Pub Date : 2024-08-21DOI: 10.1101/2024.08.16.24312140
Christi A. Essex, Jenna L. Merenstein, Devon K. Overson, Trong-Kha Truong, David J. Madden, Mayan J. Bedggood, Helen Murray, Samantha J. Holdsworth, Ashley W. Stewart, Catherine Morgan, Richard L. M. Faull, Patria Hume, Alice Theadom, Mangor Pedersen
Evidence has linked head trauma to increased risk factors for neuropathology, including acute mechanical deformation of the cortical sulcal fundus and, later, perivascular accumulation of hyperphosphorylated tau (p-tau) adjacent to these spaces related to chronic traumatic encephalopathy (CTE). Despite this, little is known about microstructural abnormalities and cellular dyshomeostasis at the acute stage of mild traumatic brain injury (mTBI) in humans, particularly in the cortex. To address this gap in the literature, we designed the first architectonically-motivated quantitative susceptibility mapping (QSM) study to assess regional patterns of positive (iron-related) and negative (myelin-, calcium-, and protein-related) magnetic susceptibility in cortical regions of interest (ROI) following mTBI. Depth- and curvature-specific positive and negative QSM values were compared between 25 males with acute (< 14 days) sports-related mTBI (sr-mTBI) and 25 age-matched male controls across 34 cortical ROIs. Bilateral between-group analyses were conducted on specific ROI curvature bins (crown, bank, and fundus) as well as a combined curvature measure, across 21 cortical depths, for each ROI. Correlations between positive and negative susceptibility were analysed for age, brain injury severity, and the number of days since injury. We observed significant group differences in magnetic susceptibility for depth, curvature, and ROIs. Our results suggest a trauma-induced pattern of iron deposition preferential to superficial, perivascular-adjacent spaces in the sulci of the parahippocampal gyrus. Co-localised decreases in diamagnetism in the same region suggest dual pathology of neural substrates, the biological mechanisms behind which remain speculative. Significant correlations were found between magnetic susceptibility and age, both in ROIs and cortical depths distinct from those showing sr-mTBI-related differences. Little to no relationship was observed between magnetic susceptibility and subjective markers of injury or injury latency. The coherence between our findings and pathognomonic patterns of misfolded proteins in trauma-related neurodegeneration is interesting, which may have implications for the role of brain iron in microstructural cortical tissue damage after a mild brain injury. Further longitudinal research is needed to elucidate the long-term implications of our findings.
有证据表明，头部创伤与神经病理学风险因素的增加有关，包括皮质沟底的急性机械变形，以及随后与慢性创伤性脑病（CTE）相关的高磷酸化 tau（p-tau）在这些空间附近的血管周围积聚。尽管如此，人们对人类轻度创伤性脑损伤（mTBI）急性期的微结构异常和细胞失衡知之甚少，尤其是在大脑皮层。为了填补这一文献空白，我们设计了第一项以建筑学为动机的定量磁感应强度绘图（QSM）研究，以评估轻微脑损伤后皮层感兴趣区（ROI）中正磁感应强度（铁相关）和负磁感应强度（髓鞘、钙和蛋白质相关）的区域模式。25 名急性（14 天）运动相关 mTBI（sr-mTBI）男性患者和 25 名年龄匹配的男性对照组患者在 34 个皮层 ROI 中比较了特定深度和曲率的正负 QSM 值。对每个 ROI 的特定 ROI 曲度分段（冠状面、基底面和基底面）以及 21 个皮层深度的综合曲度测量进行了双侧组间分析。分析了年龄、脑损伤严重程度和受伤后天数与正负磁感应强度之间的相关性。我们观察到磁感应强度在深度、曲率和 ROI 方面存在明显的组间差异。我们的研究结果表明，创伤诱导的铁沉积模式偏好于海马旁回沟内浅表、血管周围邻近的空间。同一区域的二磁性同时下降，表明神经基质出现了双重病理变化，其背后的生物机制仍有待推测。在与 sr-mTBI 相关差异不同的区域和皮层深度中，磁感应强度与年龄之间存在显著相关性。磁感应强度与损伤的主观指标或损伤潜伏期之间几乎没有关系。我们的研究结果与创伤相关神经变性中错误折叠蛋白的病理模式之间的一致性非常有趣，这可能对轻度脑损伤后脑铁在皮质组织微结构损伤中的作用有影响。要阐明我们研究结果的长期影响，还需要进一步的纵向研究。
{"title":"Distribution of paramagnetic and diamagnetic cortical substrates following mild Traumatic Brain Injury: A depth- and curvature-based quantitative susceptibility mapping study","authors":"Christi A. Essex, Jenna L. Merenstein, Devon K. Overson, Trong-Kha Truong, David J. Madden, Mayan J. Bedggood, Helen Murray, Samantha J. Holdsworth, Ashley W. Stewart, Catherine Morgan, Richard L. M. Faull, Patria Hume, Alice Theadom, Mangor Pedersen","doi":"10.1101/2024.08.16.24312140","DOIUrl":"https://doi.org/10.1101/2024.08.16.24312140","url":null,"abstract":"Evidence has linked head trauma to increased risk factors for neuropathology, including acute mechanical deformation of the cortical sulcal fundus and, later, perivascular accumulation of hyperphosphorylated tau (p-tau) adjacent to these spaces related to chronic traumatic encephalopathy (CTE). Despite this, little is known about microstructural abnormalities and cellular dyshomeostasis at the acute stage of mild traumatic brain injury (mTBI) in humans, particularly in the cortex. To address this gap in the literature, we designed the first architectonically-motivated quantitative susceptibility mapping (QSM) study to assess regional patterns of positive (iron-related) and negative (myelin-, calcium-, and protein-related) magnetic susceptibility in cortical regions of interest (ROI) following mTBI. Depth- and curvature-specific positive and negative QSM values were compared between 25 males with acute (< 14 days) sports-related mTBI (sr-mTBI) and 25 age-matched male controls across 34 cortical ROIs. Bilateral between-group analyses were conducted on specific ROI curvature bins (crown, bank, and fundus) as well as a combined curvature measure, across 21 cortical depths, for each ROI. Correlations between positive and negative susceptibility were analysed for age, brain injury severity, and the number of days since injury. We observed significant group differences in magnetic susceptibility for depth, curvature, and ROIs. Our results suggest a trauma-induced pattern of iron deposition preferential to superficial, perivascular-adjacent spaces in the