3D T1-weighted Images Research Articles

Gray and white matter differences in the medial temporal lobe in late-life depression: a multimodal PET-MRI investigation.

Late-life depression (LLD) is characterized by medial temporal lobe (MTL) abnormalities. Although gray matter (GM) and white matter (WM) differences in LLD have been reported, few studies have investigated them concurrently. Moreover, the impact of aetiological factors, such as neurodegenerative and cerebrovascular burden, on tissue differences remains elusive. This prospective cross-sectional study involved 72 participants, including 33 patients with LLD (mean age 72.2 years, 23 female) and 39 healthy controls (HCs) (mean age 70.6 years, 24 female), who underwent clinical and positron emission tomography (PET)-magnetic resonance imaging (MRI) assessments. High-resolution 3D T1-weighted and T2-weighted FLAIR images were used to assess MTL GM volumes and white matter hyperintensities (WMHs), a proxy for cerebrovascular burden. Diffusion kurtosis imaging metrics derived from multishell diffusion MRI data were analyzed to assess WM microstructure in the following MTL bundles reconstructed using constrained spherical deconvolution tractography: uncinate fasciculus, fornix, and cingulum. Standardized uptake value ratio of 18F-MK-6240 in the MTL was used to assess Alzheimer's disease (AD) type tau accumulation as a proxy for neurodegenerative burden. Compared to HCs, patients with LLD showed significantly lower bilateral MTL volumes and WM microstructural differences primarily in the uncinate fasciculi bilaterally and right fornix. In patients with LLD, higher vascular burden, but not tau, was associated with lower MTL volume and more pronounced WM differences. LLD was associated with both GM and WM differences in the MTL. Cerebrovascular disease, rather than AD type tau-mediated neurodegenerative processes, may contribute to brain tissue differences in LLD.

Clinical Evaluation of 3D Motion-Correction Via Scout Accelerated Motion Estimation and Reduction Framework Versus Conventional T1-Weighted MRI at 1.5 T in Brain Imaging.

The aim of this study was to investigate the occurrence of motion artifacts and image quality of brain magnetic resonance imaging (MRI) T1-weighted imaging applying 3D motion correction via the Scout Accelerated Motion Estimation and Reduction (SAMER) framework compared with conventional T1-weighted imaging at 1.5 T. A preliminary study involving 14 healthy volunteers assessed the impact of the SAMER framework on induced motion during 3 T MRI scans. Participants performed 3 different motion patterns: (1) step up, (2) controlled breathing, and (3) free motion. The patient study included 82 patients who required clinically indicated MRI scans. 3D T1-weighted images (MPRAGE) were acquired at 1.5 T. The MRI data were reconstructed using either regular product reconstruction (non-Moco) or the 3D motion correction SAMER framework (SAMER Moco), resulting in 145 image sequences. For the preliminary and the patient study, 3 experienced radiologists evaluated the image data using a 5-point Likert scale, focusing on overall image quality, artifact presence, diagnostic confidence, delineation of pathology, and image sharpness. Interrater agreement was assessed using Gwet's AC2, and an exploratory analysis (non-Moco vs SAMER Moco) was performed. Compared with non-Moco, the preliminary study demonstrated significant improvements across all imaging parameters and motion patterns with SAMER Moco (P < 0.001). Odds ratios favoring SAMER Moco were >999.999 for freedom of artifact and overall image quality (P < 0.0001). Excellent or good ratings for freedom of artifact were 52.4% with SAMER Moco, compared with 21.4% for non-Moco. Similarly, 66.7% of SAMER Moco images were rated excellent or good for overall image quality versus 21.4% for non-Moco. Multireader interrater agreement was excellent across all parameters.The patient study confirmed that SAMER Moco provided significantly superior image quality across all evaluated imaging parameters, particularly in the presence of motion (P < 0.001). Diagnostic confidence was rated as excellent or good in 95.1% of SAMER Moco cases, compared with 78.1% for non-Moco cases. Similarly, overall image quality was rated as excellent or good in 89.8% of SAMER Moco cases versus 65.9% for non-Moco cases. The odds ratios for diagnostic confidence and for overall image quality were 6.698 and 6.030, respectively, both favoring SAMER Moco (P < 0.0001). Multireader interrater agreement was excellent across all parameters. The application of SAMER in T1-weighted imaging datasets is feasible in clinical routine and significantly increases image quality and diagnostic confidence in 1.5 T brain MRI by effectively reducing motion artifacts.

