3D T1-weighted Images Research Articles

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

Correlations between Cerebrospinal Fluid Biomarkers and Gray Matter Atrophy in Alzheimer's and Behavioural Variant Frontotemporal Dementia.

Distinguishing between frontotemporal dementia (FTD) and Alzheimer's disease (AD) in their early stages remains a significant clinical challenge. Cerebrospinal fluid (CSF) biomarkers (total Tau, phosphorylated Tau, and beta-amyloid) are promising candidates for identifying early differences between these conditions. This study investigates the relationship between grey matter density and CSF markers in the behavioural variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). CSF and 3D T1-weighted magnetic resonance (MR) images were acquired from 14 bvFTD patients, 15 AD patients, and 13 cognitively normal (CN) matched subjects. The CSF markers and their relative ratios (total Tau/beta-amyloid, phosphorylated Tau/beta-amyloid) were compared across the three groups. Voxel-based morphometry (VBM) was performed to characterize the anatomical changes in bvFTD and AD patients compared to CN subjects. Grey matter density maps were obtained by automatic segmentation of 3.0 Tesla 3D T1-Weighted MR Images, and their correlation with CSF markers and relative ratios was investigated. Results demonstrated that, as compared to CN subjects, AD patients are characterised by higher CSF total Tau levels and lower beta-amyloid levels; however, beta-amyloid and relative ratios discriminated AD from bvFTD. In addition, AD and bvFTD patients showed different patterns of atrophy, with AD exhibiting more central (temporal areas) and bvFTD more anterior (frontal areas) atrophy. A correlation was found between grey matter density maps and CSF marker concentrations in the AD group, with total Tau and phosphorylated Tau levels showing a high association with low grey matter density in the left superior temporal gyrus. The study concludes that while bvFTD lacks a CSF marker profile, CSF beta-amyloid levels are useful for differentiating AD from bvFTD. Furthermore, MR structural imaging can contribute significantly to distinguishing between the two pathologies.

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.

Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance.

Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD. Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD. In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = -0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (r = -0.435, p = 0.006). Topological parameters, including characteristic path length (L p), global efficiency (E g), normalised shortest path length (λ) and nodal local efficiency (N le), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively. DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.

Comparing the imaging characteristics of middle-aged patients with multiple sclerosis and CADASIL: A retrospective cross-sectional study

BackgroundFew studies have quantitatively analyzed the imaging disparities between multiple sclerosis (MS) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). We aimed to compare the imaging characteristics of MS and CADASIL in middle-aged patients. Materials and MethodsThis retrospective study used a single-center database and included patients aged 40–60 years with MS and CADASIL who underwent the designated imaging protocol including 3D T1-weighted imaging and fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging and susceptibility-weighted imaging between January 2018 and March 2023. Patients with MRI-detected macrobleeds were excluded. ResultsA total of 27 patients with MS (mean age, 46.7 years ± 4.4, 8 men) and 30 patients with CADASIL (mean age, 51.6 years ± 5.8, 14 men) were included. No significant differences were observed in the Fazekas grades of white matter lesions (WMLs). Patients with CADASIL exhibited greater external capsule involvement (56.7% vs.18.5 %; p = 0.006), whereas the MS group had more lesions in the corpus callosum (81.5% vs. 53.3 %, p = 0.02) and brainstem (74.1% vs. 46.7 %, p = 0.04). The CADASIL group exhibited a higher incidence of microbleeds (12.07 vs. 0.11, p = 0.001). The WMLs in the MS group exhibited a lower T1 lesion/cerebrospinal fluid signal index (2.206 vs. 2.882, p < 0.001). A value of ≤2.57 demonstrated a sensitivity of 92.6 % and a specificity of 90.0 % in differentiating MS. Patients with MS had a thinner corpus callosum (7.18 mm vs 7.86 mm, p = 0.04), while patients with CADASIL showed significantly higher mean diffusivity (0.8776 × 10–3 vs. 0.7637 × 10–3 mm2/s, p = 0.03) and lower fractional anisotropy (0.7581 vs. 0.8389, p = 0.04) in the splenium of the corpus callosum. ConclusionMiddle-aged patients with MS and CADASIL showed comparable Fazekas grades for WMLs. However, lesion distribution, T1 signal characteristics, and splenic diffusivity changes can help differentiate between MS and CADASIL.

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.

