Multiple Magnetic Resonance Imaging Modalities Research Articles

Sex differences in the pleiotropy of hearing difficulty with imaging-derived phenotypes: a brain-wide investigation.

Hearing difficulty (HD) is one of the major health burdens in older adults. While aging-related changes in the peripheral auditory system play an important role, genetic variation associated with brain structure and function could also be involved in HD predisposition. We analyzed a large-scale HD genome-wide association study (GWAS; Ntotal = 501,825, 56% females) and GWAS data related to 3,935 brain imaging-derived phenotypes (IDPs) assessed in up to 33,224 individuals (52% females) using multiple magnetic resonance imaging modalities. To investigate HD pleiotropy with brain structure and function, we conducted genetic correlation, latent causal variable, Mendelian randomization, and multivariable generalized linear regression analyses. Additionally, we performed local genetic correlation and multi-trait colocalization analyses to identify genomic regions and loci implicated in the pleiotropic mechanisms shared between HD and brain IDPs. We observed a widespread genetic correlation of HD with 120 IDPs in females, 89 IDPs in males, and 171 IDPs in the sex-combined analysis. The latent causal variable analysis showed that some of these genetic correlations could be due to cause-effect relationships. For seven correlations, the causal effects were also confirmed by the Mendelian randomization approach: vessel volume→HD in the sex-combined analysis; hippocampus volume→HD, cerebellum grey matter volume→HD, primary visual cortex volume→HD, and HD→fluctuation amplitudes of node 46 in resting-state functional MRI dimensionality 100 in females; global mean thickness→HD and HD→mean orientation dispersion index in superior corona radiata in males. The local genetic correlation analysis identified 13 pleiotropic regions between HD and these seven IDPs. We also observed a colocalization signal for the rs13026575 variant between HD, primary visual cortex volume, and SPTBN1 transcriptomic regulation in females. Brain structure and function may have a role in the sex differences in HD predisposition via possible cause-effect relationships and shared regulatory mechanisms.

Generalized Knowledge Distillation for Unimodal Glioma Segmentation from Multimodal Models

Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods.

Multi-Modality Reconstruction Attention and Difference Enhancement Network for Brain MRI Image Segmentation

An important prerequisite for brain disease diagnosis is to segment brain tissues of Magnetic Resonance Imaging (MRI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). To improve performance, we propose a Multi-modality Reconstruction Attention and Difference Enhancement Network (MRADE-Net). We first stack three inputs of multiple MRI modalities along axial, sagittal, and coronal axes to form three enlarged volumes. Then we adopt global average pooling along each axis, fully connected layers, and activation functions to produce three orthogonal 2D coefficient maps, which are used to reconstruct a 3D attention map for weighting the three inputs. These weighted inputs are added together to generate a feature map of Multi-modality Reconstruction Attention (MRA). Similarly, we present Single-modality Reconstruction Attention (SRA) for improving feature representation abilities in middle stages. In addition, the difference of encoding features between adjacent layers is used to compensate the loss of spatial information caused by down-sampling. Experimental results show that the proposed approach is more effective than the existing state-of-the-art segmentation methods and its performance is verified on several datasets.

Selecting Patients with Favorable Risk, Grade Group 2 Prostate Cancer for Active Surveillance-Does Magnetic Resonance Imaging Have a Role?

The National Comprehensive Cancer Network® recommends that selected men with grade group 2 prostate cancer be considered for active surveillance. However, selecting which patients with grade group 2 disease can be safely managed by active surveillance remains controversial. The aim of this study was to evaluate the association of multiparametric magnetic resonance imaging with adverse pathology in the radical prostatectomy specimen of men with favorable risk grade group 2 prostate cancer, which could help select patients for active surveillance. We retrospectively analyzed a cohort of patients with favorable grade group 2 disease who underwent radical prostatectomy between 2010 and 2019. Preoperative multiparametric magnetic resonance imaging was scored as negative (no identifiable lesion), positive (identifiable lesion) or equivocal. We defined a multivariable logistic regression model with multiparametric magnetic resonance imaging score as the predictor and adverse pathology (up staging to T3a/b disease, upgrading to ≥grade group 3 or lymph node invasion) as the outcome, adjusting for preoperative prostate specific antigen, biopsy Gleason grade, clinical stage, and number of negative and positive prostate biopsy cores. Secondary outcomes of biochemical recurrence, grade group upgrading alone and the added value of incorporating multiparametric magnetic resonance imaging data into the nomogram were also investigated. We identified 1,117 patients with favorable risk grade group 2 disease who underwent radical prostatectomy. Positive multiparametric magnetic resonance imaging was associated with higher rates of adverse pathology (OR 2.55, 95% CI 1.75-3.40, p <0.0001) and upgrading (OR 3.89, 95% CI 2.00-7.56, p <0.0001). However, as our study included only grade group 2 patients who underwent radical prostatectomy, our cohort may represent a higher risk group than grade group 2 patients as a whole. Adding multiparametric magnetic resonance imaging results to a standard prediction model led to higher net benefit on decision curve analysis. An identifiable lesion on multiparametric magnetic resonance imaging was associated with an increased risk of aggressive pathological features in the radical prostatectomy specimen of patients with favorable risk grade group 2 prostate cancer who were potential active surveillance candidates. This information could be used to inform biopsy strategy, counsel patients on treatment options and guide strategies for those on active surveillance. Combining multiple magnetic resonance imaging modalities (multiparametric magnetic resonance imaging) provides a more accurate prediction of the risk presented by prostate cancer than current prediction methods. In this study, positive magnetic resonance imaging results approximately doubled the chances that a patient with favorable risk prostate cancer would be found to have adverse pathology when their prostate was removed. Thus, multiparametric magnetic resonance imaging could help select patients with favorable risk cancer who may be good candidates for active surveillance, and help guide biopsy and surveillance strategies for such patients.