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD. A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging. Cortical thickness, surface area, and subcortical volumes were measured using FreeSurfer software. Common and classic machine learning models were utilized to identify distinct morphometric alterations between BD and MDD. Significant morphological differences were observed in both common and distinct brain regions between BD, MDD, and HC. Specifically, abnormalities in the amygdala, thalamus, medial orbitofrontal cortex and fusiform were observed in both BD and MDD compared with HC. Relative to HC, unique differences in BD were identified in the lateral occipital and inferior/middle temporal regions, whereas MDD exhibited differences in nucleus accumbens and middle temporal regions. BD exhibited larger surface area in right middle temporal gyrus and greater right nucleus accumbens volume compared to MDD. The integration of two-stage models, including deep neural network (DNN) and support vector machine (SVM), achieved an accuracy rate of 91.2% in discriminating individuals with BD from MDD. These findings demonstrate that structural MRI combined with machine learning techniques can accurately discriminate individuals with BD from MDD, and provide a foundation supporting the potential of this approach to improve diagnostic accuracy.

Association of the Cervical Canal Area With Disability and Progression in People With Multiple Sclerosis.

In multiple sclerosis (MS), brain reserve serves as a protective factor against cognitive impairment. Previous research has suggested a structural counterpart in the spine-spinal cord reserve-seemed to be associated with physical disability. This study aimed to investigate the potential of the cervical canal area (CCaA) as a proxy for spinal cord reserve in a multicentric cohort of people with MS (PwMS). This retrospective, multicentric, longitudinal study included PwMS and healthy controls (HCs) from 9 European MAGNIMS sites. Baseline cervical 3D T1-weighted images were acquired, excluding poor-quality images. CCaA was estimated independently at the C2/C3 and C3/C4 levels. The Expanded Disability Status Scale (EDSS) score was assessed at baseline and 5-year follow-up. We analyzed mean CCaA differences between groups and the association of CCaA with baseline EDSS scores and disability progression using multivariable regression models adjusted for age, sex, spinal cord parenchymal fraction, and cervical cord lesions. After quality check, the cohort included 177 HCs (mean age 39.8 years, 57.6% women) and 428 PwMS (mean age 46.5 years, 60.8% women), comprising 289 people with relapsing MS (PwRMS) and 139 people with progressive MS (PwPMS). No significant differences in CCaA were found between HCs and PwRMS at C2/C3 or C3/C4 levels. Conversely, PwPMS showed a smaller CCaA at the C2/C3 level (210.51 mm2) than HCs (214.62 mm2, estimated mean difference [EMD] -4.11, 95% CI -6.28 to -1.00, p = 0.007) and PwRMS (213.68 mm2, EMD -3.17, 95% CI -5.22 to -0.34, p = 0.026). PwPMS also had a smaller CCaA at C3/C4 (165.16 mm2) than HCs (169.67 mm2, EMD -4.51, 95% CI -5.50 to -1.60, p < 0.001) and PwRMS (169.44 mm2, EMD -3.81, 95% CI -5.22 to -0.34, p < 0.001). At the C3/C4 level, CCaA and baseline EDSS scores were significantly associated (β = -0.13, p < 0.001); in addition, PwMS with clinical worsening at 5-year follow-up displayed a smaller baseline CCaA (worsened vs stable: 167.03 mm2 vs 169.13 mm2, EMD -2.10, 95% CI -3.98 to -023, p = 0.028). CCaA was associated with baseline EDSS scores and clinical worsening in a multicentric MS cohort, suggesting the existence of spinal cord reserve. PwPMS had a smaller CCaA, indicating that reduced spinal cord reserve might be characteristic of progressive MS. Therefore, spinal cord reserve may represent a novel radiologic marker for better understanding physical disability in MS.