Development and validation of a perivascular space segmentation method in multi-center datasets

BackgroundPerivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies. MethodsWe annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images. ResultsIn the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region. ConclusionThe mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Visualization of distribution in the vitreous cavity via eye drops using ultra-heavily T2-weighted sequences in MRI: a preliminary study with enucleated pig eyes

We investigated whether magnetic resonance imaging can visualize the distribution in the vitreous cavity via eye drops of ophthalmic solutions, gadolinium-based contrast agent, and 17O-water, and to clarify the usefulness of ultra-heavily T2-weighted sequences in the research of intraocular distribution. Five different solutions (V-ROHTO, TRAVATANZ, gadobutrol, H217O, and saline) were administered to excised pig eye specimens. The samples were scanned using T1 mapping, T2 mapping, 3D T2-weighted (echo times (TE): 500, 3200, and 4500 ms), a half-Fourier single-shot turbo-spin echo sequence (HASTE; TE: 440 and 3000 ms), and 3D-real inversion-recovery before eye drops administration. Subsequently, we used a plastic dropper to drop a 0.5 mL solution each, and images were obtained up to 26 h later. Temporal changes in the T1 and T2 values of the anterior chamber and vitreous cavity were compared. The other sequences were evaluated by determining temporal signal changes as signal intensity ratio (SIR) compared to “No drop.” The T1 and T2 values of samples treated with gadobutrol and H217O decreased over time. The SIR of samples treated with gadobutrol and H217O showed remarkable changes in the 3D T2-weighted images, whereas no remarkable temporal changes were observed in the other solutions. Longer TEs resulted in remarkable changes. We demonstrated that visualization of distribution in the vitreous cavity via eye drops could be achieved with excised pig eyes using gadobutrol and H217O, but not with ophthalmic solutions. Ultra-heavily T2-weighted sequences may be promising for the early and highly sensitive visualization of the intraocular distribution of eye drops.

Lesion phenotyping based on magnetic susceptibility in pediatric multiple sclerosis.

Pediatric multiple sclerosis (MS) displays different pathological features compared to adult MS, which can be studied in vivo by assessing tissue magnetic susceptibility with 3T-MRI. We aimed to assess different white matter lesions (WMLs) phenotypes in pediatric MS patients using quantitative susceptibility mapping (QSM) and susceptibility mapping weighted imaging (SMWI) over 12 months. Eleven pediatric MS patients [female: 63.6%; mean ± standard deviation (SD) age and disease duration: 16.3 ± 2.2 and 2.4 ± 1.5; median (range) Expanded Disability Status Scale (EDSS) 1 (0-2)] underwent 3 Tesla-MRI exams and EDSS assessments at baseline and after 1 year. QSM and SMWI were obtained using 3-dimensional (3D)-segmented echo-planar-imaging with submillimetric spatial resolution. WMLs were classified according to their QSM appearance and SMWI was used to identify QSM hyperintensities ascribable to veins. Total brain volumes at baseline and follow-up were computed using high-resolution 3D T1-weighted images. Mean ± SD paramagnetic rim lesions (PRLs) prevalence was 7.0% ± 9.0. Fifty-four percent (6/11) of patients exhibited at least one PRL, with one patient exhibiting ≥ 4 PRLs. All patients showed QSM-iso-/hypo-intense lesions, which represented a mean ± SD of 65.8% ± 22.7 of total WMLs. QSM-hyperintense WMLs showed a positive correlation with total brain volume reduction at follow-up (r = 0.705; p = .02). No lesion was classified as different between baseline and follow-up. Chronic compartmentalized inflammation seems to occur early in pediatric MS patients with short disease duration. A high prevalence of iso-/hypo-intense lesions was found, which could account for the higher remyelination potential in pediatric MS.

Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

PurposeTo assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.Materials and methodsThis prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed.ResultsAI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5.ConclusionsAI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.

Acceleration of uterine 3D T2-weighted imaging by compressed SENSE-a multicentre study.

To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression. Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan.

Alterations in cortical thickness and volumes of subcortical structures in pediatric patients with complete spinal cord injury.

To study the changes in cortical thickness and subcortical gray matter structures in children with complete spinal cord injury (CSCI), reveal the possible causes of dysfunction beyond sensory motor dysfunction after CSCI, and provide a possible neural basis for corresponding functional intervention training. Thirty-seven pediatric CSCI patients and 34 age-, gender-matched healthy children as healthy controls (HCs) were recruited. The 3D high-resolution T1-weighted structural images of all subjects were obtained using a 3.0 Tesla MRI system. Statistical differences between pediatric CSCI patients and HCs in cortical thickness and volumes of subcortical gray matter structures were evaluated. Then, correlation analyses were performed to analyze the correlation between the imaging indicators and clinical characteristics. Compared with HCs, pediatric CSCI patients showed decreased cortical thickness in the right precentral gyrus, superior temporal gyrus, and posterior segment of the lateral sulcus, while increased cortical thickness in the right lingual gyrus and inferior occipital gyrus. The volume of the right thalamus in pediatric CSCI patients was significantly smaller than that in HCs. No significant correlation was found between the imaging indicators and the injury duration, sensory scores, and motor scores of pediatric CSCI patients. These findings demonstrated that the brain structural reorganizations of pediatric CSCI occurred not only in sensory motor areas but also in cognitive and visual related brain regions, which may suggest that the visual processing, cognitive abnormalities, and related early intervention therapy also deserve greater attention beyond sensory motor rehabilitation training in pediatric CSCI patients.