Diffusion-weighted Magnetic Resonance Imaging Research Articles

Monitoring prostate cancer after low-dose-rate hemigland brachytherapy with delta-radiomics of diffusion-weighted magnetic resonance imaging.

Monitoring prostate cancer after low-dose-rate hemigland brachytherapy with delta-radiomics of diffusion-weighted magnetic resonance imaging.

Utility of diffusion weighted Magnetic Resonance Imagining to detect non-palpable undescended testis.

To assess the effectiveness of diffusion-weighted magnetic resonance imaging (DW-MRI) in the detection of non-palpable undescended testis (NPUT) and to compare DW-MRI results with surgical findings. This descriptive, cross-sectional study was carried out at Dow Institute of Radiology, Dow University of Health Sciences. Karachi on a cohort of patients who underwent DW-MRI for suspected NPUT between 15th September, 2022 to 16th March, 2023. The study included 175 boys below the age of 16 years with history of clinically non-palpable testes. MRI scans were acquired using additional DWI sequence. Two radiology faculty of more than five years of experience independently evaluated the DW-MRI images for the presence or absence of testes in its normal anatomical position, abnormal location, side and size of testes. Out of the 175 patients, DW-MRI successfully detected 128 (73.1%) non-palpable undescended testes. Statistical analysis in clinically detected cases of showed sensitivity of NPTU 0.8%, specificity of 92.5%, diagnostic accuracy 94.29%, positive predictive value 97.7%, and negative predictive value 84.1% for localizing undescended testes with Diffusion Weighted DW-MRI taking surgical findings as gold standard. DWI sequences complement the conventional MRI, increasing its sensitivity and diagnostic accuracy. By facilitating early and accurate diagnosis, DW-MRI has the potential to streamline patient management, reduce unnecessary surgical exploration, and ultimately improve the long-term reproductive health and quality of life for individuals with undescended testes.

Harmonization of Structural Brain Connectivity through Distribution Matching.

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from two distinct datasets of OASIS-3 and ADNI-2, comparing its performance to the widely used ComBat method. Our approach is meant to align the statistical properties of connectivity data from these two datasets. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to ComBat. Qualitative assessments illustrate the desired distributional alignment of ADNI-2 with OASIS-3, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

Long-term brain structural and cognitive outcomes in a low-risk preterm-born sample

Prematurity has been related to altered brain structure and cognition, and so our aim was to describe them in the absence of major structural brain injury following low-risk preterm birth during adolescence and young adulthood. The sample consisted of 250 participants, 132 of whom were low-risk preterm (30–36 weeks’ gestational age) and 118 were full-term individuals (37–42 weeks’ gestational age), aged between 16 and 38 years old. All participants underwent an extensive neuropsychological assessment. T1- and diffusion-weighted MRI images of 33 low-risk preterm and 31 full-term young adults (20–32 years old) were analyzed. No differences were found in terms of general cognitive functioning score or current socioeconomic status; however, the low-risk preterm group obtained lower scores in phonetic and semantic fluencies, and theory of mind. Significant reductions were identified in the thalamus volume as well as thicker cortex in the inferior temporal gyrus in the low-risk preterm group. Low-risk preterm young adults evidenced greater regional AD and MD compared to the full-term sample; while low-risk preterm group showed lower mean NDI and ODI (FWE-corrected, p < 0.05). Being born preterm is associated with poorer performance in various cognitive domains (i.e., phonetic and semantic fluencies, and theory of mind) later in life, along with differences in normative structural brain development in inferior temporal gyrus and regional white matter microstructure.

Advanced diffusion-weighted MRI models for preoperative prediction of lymph node metastasis in resectable gastric cancer.

To investigate the potential of six advanced diffusion-weighted imaging (DWI) models for preoperative prediction of lymph node metastasis (LNM) in resectable gastric cancer (GC). Between Nov 2022 and Nov 2023, standard MRI scans were prospectively performed in consecutive patients with endoscopic pathology-confirmed gastric adenocarcinoma who were referred for direct radical gastrectomy. Six DWI models, including fractional order calculus (FROC), continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), the mono-exponential model (MEM) and the stretched exponential model (SEM) were computed. Surgical pathologic diagnosis of LNM was the reference standard, and patients were classified into LNM-positive or LNM-negative groups accordingly. The morphological features and quantitative parameters of the DWI models in different LNM categories were analyzed and compared. Multivariable logistic regression was used to screen significant predictors. Receiver-operating characteristic curves and the area under the curve (AUC) were plotted to evaluate the performances, the Delong test was performed to compare the AUCs. In the LNM-positive group, tumor thickness and kurtosis (DKI_K) were significantly higher, while anomalous diffusion coefficient (CTRW_D), diffusivity (DKI_D), diffusion coefficient (FROC_D), pseudodiffusion coefficient (IVIM_D*), perfusion fraction (IVIM_f), and ADC were lower compared to the LNM-negative group. Clinical tumor staging (cT) and CTRW_D were independent predictors. Their combination demonstrated a superior AUC of 0.930, significantly higher than that of individual parameters. Tumor thickness, DKI_K, CTRW_D, DKI_D, FROC_D, IVIM_D*, IVIM_f and ADC were associated with LNM status. The combination of independent predictors of cT and CTRW_D further enhanced the performance.