Alterations of gray matter volume and functional connectivity in patients with cognitive impairment induced by occupational aluminum exposure: a case-control study.

Cognitive impairment (CI) is a condition in which an individual experiences noticeable impairment in thinking abilities. Long-term exposure to aluminum (Al) can cause CI. This study aimed to determine the relationship between CI and MRI-related changes in postroom workers exposed to Al. Thirty patients with CI and 25 healthy controls were recruited. Plasma aluminum levels were measured using inductively coupled plasma-mass spectrometry. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and an auditory-verbal learning test (AVLT). All participants underwent magnetic resonance imaging scans. 3D T1-weighted anatomical images and resting-state functional magnetic resonance imaging data were acquired, and voxel-based morphometry and ROI-based FC were used for analysis. A mediation analysis was also conducted. Plasma aluminum levels were significantly higher in the CI group than in the normal control group. The gray matter (GM) volume in the left caudate and bilateral hippocampus was lower in the CI group and was positively correlated with cognitive scale scores. There was no significant difference in functional connectivity (FC) between the left caudate and the whole brain between the two groups. Significant alterations in hippocampal FC were observed in certain brain areas, mainly in the left cerebellar vermis, left middle frontal gyrus (BA9), and right superior frontal gyrus relative to the supplementary motor area (BA6). The FC coefficients were also associated with cognitive scale scores. Furthermore, plasma Al concentration was negatively correlated with the Montreal Cognitive Assessment score, bilateral hippocampal GM volume, and FC coefficient between the left hippocampus and left cerebellar vermis. Mediation analysis showed GM alteration of left caudate and bilateral hippocampus and FC alteration of left hippocampus to left cerebellar vermis could explained 19.80-32.07% of the effect of MoCA scores change related to Al exposure, besides the GM alteration of right hippocampus acted as indirect mediator (68.75%) of the association between Al and AVLT delayed recall scores. Our data indicates that alterations in the structure and function of special brain domain, especially the hippocampus, are associated with Al-induced CI. These brain regions can partly explain the effect of Al on cognitive impairment.

Improved identification of multiple sclerosis brain lesions with three-dimensional double inversion recovery MRI

Background Multiple sclerosis (MS) is a chronic autoimmune disease that results in demyelination within the central nervous system, causing brain lesions and neurological dysfunction. Precise identification of these lesions is essential for proper diagnosis and management. Aim This study aims to assess the effectiveness of three-dimensional double inversion recovery (3D DIR) MRI in detecting MS brain lesions compared to two-dimensional fluid-attenuated inversion recovery (2D FLAIR) and T2-weighted imaging (T2WI). Patients and methods A cross-sectional study involving 49 MS patients (21 patients with clinically suspected MS, 28 patients with remitting relapsing MS). MRI scans were performed using a standardized protocol, including 3D DIR, 2D FLAIR, and T2WI sequences. Lesion detection rates and regional lesion visibility were analyzed and compared. Results 3D DIR MRI significantly improved lesion detection rates, identifying 19% more lesions than 2D FLAIR and 28% more than T2WI (P&lt;0.001 for both). Regionally, 3D DIR MRI showed superior detection in the periventricular (13%, 23% higher), deep white matter (8%, 14% higher), juxtacortical (10%, 27% higher), intracortical (71%, 91% higher), and infratentorial (27%, 10% higher) regions compared to 2D FLAIR and T2WI, respectively (P&lt;0.001 for all). Conclusion 3D DIR MRI is more effective in detecting MS brain lesions than 2D FLAIR and T2WI, suggesting its integration into routine MS imaging protocols for better diagnostic accuracy.

