective scores of the images reconstructed using DL-based methods were higher than those of the images reconstructed using conventional methods (p < 0.01), with significantly lower variation (p < 0.01). Exploratory analysis revealed that the DL-based reconstructions with thin slice thickness and higher temporal resolution had the highest image quality and were associated with the shortest scan times. ConclusionsDL-based reconstruction methods can be used to improve the quality with higher stability and accelerate the acquisition of abdominal MRI.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. Retrospective. One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. 3 TECHNICAL EFFICACY: Stage 3.

Normal ovarian sizes on MRI in pediatric patients: a preliminary study.

Ovarian enlargement is one of several findings of pathology, including ovarian torsion. With increasing use of MRI for acute abdominal pain in children, data for normal ovary size and appearance are needed. To provide preliminary data on normal sizes of ovaries on MRI in pediatric patients. This retrospective IRB-approved study included girls (5 to 17years of age) with MRI examinations performed for indications not related to the ovaries from 2018 to 2022. For each MRI, coronal T2-weighted single shot fast spin echo and axial T2-weighted fat-saturated images were independently reviewed by three pediatric radiologists who recorded ovary visualization and ovarian linear measurements (3 planes). Ovarian volumes were calculated from linear measurements. Agreement among observers was calculated using kappa statistics and intraclass correlation coefficients. A total of 181 MRIs were reviewed. The left ovary was visualized in 166-176 (92-97%) cases (R1-R3) and the right ovary was visualized in 165-174 (91-96%) cases with excellent agreement among reviewers (left: K = 0.89 [0.84-0.94], right: K = 0.85 [0.79-0.91]). Interrater class coefficient (ICC) for largest single dimension of the ovary was left: 0.83 (CI 0.79-0.87) and right: 0.85 (CI 0.81-0.89). There were significant moderate to strong correlations between ovarian volume and age (left: 0.67 [0.58-0.75], right: 0.66 [0.57-0.74]). The ovaries can be adequately visualized and measured on MRI with excellent inter-reader agreement. This study serves as the foundation for developing normative values for ovarian volumes by age on MRI.

Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers

Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal–Wallis tests, Kaplan–Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.

Evaluating the Efficacy of Volume Isotropic Turbo Spin Echo Acquisition Versus T2-Weighted Turbo Spin Echo Imaging in the Diagnosis of Nerve Root and Perineuronal Pathologies in Spinal Disorders.

Background While two-dimensional (2D) turbo spin echo (TSE) sequences offer better through-plane resolution than three-dimensional (3D) isotropic TSE sequences images, with a narrower thickness of the slice, 3D isotropic TSE sequences are known to have a weaker in-plane resolution as well as blurring of the image. These elements may make it more difficult to distinguish between nearby structures that may affect nerve roots and small nerve roots during spinal imaging. This study aimedto analyze the accuracy of T2 TSE sequence and volumetric isotropic TSE acquisition in determining the indentation of nerve roots and perineural diseases such as nerve sheath tumors and Tarlov cysts. Methods Fifty patients who attended the Department of Radiodiagnosis for magnetic resonance (MR) spine participated in this prospective study. Routine MR lumbosacral (LS) spine sequences, such as survey, coronal T2short-tau inversion recovery (STIR), sagittal T2 TSE, sagittal T1 TSE, and axial T2 TSE, were carried out after a localizer was acquired. More sequences from volume isotropic turbo spin echo acquisition (VISTA) were acquired. For both 2D and 3D sequences, the visibility ratings for perineural cysts, spinal canal stenosis, and nerve root indentation were evaluated. Visibility ratings ranged from zero to four. Results In the cases of perineural cyst, spinal canal stenosis, and nerve root impingement, the mean difference between the VISTA and T2 TSE visibility scores was 0.04, 0.54, and 0.56, respectively. The VISTA and T2 TS had standard deviation differences of 0.006, 0.026, and 0.06, respectively. The "t" values for nerve root impingement, spinal canal stenosis, and perineural cysts were, in order, 50, 180, and 70. Because the p-value was <0.01, a statistically significant variation has been observed. Conclusion In the diagnosis of neural and perineuronal disorders, the visibility scores for 3D T2 TSE (VISTA) were considerably better than those for 2D T2 TSE in identifying perineural cysts, spinal canal stenosis, and nerve root indentation.