Light-weight neural network for intra-voxel structure analysis.

We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in diffusion-weighted MRI analysis for brain connectivity and development studies. The network architecture, called the Local Neighborhood Neural Network, is designed to use the spatial correlations of neighboring voxels for an enhanced inference while reducing parameter overhead. Our model exploits these relationships to improve the analysis of complex structures and noisy data environments. We adopt a self-supervised approach to address the lack of ground truth data, generating signals of voxel neighborhoods to integrate the training set. This eliminates the need for manual annotations and facilitates training under realistic conditions. Comparative analyses show that our method outperforms the constrained spherical deconvolution (CSD) method in quantitative and qualitative validations. Using phantom images that mimic in vivo data, our approach improves angular error, volume fraction estimation accuracy, and success rate. Furthermore, a qualitative comparison of the results in actual data shows a better spatial consistency of the proposed method in areas of real brain images. This approach demonstrates enhanced intra-voxel structure analysis capabilities and holds promise for broader application in various imaging scenarios.

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13). The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2. The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3–6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems.

APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges

Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density.

Structure-function coupling in highly sampled individual brains.

Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78min of diffusion-weighted MRI for SC and 360min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.

Feasibility study of computed high b-value diffusion-weighted magnetic resonance imaging for pediatric posterior fossa tumors.

To evaluate the diagnostic efficacy of computed diffusion-weighted imaging (DWI) in pediatric posterior fossa tumors generated using high b-values. We retrospectively performed our study on 32 pediatric patients who had undergone brain magnetic resonance imaging for a posterior fossa tumor between January 2016 and January 2022. The DWIs were evaluated for each patient by two blinded radiologists. The computed DWI (cDWI) was mathematically derived using a mono-exponential model from images with b = 0 and 1,000 s/mm2 and high b-values of 1,500, 2,000, 3,000, and 5,000 s/mm2. The posterior fossa tumors were divided into two groups, low grade and high grade, and the tumor/thalamus signal intensity (SI) ratios were compared. The Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to compare the diagnostic performance of the acquired DWI (DWI1000), apparent diffusion coefficient (ADC)1000 maps, and cDWI (cDWI1500, cDWI2000, cDWI3000, and cDWI5000). The comparison of the two tumor groups revealed that the tumor/thalamus SI ratio on the DWI1000 and cDWI (cDWI1500, cDWI2000, cDWI3000, and cDWI5000) was statistically significantly higher in high-grade tumors (P < 0.001). In the ROC curve analysis, higher sensitivity and specificity were detected in the cDWI1500, cDWI2000, cDWI3000, and ADC1000 maps (100%, 90.90%) compared with the DWI1000 (80%, 81.80%). cDWI3000 had the highest area under the curve (AUC) value compared with other parameters (AUC: 0.976). cDWI generated using high b-values was successful in differentiating between low-grade and high-grade posterior fossa tumors without increasing imaging time. cDWI created using high b-values can provide additional information about tumor grade in pediatric posterior fossa tumors without requiring additional imaging time.

Imaging of Ulcerative Colitis: The Role of Diffusion-Weighted Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) has emerged as a promising and appealing alternative to endoscopy in the objective assessment of patients with inflammatory bowel disease (IBD). Diffusion-weighted imaging (DWI) is a specialized imaging technique that enables the mapping of water molecule diffusion within biological tissues, eliminating the need for intravenous gadolinium contrast injection. It is expanding the capability of traditional MRI sequences in Ulcerative Colitis (UC). Recently, there has been growing interest in the application of intravoxel incoherent motion (IVIM) imaging in the field of IBD. This technique combines diffusion and perfusion information, making it a valuable tool for assessing IBD treatment response. Previous studies have extensively studied the use of DWI techniques for evaluating the severity of activity in IBD. However, the majority of these studies have primarily focused on Crohn's disease (CD), with only a limited number of reports specifically examining UC. Therefore, this review briefly introduces the basics of DWI and IVIM imaging and conducts a review of relevant studies that have investigated its application in UC to show whether these techniques are useful techniques for evaluating patients with UC in terms of detection, characterization, and quantification of disease activity. Through the extensive literature survey, most of these studies indicate that DWI proves valuable in the differential diagnosis of UC and could be used as an effective modality for staging UC.