Multimodal magnetic resonance imaging studies on non-motor symptoms of Parkinson's disease.

This study aims to investigate the diagnostic value of multi-modal magnetic resonance imaging (MRI) utilizing arterial spin labeling (ASL), quantitative susceptibility mapping (QSM), and 3D T1-weighted imaging (3DT1WI) in patients with Parkinson's disease (PD). Additionally, it evaluates the relationship between MRI findings and non-motor symptoms associated with PD. ASL, QSM, and 3DT1WI scans were performed on 48 PD patients and 46 healthy controls (HC). We extracted and analyzed differences in regional cerebral blood flow (rCBF), magnetic susceptibility, and gray matter density parameters between the two groups. These MRI parameters were correlated with clinical scale scores assessing non-motor symptoms, including cognitive function, sleep quality, olfaction, autonomic function, anxiety, depression, and fatigue. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of each imaging modality in distinguishing PD from HC. The areas under the ROC curve (AUC) for rCBF, magnetic susceptibility, and gray matter density were 0.941, 0.979, and 0.624, respectively. In PD patients, a negative correlation was found between Unified Parkinson's Disease Rating Scale Part II (UPDRS II) scores and rCBF in the bilateral precuneus. The Pittsburgh Sleep Quality Index (PSQI) scores negatively correlated with rCBF in the left middle temporal gyrus and right middle occipital gyrus. Hamilton Depression Rating Scale (HAMD) scores positively correlated with QSM values in the right supplementary motor area, while scores on the Argentine Smell Identification Test (AHRS) negatively correlated with QSM values in the same area. Disease duration showed a positive correlation with QSM values in the right middle cingulate gyrus. Additionally, PSQI scores positively correlated with QSM values in the left middle cingulate gyrus, and fatigue severity scale (FSS) scores also positively correlated with QSM values in the left middle cingulate gyrus. Gray matter atrophy in the left inferior temporal gyrus was associated with cognitive impairment in PD. Occipital hypoperfusion and cortical atrophy in the left inferior temporal gyrus may serve as novel imaging biomarkers for PD and are associated with sleep disturbances and cognitive impairment in PD patients. Extensive iron deposition in the bilateral cerebral cortex of PD patients may be a contributing factor to non-motor symptoms such as sleep disturbances and fatigue. Multimodal imaging techniques, including ASL, QSM, and 3DT1WI, can enhance the diagnostic accuracy for PD.

Decreased Water Diffusivity Along the Perivascular Space in Older Adults With Poor Sleep Quality.

This study included 52 Japanese older adults with Pittsburgh Sleep Quality Index (PSQI) scores > 5 and 52 healthy controls (HCs) with PSQI score ≤ 5. Diffusion-weighted imaging (DWI) and 3D T1-weighted imaging were acquired using 3T magnetic resonance imaging. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated using preprocessed DWI. The choroid plexus volume (CPV) was calculated using FreeSurfer 6.0. The mean ALPS index and CPV were compared between the older adults with poor sleep quality (PSQ) and HCs using a general linear model, adjusted for covariates including age, sex, years of education, total intracranial volume, systolic blood pressure, hemoglobin A1c, and white matter lesion volume. We also conducted a partial correlation analysis between the mean ALPS index and CPV, Montreal Cognitive Assessment (MoCA), and PSQI scores, adjusting for all the mentioned covariates. The PSQ group had a significantly lower mean ALPS index than HCs. The mean ALPS index in the PSQ group was negatively correlated with CPV and positively correlated with the MoCA score. Therefore, older adults with PSQ may experience dysfunction in the excretory pathway of the perivascular space around the medullary veins. This impairment may be associated with an increase in CPV and cognitive dysfunction.

Estimated Brain Age in Healthy Aging and Across Multiple Neurological Disorders.