Evaluation of left atrial function and tissue after pulmonary vein isolation - initial results of The EVALUATE-PVI STUDY

Abstract Background Pulmonary vein (PV) isolation (PVI) by catheter ablation is a well-established treatment for atrial fibrillation (AF). Currently, the direct and long-term impact of ablation injury on left atrial (LA) function and tissue is poorly understood. Purpose The aim of this study is to evaluate the acute and long-term impact of radiofrequency (RF) PVI ablation on LA function and corresponding tissue changes as assessed with cardiac magnetic resonance imaging (CMR). Methods Seventeen patients (76.5% male, 60.8±9.7 years) with AF who underwent primo RF PVI as part of the EVALUATE-PVI STUDY underwent three sequential CMR scans prior to PVI, and &amp;lt;72 hours and 3 months after PVI ablation. T2-weighted turbo spin echo imaging was applied to assess post-ablation edema. High-resolution three-dimensional (3D) late gadolinium-enhancement (LGE) images were acquired to study acute LA wall injury and chronic fibrosis formation. LA volume and function were derived from two-chamber and four-chamber SSFP cine images. Maximum and minimal LA volume was indexed to body surface area to compute LAVimax and LAVimin. Longitudinal LA strain was calculated using feature tracking, and subdivided into reservoir, conduit, and contractile strain. Results Acutely post-PVI, T2w images showed increased signal intensity and thickening throughout the entire LA wall. LGE images showed diffuse increased signal intensity, without distinct focal high signal intensity areas (Fig. 1). Functional assessment at this stage showed significantly reduced LA emptying fraction (EF), LA reservoir, and contractile strain compared to pre-PVI (54.8±11.1% vs. 48.5±7.9%, p=0.01; 18.6±5.6% vs. 14.6±2.0%, p&amp;lt;0.01; 8.5±3.5% vs. 5.2±1.8%, p&amp;lt;0.001, respectively), while LA conduit strain, LAVimin, and LAVimax did not differ significantly (Fig. 2). The LGE images acquired at 3 months, displayed clear, focal, high signal intensity areas surrounding the PVs. At this stage post-PVI, a significant increase in LA strain was observed (LA reservoir strain 14.6±2.0% vs. 16.9±3.5%, p&amp;lt;0.01; and contractile strain 5.2±1.8% vs. 6.7±2.9%, p&amp;lt;0.01; respectively). LAVimax was significantly reduced at 3 months compared to acutely post-PVI (Fig. 2). LAVimin and LA conduit strain did not differ between the pre- and post-PVI stages. Conclusion PVI ablation resulted in temporary reduced LA function acutely post-PVI, likely due to edema and thickening of the LA wall. At three months, LGE images revealed clearly visible increased signal intensity in the ablated segments. At this stage, LA function improved, though full recovery did not occur as evidenced by the persistent slightly reduced contractile strain, possibly due to the ablation-induced loss of LA myocardium and formation of fibrosis. The decrease in LAVimax indicates reverse remodeling as a result of the ablation.

Investigating MRI-Associated Biological Aspects of Racial Disparities in Prostate Cancer for African American and White Men.

Understanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes. To investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men. Retrospective. One hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy. 3.0 T, T2-weighted turbo spin echo imaging, a single-shot spin-echo EPI sequence diffusion-weighted imaging, and a gradient echo sequence dynamic contrast-enhanced MRI with an ultrafast 3D spoiled gradient-echo sequence. The diagnostic performance of mpMRI in AA and W men was assessed using detection rates (DRs) and positive predictive values (PPVs) in zones defined by the PI-RADS v2.1 prostate sector map. Quantitative MRI parameters, including Ktrans and ve of clinically significant (cs) PCa (Gleason score ≥ 7) tumors were compared between AA and W sub-cohorts after matching age, prostate-specific antigen (PSA), and prostate volume. Weighted Pearson's chi-square and Mann-Whitney U tests with a statistically significant level of 0.05 were used to examine differences in DR and PPV and to compare parameters between AA and matched W men, respectively. A total number of 264 PCa lesions were identified in the study cohort. The PPVs in the peripheral zone (PZ) and posterior prostate of mpMRI for csPCa lesions were significantly higher in AA men than in matched W men (87.8% vs. 68.1% in PZ, and 89.3% vs. 69.6% in posterior prostate). The Ktrans of index csPCa lesions in AA men was significantly higher than in W men (0.25 ± 0.12 vs. 0.20 ± 0.08 min-1; P < 0.01). This study demonstrated race-related differences in the diagnostic performances and quantitative MRI measures of csPCa that were not reflected in age, PSA, and prostate volume. 3 TECHNICAL EFFICACY: Stage 2.