Acute and sub-acute stroke lesion segmentation from multimodal MRI

Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment.Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing.Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance.Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.

MRLN: Multi-Task Relational Learning Network for MRI Vertebral Localization, Identification, and Segmentation.

Magnetic resonance imaging (MRI) vertebral localization, identification, and segmentation are important steps in the automatic analysis of spines. Due to the similar appearances of vertebrae, the accurate segmentation, localization, and identification of vertebrae remain challenging. Previous methods solved the three tasks independently, ignoring the intrinsic correlation among them. In this paper, we propose a multi-task relational learning network (MRLN) that utilizes both the relationships between vertebrae and the relevance of the three tasks. A dilation convolution group is used to expand the receptive field, and LSTM(Long Short-Term Memory) to learn the prior knowledge of the order relationship between the vertebral bodies. We introduce a co-attention module to learn the correlation information, localization-guided segmentation attention(LGSA) and segmentation-guided localization attention(SGLA), in the decoder stage of segmentation and localization tasks. Learning two tasks simultaneously as well as the correlation between tasks can not only avoid the overfitting of a single task but also correct each other. To avoids the cumbersome weight adjustment for different tasks loss functions, we formulated a novel XOR loss that provides a direct evaluation criterion for the localization relationship of the semantic location regression and semantic segmentation. This method was evaluated on a dataset which includes multiple MRI modalities (T1 and T2), various fields of view. Experimental results demonstrate that both of the co-attention and XOR loss work outperforms the most recent state of art.

Dissecting the pathobiology of altered MRI signal in amyotrophic lateral sclerosis: A post mortem whole brain sampling strategy for the integration of ultra-high-field MRI and quantitative neuropathology

BackgroundAmyotrophic lateral sclerosis (ALS) is a clinically and histopathologically heterogeneous neurodegenerative disorder, in which therapy is hindered by the rapid progression of disease and lack of biomarkers. Magnetic resonance imaging (MRI) has demonstrated its potential for detecting the pathological signature and tracking disease progression in ALS. However, the microstructural and molecular pathological substrate is poorly understood and generally defined histologically. One route to understanding and validating the pathophysiological correlates of MRI signal changes in ALS is to directly compare MRI to histology in post mortem human brains.ResultsThe article delineates a universal whole brain sampling strategy of pathologically relevant grey matter (cortical and subcortical) and white matter tracts of interest suitable for histological evaluation and direct correlation with MRI. A standardised systematic sampling strategy that was compatible with co-registration of images across modalities was established for regions representing phosphorylated 43-kDa TAR DNA-binding protein (pTDP-43) patterns that were topographically recognisable with defined neuroanatomical landmarks. Moreover, tractography-guided sampling facilitated accurate delineation of white matter tracts of interest. A digital photography pipeline at various stages of sampling and histological processing was established to account for structural deformations that might impact alignment and registration of histological images to MRI volumes. Combined with quantitative digital histology image analysis, the proposed sampling strategy is suitable for routine implementation in a high-throughput manner for acquisition of large-scale histology datasets. Proof of concept was determined in the spinal cord of an ALS patient where multiple MRI modalities (T1, T2, FA and MD) demonstrated sensitivity to axonal degeneration and associated heightened inflammatory changes in the lateral corticospinal tract. Furthermore, qualitative comparison of R2* and susceptibility maps in the motor cortex of 2 ALS patients demonstrated varying degrees of hyperintense signal changes compared to a control. Upon histological evaluation of the same region, intensity of signal changes in both modalities appeared to correspond primarily to the degree of microglial activation.ConclusionThe proposed post mortem whole brain sampling methodology enables the accurate intraindividual study of pathological propagation and comparison with quantitative MRI data, to more fully understand the relationship of imaging signal changes with underlying pathophysiology in ALS.

RNaseT2 knockout rats exhibit hippocampal neuropathology and deficits in memory.