The brain aging in the general population and patients with neurological disorders is not well understood. To characterize brain aging in the above conditions and its clinical relevance. Retrospective. A total of 2913 healthy controls (HC), with 1395 females; 331 multiple sclerosis (MS); 189 neuromyelitis optica spectrum disorder (NMOSD); 239 Alzheimer's disease (AD); 244 Parkinson's disease (PD); and 338 cerebral small vessel disease (cSVD). 3.0 T/Three-dimensional (3D) T1-weighted images. The brain age was estimated by our previously developed model, using a 3D convolutional neural network trained on 9794 3D T1-weighted images of healthy individuals. Brain age gap (BAG), the difference between chronological age and estimated brain age, was calculated to represent accelerated and resilient brain conditions. We compared MRI metrics between individuals with accelerated (BAG ≥ 5 years) and resilient brain age (BAG ≤ -5 years) in HC, and correlated BAG with MRI metrics, and cognitive and physical measures across neurological disorders. Student's t test, Wilcoxon test, chi-square test or Fisher's exact test, and correlation analysis. P < 0.05 was considered statistically significant. In HC, individuals with accelerated brain age exhibited significantly higher white matter hyperintensity (WMH) and lower regional brain volumes than those with resilient brain age. BAG was significantly higher in MS (10.30 ± 12.6 years), NMOSD (2.96 ± 7.8 years), AD (6.50 ± 6.6 years), PD (4.24 ± 4.8 years), and cSVD (3.24 ± 5.9 years) compared to HC. Increased BAG was significantly associated with regional brain atrophy, WMH burden, and cognitive impairment across neurological disorders. Increased BAG was significantly correlated with physical disability in MS (r = 0.17). Healthy individuals with accelerated brain age show high WMH burden and regional volume reduction. Neurological disorders exhibit distinct accelerated brain aging, correlated with impaired cognitive and physical function. 4 TECHNICAL EFFICACY: Stage 2.

Deep-learning assessment of hippocampal magnetic susceptibility in Alzheimer's disease.

Quantitative susceptibility mapping (QSM) is pivotal for analyzing neurodegenerative diseases. However, accurate hippocampal segmentation remains a challenge. This study introduces a method for extracting hippocampal magnetic susceptibility values using a convolutional neural network (CNN) model referred to as 3D residual UNET. The model was pre-trained on whole QSM images and hippocampal segmentations from 3D T1-weighted images of 297 patients with Alzheimer's disease and mild cognitive impairment. Fine-tuning was conducted through manually annotated hippocampal segmentations from the QSM images of 60 patients. The performance was assessed using the Dice similarity coefficient (DSC) and Pearson correlation coefficient. The developed model was applied to another 98 patients, 49 with AD and 49 with mild cognitive impairment (MCI), and the correlation between the hippocampal magnetic susceptibility and volume was evaluated. The mean DSC for the hippocampal segmentation model was 0.716 ± 0.045. The correlation coefficient between the magnetic susceptibility values derived from manual segmentation and the CNN model was 0.983. The Pearson correlation coefficient between magnetic susceptibility and hippocampal volume from the CNN model was -0.252 (p = 0.012) on the left side and -0.311 (p = 0.002) on the right. The 3D residual UNET model enhances hippocampal analysis precision using QSM, which is capable of accurately extracting magnetic susceptibility.

An SBM and TBSS Analysis in Early-stage Patients With Alzheimer's Disease, Lewy Body Dementias, and Corticobasal Syndrome.

To compare gray matter (GM) and white matter (WM) changes in patients with Alzheimer's disease (AD), Lewy body dementias (LBD), corticobasal syndrome (CBS), and healthy controls (HC). Surface-based morphometry (SBM) was assessed on 3D T1-weighted images using FreeSurfer image analysis and WM microstructure was studied using Tract-Based Spatial Statistics (TBSS) in 12 AD, 15 LBD, 10 CBS patients, and 10 HC. Patients with AD, compared with HC, exhibited reduced cortical surface area and volume in the superior frontal, middle frontal, and medial orbitofrontal cortex. In TBSS, AD patients, compared with HC and LBD, displayed decreased fractional anisotropy, axial diffusivity, and increased radial diffusivity in all major WM tracts. Other comparisons between the groups yielded no differences, either in the SBM or the TBSS analysis. The results indicate significant early structural changes in the GM of the frontal lobe, along with WM alterations early in AD patients.