Encephalopathy with a reversible lesion in the splenium of the corpus callosum in adults

Background &amp; Objectives: Mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) is a clinical and radiological syndrome with a lesion in splenium of the corpus callosum. This lesion is typically detected in the corpus callosum with magnetic resonance imaging (MRI) and usually disappears within a few weeks. The clinical and radiological features of MERS were evaluated for this study. Methods: Twelve patients were included in the study. They were treated between 2018 and 2021 years. The patients’ demographic and clinical features, laboratory data, treatment and prognoses were documented. Results: There were 7 male and 5 female patients in the series. The mean age was 37.92 ± 15.32 (19–62) years. There was history of upper respiratory tract infection in 7 patients, visual loss episodes in 3 patients, epileptic seizure in 2 patients, arthroplasty in 1 patient, cardiac arrhythmia in 1 patient, and COVID-19 (SARS-CoV-2) infection in 2 patients. Focal lesion was detected in the splenium of the corpus callosum with diffusion-weighted MRI which resolved with repeat diffusion MRI and clinical symptoms improved. Conclusions: Axial diffusion-weighted MRI images show typical splenium ovoid lesion of corpus callosum in patients with MERS. Patients with MERS have a good prognosis. It is important to consider MERS as one of the differential diagnoses in adult patients with fever and cognitive impairment.

Assessment of Tubo-Ovarian Abscess Using Diffusion-Weighted Magnetic Resonance Imaging

Aim: To evaluate systematically the magnetic resonance imaging (MRI) features of tubo-ovarian abscess (TOA), the efficacy of diffusion-weighted imaging (DWI) in the assessment of TOA, and the differentiation of TOA from ovarian masses. Material and methods: PubMed/MEDLINE, Web of Science, and ResearchGate were searched with keywords: Tubo-ovarian abscess and Diffusion-weighted magnetic resonance imaging. No restrictions regarding the language or publication year were applied. Inclusion criteria regarded research papers evaluating the role of MRI and DWI in the assessment of TOA, and the differentiation of TOA from ovarian tumours. Studies appearing to meet inclusion criteria were reviewed in full. Results: A total of 14 studies were included. TOA is usually presented as a multilocular, cystic, pelvic mass with a heterogeneously high signal on T2-weighted (T2W) and low signal on T1-weighted (T1W) images with the “penumbra sign” on T1W images. Following contrast administration, septal and thick wall rim enhancement could be visualized. TOA usually demonstrated hyperintensity on DWI and hypointensity on ADC maps with lower ADC values of cystic, and higher ADC values of solid components compared to ovarian malignancy. TOA was smaller in size, and invasion into adjacent organs and tubal dilatation were more frequent in TOA than in ovarian neoplasms. In comparison to other methods, DWI possessed the highest sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in differentiating TOA from adnexal tumours. Conclusions: The addition of DWI with ADC values improves the detection, characterization, and overall diagnostic accuracy of TOA and its distinction from ovarian malignancy. The combination of DWI with MRI in the assessment of TOA is obligatory.

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with conventional MRI for the detection of skull-base invasion in nasopharyngeal carcinoma: comparison with 18F-sodium fluoride (18F-NaF) positron emission tomography/computed tomography (PET/CT).