ABSTRACTRNASET2 deficiency in humans is associated with infant cystic leukoencephalopathy, which causes psychomotor impairment, spasticity and epilepsy. A zebrafish mutant model suggests that loss of RNASET2 function leads to neurodegeneration due to the accumulation of non-degraded RNA in the lysosomes. The goal of this study was to characterize the first rodent model of RNASET2 deficiency. The brains of 3- and 12-month-old RNaseT2 knockout rats were studied using multiple magnetic resonance imaging modalities and behavioral tests. While T1- and T2-weighted images of RNaseT2 knockout rats exhibited no evidence of cystic lesions, the prefrontal cortex and hippocampal complex were enlarged in knockout animals. Diffusion-weighted imaging showed altered anisotropy and putative gray matter changes in the hippocampal complex of the RNaseT2 knockout rats. Immunohistochemistry for glial fibrillary acidic protein (GFAP) showed the presence of hippocampal neuroinflammation. Decreased levels of lysosome-associated membrane protein 2 (LAMP2) and elevated acid phosphatase and β-N-acetylglucosaminidase (NAG) activities indicated that the RNASET2 knockout rats likely had altered lysosomal function and potential defects in autophagy. Object recognition tests confirmed that RNaseT2 knockout rats exhibited memory deficits. However, the Barnes maze, and balance beam and rotarod tests indicated there were no differences in spatial memory or motor impairments, respectively. Overall, patients with RNASET2 deficiency exhibited a more severe neurodegeneration phenotype than was observed in the RNaseT2 knockout rats. However, the vulnerability of the knockout rat hippocampus as evidenced by neuroinflammation, altered lysosomal function and cognitive defects indicates that this is still a useful in vivo model to study RNASET2 function.

Assessment of consistency of multiple magnetic resonance imaging modalities in measurement of gross tumor volume for nasopharyngeal carcinoma

Objective To investigate the consistency of multiple magnetic resonance imaging (MRI) modalities in measurement of gross tumor volume (GTV) for nasopharyngeal carcinoma. Methods A retrospective analysis was performed among 45 patients who were newly diagnosed and pathologically confirmed with nasopharyngeal carcinoma. All patients underwent plain, enhanced, and diffusion-weighted MRI scans. Primary tumor was manually delineated on enhanced axial T1WI, axial T2WI, and apparent diffusion coefficient (ADC) maps. GTV was calculated by the sum of areas. The obtained GTV was divided into T1+ C group, T2WI group, and ADC group. One-way analysis of variance was used to evaluate the differences between the mean values from the three groups. The Bland-Altman method was used to evaluate the consistency of different imaging modalities in GTV measurement using the mean difference between the two groups and 95% distribution of the difference. Results There was no significant difference in mean GTV between the T1+ C group, the T2WI group, and the ADC group (20.8±18.1) vs. (21.5±17.2) vs. (24.4±20.8) cm3(P=0.617). The mean differences and 95% limits of agreement between the T2WI group and the T1+ C group, between the ADC group and the T2WI group, and between the ADC group and the T1+ C group were 0.74, 2.94, and 3.68 cm3 and (-5.97-7.44), (-8.25-13.69), and (-3.34-10.70) cm3, respectively. Conclusions The multiple MRI modalities have poor consistency in measurement of GTV for nasopharyngeal carcinoma. Therefore, they cannot replace each other. Enhanced T1WI combined with T2WI and DWMRI can determine a relatively accurate range of target volume for nasopharyngeal carcinoma. Key words: Multimodality magnetic resonance; Gross tumor volumel; Consistency

Advanced structural multimodal imaging of a patient with subcortical band heterotopia.

SummarySubcortical band heterotopia (SBH) is a disorder of neuronal migration most commonly due to mutations of the Doublecortin (DCX) gene. A range of phenotypes is seen, with most patients having some degree of epilepsy and intellectual disability. Advanced diffusion and structural magnetic resonance imaging (MRI) sequences may be useful in identifying heterotopias and dysplasias of different sizes in drug‐resistant epilepsy. We describe a patient with SBH and drug‐resistant epilepsy and investigate neurite density, neurite dispersion, and diffusion parameters as compared to a healthy control through the use of multiple advanced MRI modalities. Neurite density and dispersion in heterotopia was found to be more similar to white matter than to gray matter. Neurite density and dispersion maps obtained using diffusion imaging may be able to better characterize different subtypes of heterotopia.

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey.

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Neurosurgery planning in rodents using a magnetic resonance imaging assisted framework to target experimentally defined networks

Background and objectiveMeaningful targeting of brain structures is required in a number of experimental designs in neuroscience. Current technological developments as high density electrode arrays for parallel electrophysiological recordings and optogenetic tools that allow fine control of activity in specific cell populations provide powerful tools to investigate brain physio-pathology. However, to extract the maximum yield from these fine developments, increased precision, reproducibility and cost-efficiency in experimental procedures is also required. MethodsWe introduce here a framework based on magnetic resonance imaging (MRI) and digitized brain atlases to produce customizable 3D-environments for brain navigation. It allows the use of individualized anatomical and/or functional information from multiple MRI modalities to assist experimental neurosurgery planning and in vivo tissue processing. ResultsAs a proof of concept we show three examples of experimental designs facilitated by the presented framework, with extraordinary applicability in neuroscience. ConclusionsThe obtained results illustrate its feasibility for identifying and selecting functionally and/or anatomically connected neuronal population in vivo and directing electrode implantations to targeted nodes in the intricate system of brain networks.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects.To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]).OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.