Sex differences in brain MRI using deep learning toward fairer healthcare outcomes.

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique.

The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE). This retrospective study included 54 patients with focal hepatic lesions who underwent dynamic contrast-enhanced MRI. Both standard HBP images using CS with AiCE and high-precision HBP images using modified Fast 3D mode wheel and PIQE were obtained. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated using the Wilcoxon signed-rank test. p values of < 0.05 were considered to be statistically significant. Scores for image noise, conspicuity of liver contours and intrahepatic structures, and overall image quality in high-precision HBP imaging using modified Fast 3D mode wheel and PIQE were significantly higher than those in HBP imaging using CS and AiCE (all p < 0.001). There was no significant difference in the presence of artifact and motion-related blurring. There were no significant differences between the sequences in SNR (p = 0.341) or CNR (p = 0.077). The detection rate of focal hepatic lesions was 71.4-85.3% in CS with AiCE, and 82.2-95.8% in modified Fast 3D mode wheel and PIQE. A high-precision MR sequence using a modified Fast 3D mode wheel and PIQE is applicable for the HBP of BH 3D T1-weighted imaging.

Magnetic Resonance Elastography Combined With PI-RADS v2.1 for the Identification of Clinically Significant Prostate Cancer.

Multiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). To investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI-RADS v2.1 for diagnosing csPCa. Prospective. One hundred forty-seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa. T1-weighted fast spin-echo, T2-weighted fast spin-echo, single-shot echo-planar diffusion-weighted imaging, fast 3D gradient-echo T1-weighted dynamic contrast-enhanced imaging, and 3D single-shot spin-echo based echo-planar MR elastography at 3.0 T. The PI-RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI-RADS integrating stiffness with PI-RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI-RADS v2.1 and the modified PI-RADS. Spearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one-way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05. In the peripheral zone, csPCa (N = 35) had significantly higher SS than non-csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI-RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI-RADS, combing SS at 60 Hz with PI-RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI-RADS v2.1, with a sensitivity of 97% and specificity of 75%. Stiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI-RADS v2.1 improved the diagnostic accuracy and specificity for csPCa. 1 TECHNICAL EFFICACY: Stage 2.

Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI

ObjectiveTo compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).MethodsThis prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIRTM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.ResultsFifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71–4.40 vs 3.37–3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).ConclusionDL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.Critical relevance statementDL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.Key PointsDL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases.DL reconstruction showed superior image quality with reduced noise and ringing artifacts.Hepatic anatomic structures were more conspicuous on DL-reconstructed images.Graphical

Differentiation of MS lesions through analysis of microvascular distribution

Abstract Conventional MRI is crucial for diagnosing multiple sclerosis (MS) but lacks precision, leading to the clinico-radiological paradox and misdiagnosis risk, especially when confronted with unspecific lesions not related to MS. Advancements in perfusion-weighted imaging (PWI) with an algorithm designed for diseases with anticipated contrast agent extravasation offer insight into microvascular impairment and flow heterogeneity. Our study aimed to assess these factors in MS patients and their association with clinically relevant white matter injury and disease course. We evaluated 60 adults with white matter lesions (WML), including 50 diagnosed with MS or MS syndromes and 10 non-diseased symptomatic controls (SC) with unspecific WML. MRI included conventional three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), 3D magnetization-prepared 2 rapid acquisition gradient-echo (MP2RAGE), post-contrast 3D T1-weighted (T1) images and Dynamic Susceptibility Contrast (DSC) PWI at 3T. WML masks of “unspecific T2-FLAIR lesions”, “MS T2-FLAIR lesions”, and “MS T1-lesions” were manually outlined and validated by a neuroradiologist. DSC-derived parameters were analyzed in WML masks and healthy-appearing tissue. MS T2-FLAIR lesions showed increased flow heterogeneity and vasodilation compared to unspecific T2-FLAIR lesions in SC, as well as compared to unspecific T2-FLAIR lesions within the MS group. MS T1-lesions exhibited more homogenized flow. Our findings suggest that DSC-PWI combined with lesion delineation, can provide clinically relevant differentiation of MS lesions from unspecific WML, highlighting potential microvascular pathology previously overlooked in MS.