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical-pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Gray–white matter contrast reversal on T1-weighted spin-echo in postmortem brain

PurposeThe image contrast of postmortem magnetic resonance imaging (MRI) may differ from that of antemortem MRI because of circulator arrest, changes in postmortem tissue, and low-body-temperature scanning conditions. In fact, we have found that the signal intensity of white matter (WM) on T1-weighted spin-echo (T1WSE) images of the postmortem brain was lower than that of gray matter (GM), which resulted in image contrast reversal between GM and WM relative to the living brain. However, the reason for this phenomenon is unclear. Therefore, the aim of this study is to clarify the reason why image contrast reversal occurs between GM and WM of the postmortem brain. Materials and methodsTwenty-three corpses were included in the study (mean age, 60.6 years; range: 19–60 years; mean rectal temperature at scan, 6.9℃; range: 4–11℃). On a 1.5 T MRI system, postmortem T1W-SE MRI of the brain was conducted in the 23 corpses prior to medico-legal autopsy. Next, T1 and T2 of the GM and WM at the level of the basal ganglia were determined in the same participants using inversion recovery and multiple SE sequences, respectively. The proton density (PD) was also calculated from the T1 and T2 images (in the same slice). ResultsT1W-SE image contrast between the GM and WM of all postmortem brains was inverted relative to the living brain. T1 (579 ms in GM and 307 ms in WM) and PD (64 in GM and 44 in WM) of the postmortem brain decreased compared with the living brain. While T1 of WM/GM remained below 1 even postmortem, the PD of WM/GM decreased. T2 (110 ms in GM and 98 ms in WM) of the postmortem brain did not differ from the living brain. ConclusionThe decrease in PD of WM/GM in the postmortem brain may be the major driver of contrast reversal between the GM and WM relative to the living brain.

Investigating TE effects on the relationship between signal intensity and PVP-grafted SPION concentration in T2-weighted Spin Echo imaging

Investigating TE effects on the relationship between signal intensity and PVP-grafted SPION concentration in T2-weighted Spin Echo imaging

Evaluation of the performance of a 7-T 8 × 2 transceiver array.

The decoupled 8 × 2 transceiver array has been shown to achieve a mean B1 + of 11.7 uT with a coefficient of variation of ~11% over the intracranial brain volume for 7-T MR imaging. However, this array may be thought to give lower signal-to-noise ratio (SNR) and higher g-factors for parallel imaging compared with a radio frequency (RF) receive-only coil due to the latter's higher coil count and use of coil overlap to reduce the mutual impedance. Nonetheless, because the transceiver's highly decoupled design (pertinent for transmission) should also be constructive for reception, we measured the noise correlation, g-factors, and SNR for the decoupled transceiver in comparison with a commercial reference coil. We found that although the transceiver has half the number of receive elements in comparison with the reference coil (16 vs. 32), comparable g-factors and SNR over the head were obtained. From five subjects, the transceiver versus reference coil SNR was 65 ± 10 versus 67 ± 15. The mean noise correlation for all coil pairs was 10% ± 5% and 12% ± 9% (transceiver and reference coil, respectively). As changes in load impedance may alter the S parameters, we also examined the performance of the transceiver with tuned and matched (TM) versus untuned and unmatched (UTM) conditions on five subjects. We found that the noise correlation and SNR are robust to load variation; a noise correlation of 10% ± 5% and 10% ± 6% was determined with TM versus UTM conditions (SNRUTM/SNRTM = 0.97 ± 0.08). Finally, we demonstrate the performance of the array in human brain using T2-weighted turbo spin echo imaging, finding excellent SNR performance in both caudal and rostral brain regions.

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.

Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors.

We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors. This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers' preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings. The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P < 0.001, respectively). The observer's preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR. DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors.

Magnetic resonance imaging scoring system of the lower limbs in adult patients with suspected idiopathic inflammatory myopathy

PurposeWe aim to propose a visual quantitative score for muscle edema in lower limb MRI to contribute to the diagnosis of idiopathic inflammatory myopathy (IIM).Material and methodsWe retrospectively evaluated 85 consecutive patients (mean age 57.4 ± 13.9 years; 56.5% female) with suspected IIM (muscle weakness and/or persistent hyper-CPK-emia with/without myalgia) who underwent MRI of lower limbs using T2-weighted fast recovery-fast spin echo images and fat-sat T2 echo planar images. Muscle inflammation was evaluated bilaterally in 11 muscles of the thigh and eight muscles of the leg. Edema in each muscle was graded according to a four-point Likert-type scale adding up to 114 points ([11 + 8)] × 3 × 2). Diagnostic accuracy of the total edema score was explored by assessing sensitivity and specificity using the area under the ROC curve. Final diagnoses were made by a multidisciplinary Expert Consensus Panel applying the Bohan and Peter diagnostic criteria whenever possible.ResultsOf the 85 included patients, 34 (40%) received a final diagnosis of IIM (IIM group) while 51 (60%) received an alternative diagnosis (non-IIM group). A cutoff score ≥ 18 was able to correctly classify patients having an IIM with an area under the curve of 0.85, specificity of 96%, and sensitivity of 52.9%.ConclusionOur study demonstrates that a quantitative MRI score for muscle edema in the lower limbs (thighs and legs) aids in distinguishing IIM from conditions that mimic it.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.