Frontiers in neuroimaging最新文献

{"title":"Survey of neuroimaging in neurological surgery, current state, and emerging research","authors":"Saramati Narasimhan, Hernán F. J. González","doi":"10.3389/fnimg.2023.1022680","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1022680","url":null,"abstract":"Neuroimaging plays a crucial role in enabling a surgeon's proficiency and achieving optimal outcomes across various subspecialties of neurosurgery. Since Wilhelm Roentgen's groundbreaking discovery of the radiograph in 1895, imaging capabilities have advanced astronomically, significantly benefiting the field of neurosurgery. In this review, we aim to provide a concise overview of neuroimaging in four specific subspecialties: neuro-oncology, cerebrovascular, spine, and functional neurosurgery. Although the diseases and procedures mentioned are not exhaustive, they are illustrative examples of how neuroimaging has contributed to advancements in neurosurgery. Our intention is to emphasize the critical role of neuroimaging in pre-operative, intra-operative, and post-operative settings, while also highlighting its potential to drive research to further enhance existing neurosurgical technologies and ultimately better patient outcomes.","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41445535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translocator protein PET imaging in temporal lobe epilepsy: A reliable test-retest study using asymmetry index.","authors":"Mohammad Mahmud, Charles Wade, Sarah Jawad, Zaeem Hadi, Christian Otoul, Rafal M Kaminski, Pierandrea Muglia, Irena Kadiu, Eugenii Rabiner, Paul Maguire, David R Owen, Michael R Johnson","doi":"10.3389/fnimg.2023.1142463","DOIUrl":"10.3389/fnimg.2023.1142463","url":null,"abstract":"<p><strong>Objective: </strong>Translocator protein (TSPO) targeting positron emission tomography (PET) imaging radioligands have potential utility in epilepsy to assess the efficacy of novel therapeutics for targeting neuroinflammation. However, previous studies in healthy volunteers have indicated limited test-retest reliability of TSPO ligands. Here, we examine test-retest measures using TSPO PET imaging in subjects with epilepsy and healthy controls, to explore whether this biomarker can be used as an endpoint in clinical trials for epilepsy.</p><p><strong>Methods: </strong>Five subjects with epilepsy and confirmed mesial temporal lobe sclerosis (mean age 36 years, 3 men) were scanned twice-on average 8 weeks apart-using a second generation TSPO targeting radioligand, [¹¹C]PBR28. We evaluated the test-retest reliability of the volume of distribution and derived hemispheric asymmetry index of [¹¹C]PBR28 binding in these subjects and compared the results with 8 (mean age 45, 6 men) previously studied healthy volunteers.</p><p><strong>Results: </strong>The mean (± SD) of the volume of distribution (<i>V</i>_T), of all subjects, in patients living with epilepsy for both test and retest scans on all regions of interest (ROI) is 4.49 ± 1.54 vs. 5.89 ± 1.23 in healthy volunteers. The bias between test and retest in an asymmetry index as a percentage was small (-1.5%), and reliability is demonstrated here with Bland-Altman Plots (test mean 1.062, retest mean 2.56). In subjects with epilepsy, <i>V</i>_T of [¹¹C]PBR28 is higher in the (ipsilateral) hippocampal region where sclerosis is present than in the contralateral region.</p><p><strong>Conclusion: </strong>When using TSPO PET in patients with epilepsy with hippocampal sclerosis (HS), an inter-hemispheric asymmetry index in the hippocampus is a measure with good test-retest reliability. We provide estimates of test-retest variability that may be useful for estimating power where group change in <i>V</i>_T represents the clinical outcome.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1142463"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9965710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.","authors":"Keerthi Sravan Ravi, Gautham Nandakumar, Nikita Thomas, Mason Lim, Enlin Qian, Marina Manso Jimeno, Pavan Poojar, Zhezhen Jin, Patrick Quarterman, Girish Srinivasan, Maggie Fung, John Thomas Vaughan, Sairam Geethanath","doi":"10.3389/fnimg.2023.1072759","DOIUrl":"10.3389/fnimg.2023.1072759","url":null,"abstract":"<p><p>Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI <i>via</i> intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging <i>via</i> automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1072759"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10294765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Manual lesion segmentations for traumatic brain injury characterization.","authors":"Alexis Bennett, Rachael Garner, Michael D Morris, Marianna La Rocca, Giuseppe Barisano, Ruskin Cua, Jordan Loon, Celina Alba, Patrick Carbone, Shawn Gao, Asenat Pantoja, Azrin Khan, Noor Nouaili, Paul Vespa, Arthur W Toga, Dominique Duncan","doi":"10.3389/fnimg.2023.1068591","DOIUrl":"10.3389/fnimg.2023.1068591","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis <i>via</i> inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1068591"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10412940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality control procedures and metrics for resting-state functional MRI.","authors":"Rasmus M Birn","doi":"10.3389/fnimg.2023.1072927","DOIUrl":"10.3389/fnimg.2023.1072927","url":null,"abstract":"<p><p>The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1072927"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing automated white matter hyperintensity segmentation in individuals with stroke.","authors":"Jennifer K Ferris, Bethany P Lo, Mohamed Salah Khlif, Amy Brodtmann, Lara A Boyd, Sook-Lei Liew","doi":"10.3389/fnimg.2023.1099301","DOIUrl":"10.3389/fnimg.2023.1099301","url":null,"abstract":"<p><p>White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1099301"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9968180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia.","authors":"Behnam Kazemivash, Theo G M van Erp, Peter Kochunov, Vince D Calhoun","doi":"10.3389/fnimg.2023.1097523","DOIUrl":"10.3389/fnimg.2023.1097523","url":null,"abstract":"<p><p>Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (<i>N</i> = 708) and controls (<i>N</i> = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1097523"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A structural connectivity atlas of limbic brainstem nuclei.","authors":"Simon Levinson, Michelle Miller, Ahmed Iftekhar, Monica Justo, Daniel Arriola, Wenxin Wei, Saman Hazany, Josue M Avecillas-Chasin, Taylor P Kuhn, Andreas Horn, Ausaf A Bari","doi":"10.3389/fnimg.2022.1009399","DOIUrl":"10.3389/fnimg.2022.1009399","url":null,"abstract":"<p><strong>Background: </strong>Understanding the structural connectivity of key brainstem nuclei with limbic cortical regions is essential to the development of therapeutic neuromodulation for depression, chronic pain, addiction, anxiety and movement disorders. Several brainstem nuclei have been identified as the primary central nervous system (CNS) source of important monoaminergic ascending fibers including the noradrenergic locus coeruleus, serotonergic dorsal raphe nucleus, and dopaminergic ventral tegmental area. However, due to practical challenges to their study, there is limited data regarding their <i>in vivo</i> anatomic connectivity in humans.</p><p><strong>Objective: </strong>To evaluate the structural connectivity of the following brainstem nuclei with limbic cortical areas: locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius. Additionally, to develop a group average atlas of these limbic brainstem structures to facilitate future analyses.</p><p><strong>Methods: </strong>Each nucleus was manually masked from 197 Human Connectome Project (HCP) structural MRI images using FSL software. Probabilistic tractography was performed using FSL's FMRIB Diffusion Toolbox. Connectivity with limbic cortical regions was calculated and compared between brainstem nuclei. Results were aggregated to produce a freely available MNI structural atlas of limbic brainstem structures.</p><p><strong>Results: </strong>A general trend was observed for a high probability of connectivity to the amygdala, hippocampus and DLPFC with relatively lower connectivity to the orbitofrontal cortex, NAc, hippocampus and insula. The locus coeruleus and nucleus tractus solitarius demonstrated significantly greater connectivity to the DLPFC than amygdala while the periaqueductal grey, dorsal raphe nucleus, and ventral tegmental area did not demonstrate a significant difference between these two structures.</p><p><strong>Conclusion: </strong>Monoaminergic and other modulatory nuclei in the brainstem project widely to cortical limbic regions. We describe the structural connectivity across the several key brainstem nuclei theorized to influence emotion, reward, and cognitive functions. An increased understanding of the anatomic basis of the brainstem's role in emotion and other reward-related processing will support targeted neuromodulatary therapies aimed at alleviating the symptoms of neuropsychiatric disorders.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"1009399"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9957248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB.","authors":"Xin Di, Bharat B Biswal","doi":"10.3389/fnimg.2022.1070151","DOIUrl":"10.3389/fnimg.2022.1070151","url":null,"abstract":"<p><p>Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"1070151"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A standardized protocol for manually segmenting stroke lesions on high-resolution T1-weighted MR images.","authors":"Bethany P Lo, Miranda R Donnelly, Giuseppe Barisano, Sook-Lei Liew","doi":"10.3389/fnimg.2022.1098604","DOIUrl":"10.3389/fnimg.2022.1098604","url":null,"abstract":"<p><p>Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions (\"tracers\") in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"1098604"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10412941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}