Quantifying CSF Dynamics disruption in idiopathic normal pressure hydrocephalus using phase lag between transmantle pressure and volumetric flow rate

Background and purposeIdiopathic normal pressure hydrocephalus (iNPH) is a cerebrospinal fluid (CSF) dynamics disorder as evidenced by the delayed ascent of radiotracers over the cerebral convexity on radionuclide cisternography. However, the exact mechanism causing this disruption remains unclear. Elucidating the pathophysiology of iNPH is crucial, as it is a treatable cause of dementia. Improving the diagnosis and treatment prognosis rely on the better understanding of this disease. In this study, we calculated the pulsatile transmantle pressure and investigated the phase lag between this pressure and the volumetric CSF flow rate as a novel biomarker of CSF dynamics disruption in iNPH. Methods44 iNPH patients and 44 age- and sex-matched cognitively unimpaired (CU) control participants underwent MRI scans on a 3T Siemens scanner. Pulsatile transmantle pressure was calculated analytically and computationally using volumetric CSF flow rate, cardiac frequency, and aqueduct dimensions as inputs. CSF flow rate through the aqueduct was acquired using phase-contrast MRI. The aqueduct length and radius were measured using 3D T1-weighted anatomical images. ResultsPeak pressure amplitudes and the pressure load (integrated pressure exerted over a cardiac cycle) were similar between the groups, but the non-dimensionalized pressure load (adjusted for anatomical factors) was significantly lower in the iNPH group (p<0.001, Welch's t-test). The phase lag between the pressure and the flow rate, arising due to viscous drag, was significantly higher in the iNPH group (p<0.001). ConclusionThe increased phase lag is a promising new biomarker for quantifying CSF dynamics dysfunction in iNPH. Statement of SignificanceThe exact mechanism causing the disruption of CSF circulation in idiopathic normal pressure hydrocephalus (iNPH) remains unclear. Elucidating the pathophysiology of iNPH is crucial, as it is a treatable cause of dementia. In this study, we provided an analytical and a computational method to calculate the pulsatile transmantle pressure and the phase lag between the pressure and the volumetric CSF flow rate across the cerebral aqueduct. The phase lag was significantly higher in iNPH patients than in controls and may serve as a novel biomarker of CSF dynamics disruption in iNPH.

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.

The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volumetry. This retrospective study collected 3D T1-weighted data using 3T from 42 participants for the simulated acceleration dataset and 48 for the validation dataset. The simulated acceleration dataset consists of three sets at different simulated acceleration levels (Simul-Accel) corresponding to level 1 (65% undersampling), 2 (70%), and 3 (75%). These images were then subjected to deep learning-based reconstruction (Simul-Accel-DL). Conventional images (Conv) without acceleration and DL were set as the reference. In the validation dataset, DICOM images were collected from Conv and accelerated scan with DL-based reconstruction (Accel-DL). The image quality of Simul-Accel-DL was evaluated using quantitative error metrics. Volumetric measurements were evaluated using intraclass correlation coefficients (ICCs) and linear regression analysis in both datasets. The volumes were estimated by two software, NeuroQuant and DeepBrain. Simul-Accel-DL across all acceleration levels revealed comparable or better error metrics than Simul-Accel. In the simulated acceleration dataset, ICCs between Conv and Simul-Accel-DL in all ROIs exceeded 0.90 for volumes and 0.77 for normative percentiles at all acceleration levels. In the validation dataset, ICCs for volumes > 0.96, ICCs for normative percentiles > 0.89, and R2 > 0.93 at all ROIs except pallidum demonstrated good agreement in both software. DL-based reconstruction achieves clinical feasibility of 3D T1 brain volumetric MRI by up to 75% acceleration relative to full-sampled acquisition.