The extent of skull base invasion (SBI) in nasopharyngeal carcinoma (NPC) directly impacts tumor staging, treatment strategies, and prognosis assessment for NPC patients, emphasizing the critical need for prompt diagnosis and precise assessment of invasion. Thus, we aimed to integrate the advantages of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and conventional magnetic resonance imaging (cMRI), and assess their combined diagnostic efficacy versus that of 18F-sodium fluoride (18F-NaF) positron emission tomography/computed tomography (PET/CT) for detecting SBI in NPC patients. The study prospectively and randomly recruited 62 patients newly diagnosed with NPC by pathological biopsy at the Cancer Center of Affiliated Hospital of Guangdong Medical University from January 2021 to September 2022. All patients underwent baseline cMRI, IVIM-DWI, and PET/CT scans. The IVIM-DWI analysis included 3 primary parameters: true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f). SBI was defined as the involvement of any substructure confirmed by follow-up MRI and clinical symptoms. Inter-observer agreement was evaluated utilizing the intraclass correlation coefficients (ICC) and kappa coefficients. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of cMRI, IVIM-DWI plus cMRI, and PET/CT. DeLong test was used to compare the areas under the curve (AUC) of the 3 modalities. Excellent inter-observer reliability was observed (range, 0.841-0.946). Among the IVIM-DWI parameters, D* + f demonstrated comparable accuracy to D + D* + f (AUC 0.906 vs. 0.904; sensitivity 88.9% vs. 89.8%; specificity 92.3% vs. 91.0%). IVIM-DWI plus cMRI yielded an overall AUC of 0.947, sensitivity of 92.6%, and specificity of 96.8%, surpassing cMRI alone with an AUC of 0.914 (P=0.025), sensitivity of 91.2%, and specificity of 91.7%, as well as 18F-NaF PET/CT with an AUC of 0.852 (P<0.001), sensitivity of 80.1%, and specificity of 90.4%. In detecting substructures of SBI, IVIM-DWI plus cMRI showed superior performance compared to 18F-NaF PET/CT within the petrous part of the temporal bone (AUC 0.968 vs. 0.871, P=0.011; sensitivity 93.5% vs. 87.1%, specificity 100% vs. 87.1%), pterygopalatine fossa (AUC 0.935 vs. 0.831, P=0.032; sensitivity 93.9% vs. 69.7%, specificity 93.1% vs. 96.6%), and foramen ovale (AUC 0.885 vs. 0.710, P=0.019; sensitivity 76.9% vs. 61.5%, specificity 100% vs. 80.6%). IVIM-DWI plus cMRI can accurately detect SBI and the substructures in NPC, providing a valuable reference for personalized treatment strategies and precise prognosis assessment.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundlewise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxelwise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered. NeSH, a spatial and angular resampling method, was also effective and has generalizable framework not reliant co-registration. Our code is available at &lt;a href='https://github.com/nancynewlin-masi/QuantConn/'&gt;https://github.com/nancynewlin-masi/QuantConn/&lt;/a&gt;

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities

Abstract We examined how the connectivity structure of left thalamus, especially thalamo-cortical connections, are reflected in children’s reading performance. Diffusion-weighted MRI at 3T and a series of reading measures were collected from 64 children ages from 8 to 14 years, 33 girls and 31 boys. The topological properties of the left thalamus were computed based on the whole-brain white matter network and a hub-attached reading network, and correlated with reading scores. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Two efficiency measures were negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of efficiency scores. A validation analysis using an independent dataset replicated most of the findings, except for the correlation between local efficiency of the thalamus and rapid automatized naming. Our result highlights the role of the left thalamus and thalamo-cortical network in understanding the neurocognitive bases of skilled reading and dyslexia in children.

Comparison of the Effectiveness of Computed Tomography and Magnetic Resonance Imaging Techniques in Patient Groups Aged under and over 65 Years and Diagnosed with Ischemic Stroke in the Emergency Department

Objectives: Stroke is a condition with high morbidity and mortality. This study aims to investigate whether the effectiveness of Computed Tomography (CT) and diffusion-weighted Magnetic Resonance Imaging (MRI), the techniques that have a significant place in the diagnosis of ischemic stroke, the most common form of stroke, are affected by the physiological changes of advanced age. Methods: A total of 436 patients were included in the study. The study population was divided into two groups depending on age: those above 65 and those under 65. Medical files, both the emergency department and clinical ward files, of the patients who were admitted to the emergency department in nine months and admitted to the neurology clinic with the diagnosis of ischemic stroke were retrospectively analyzed. The time from admission to imaging was determined depending on patient files and the Hospital Management Information System (HBYS). The CT and MRI reports interpreted by radiologists were also reviewed. While recording the data, the presence of a lesion, its direction, and localization were also noted. Results: CT positivity was 21.3%, and the positivity of diffusion-weighted imaging was 82.1% in the study population. The time was shorter in the group of patients with positive CT results than in the group with negative CT results. In subjects under 65, the time between onset and imaging was shorter in the group with positive CT results than in the group with negative CT results. In subjects over 65, the time with positive CT results was not different from the group with negative CT results. It was determined that the mean time was shorter in the group with positive MRI results than in the group with negative results. In both the groups under the age of 65 and over the age of 65, the time interval was shorter in the patients with positive MRI results compared to those with negative MRI results. Conclusions: Regardless of the positivity or negativity of CT and MRI results, the mean time from symptom onset to imaging was shorter in the group under 65 years of age compared to the group over 65 years. Aging prolongs the time to admission and the neuroradiological response of geriatric patients.

Diffusion MRI in prostate cancer with ultra-strong whole-body gradients.

Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4ms/μm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.