Association between subcortical nuclei volume changes and cognition in preschool-aged children with tetralogy of Fallot after corrective surgery: a cross-sectional study

BackgroundNeurocognitive disorders frequently occur in patients with cyanotic congenital heart disease (CCHD) because of the hemodynamic abnormalities induced by preoperative cardiac structural changes. We aimed to evaluate subcortical nuclei volume changes and cognition in postoperative tetralogy of Fallot (TOF) children, and analyze their relationship with preoperative cardiac structural changes.MethodsThis case-control study involved thirty-six children with repaired TOF and twenty-nine healthy controls (HCs). We utilized three-dimensional (3D) T1-weighted high-resolution structural images alongside the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) to evaluate the cognitive differences between the TOF and HC group.ResultsWe observed notable differences in subcortical nuclei volume between the TOF and HC group, specifically in the left amygdala nucleus (LAM, TOF: 1292.60 ± 155.57; HC: 1436.27 ± 140.62, p < 0.001), left thalamus proper nucleus (LTHA, TOF: 6771.54 ± 666.03; HC: 7435.36 ± 532.84, p < 0.001), and right thalamus proper nucleus (RTHA, TOF: 6514.61 ± 715.23; HC: 7162.94 ± 554.60, p < 0.001). Furthermore, a diminished integrity of LAM ( β:-19.828, 95% CI: -36.462, -3.193), which showed an inverse relationship with the size of the preoperative ventricular septal defect (VSD), correlated with lower working memory indices in children with TOF.ConclusionsOur findings indicate that subcortical nuclei structural injuries possibly potentially stemming from cardiac anatomical abnormalities, are associated with impaired working memory in preschool-aged children with TOF. The LAM in particular may serve as a potential biomarker for neurocognitive deficits in TOF, offering predictive value for future neurodevelopmental outcomes, and shedding light on the neurophysiological mechanisms of these cognitive impairments.

Deep learning constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine

We sought to assess the image quality of three-dimensional (3D) T2-weighted (T2w) turbo spin echo (TSE) sequences with deep learning (DL)-constrained compressed sensing (CS) reconstruction relative to a reference two-dimensional (2D) T2w TSE sequence for routine clinical lumbar spine MRI. Fifty-three patients underwent imaging of the lumbar spine with a sagittal 2D T2w TSE sequence and with two CS-accelerated 3D T2w TSE sequences (voxel size of 0.4×0.4×0.5 mm) with CS factors of 7 and 11. The CS-accelerated sequences were reconstructed with iterative reconstruction with wavelet transformation (conventional CS) and secondly with a DL-constrained CS reconstruction (named CS-AI). Two readers graded image quality, based on 8 metrics (overall image quality, presence of image noise, presence of motion artifacts, delineation/conspicuity and clarity of anatomical structures such as the spinal cord, cauda equine nerve roots, cerebrospinal fluid (CSF), intervertebral disc, and bone marrow and intervertebral foramen) using Likert scales. Overall inter-readout agreement was substantial (Krippendorff's α=0.724, 95% confidence interval [CI]: 0.692-0.755). The CS7-AI and CS11-AI sequences were comparable or better than the 2D sequence in all 8 metrics (p < 0.001-p > 0.99). The CS7 and CS11 sequences were comparable or better than the 2D sequence in only 5 and 3 of the 8 metrics, respectively (p < 0.001-p > 0.99). A DL-constrained CS reconstruction significantly improves the quality of accelerated high-resolution 3D T2w TSE imaging of the lumbar spine. Thus, high-quality imaging in a submillimeter resolution in all three imaging planes can be achieved without compromising the image quality as compared with standard 2D T2w TSE imaging.