sulci of the parahippocampal gyrus. Co-localised decreases in diamagnetism in the same region suggest dual pathology of neural substrates, the biological mechanisms behind which remain speculative. Significant correlations were found between magnetic susceptibility and age, both in ROIs and cortical depths distinct from those showing sr-mTBI-related differences. Little to no relationship was observed between magnetic susceptibility and subjective markers of injury or injury latency. The coherence between our findings and pathognomonic patterns of misfolded proteins in trauma-related neurodegeneration is interesting, which may have implications for the role of brain iron in microstructural cortical tissue damage after a mild brain injury. Further longitudinal research is needed to elucidate the long-term implications of our findings.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181591","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}
Pub Date : 2024-08-20DOI: 10.1101/2024.08.15.24311978
Alexandra F Bonthrone, Daniel Cromb, Andrew Chew, Barat Gal-Er, Christopher Kelly, Shona Falconer, Tomoki Arichi, Kuberan Pushparajah, John Simpson, Mary A. Rutherford, Joseph V. Hajnal, Chiara Nosarti, A. David Edwards, Jonathan O'Muircheartaigh, Serena J. Counsell
Theoretically derived scaling laws capture the non-linear relationships between rapidly expanding brain volume and cortical gyrification across mammalian species and in adult humans. However, the preservation of these laws has not been comprehensively assessed in typical or pathological brain development. Here we assessed the scaling laws governing cortical thickness, surface area and cortical folding in the neonatal brain. We also assessed multivariate morphological terms that capture brain size, shape and folding processes. The sample consisted of 375 typically developing infants, 73 preterm infants and 107 infants with congenital heart disease (CHD) who underwent brain magnetic resonance imaging (MRI). Our results show that typically developing neonates and those with CHD follow the cortical folding scaling law obtained from mammalian brains, children and adults which captures the relationship between exposed surface area, total surface area and cortical thickness. Cortical folding scaling was not affected by gestational age at birth, postmenstrual age at scan, sex or multiple birth in these populations. CHD was characterized by a unique reduction in the multivariate morphological term capturing size, suggesting CHD affects cortical growth overall but not cortical folding processes. In contrast, preterm birth was characterized by altered cortical folding scaling and altered shape, suggesting the developmentally programmed processes of cortical folding are disrupted in this population. The degree of altered shape was associated with cognitive abilities in early childhood in preterm infants.
{"title":"Cortical scaling of the neonatal brain in typical and altered development","authors":"Alexandra F Bonthrone, Daniel Cromb, Andrew Chew, Barat Gal-Er, Christopher Kelly, Shona Falconer, Tomoki Arichi, Kuberan Pushparajah, John Simpson, Mary A. Rutherford, Joseph V. Hajnal, Chiara Nosarti, A. David Edwards, Jonathan O'Muircheartaigh, Serena J. Counsell","doi":"10.1101/2024.08.15.24311978","DOIUrl":"https://doi.org/10.1101/2024.08.15.24311978","url":null,"abstract":"Theoretically derived scaling laws capture the non-linear relationships between rapidly expanding brain volume and cortical gyrification across mammalian species and in adult humans. However, the preservation of these laws has not been comprehensively assessed in typical or pathological brain development. Here we assessed the scaling laws governing cortical thickness, surface area and cortical folding in the neonatal brain. We also assessed multivariate morphological terms that capture brain size, shape and folding processes. The sample consisted of 375 typically developing infants, 73 preterm infants and 107 infants with congenital heart disease (CHD) who underwent brain magnetic resonance imaging (MRI). Our results show that typically developing neonates and those with CHD follow the cortical folding scaling law obtained from mammalian brains, children and adults which captures the relationship between exposed surface area, total surface area and cortical thickness. Cortical folding scaling was not affected by gestational age at birth, postmenstrual age at scan, sex or multiple birth in these populations. CHD was characterized by a unique reduction in the multivariate morphological term capturing size, suggesting CHD affects cortical growth overall but not cortical folding processes. In contrast, preterm birth was characterized by altered cortical folding scaling and altered shape, suggesting the developmentally programmed processes of cortical folding are disrupted in this population. The degree of altered shape was associated with cognitive abilities in early childhood in preterm infants.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181594","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}
Pub Date : 2024-08-20DOI: 10.1101/2024.08.19.24312228
Joshua Mawuli Ametepe, James Gholam, Leandro Beltrachini, Mara Cercignani, Derek Jones
Purpose: This study aims to reduce Diffusion Tensor MRI (DT-MRI) scan time by minimizing diffusion-weighted measurements. Using machine learning, DT-MRI parameters are accurately estimated with just four tetrahedrally-arranged diffusion-encoded measurements, instead of the usual six or more. This significantly shortens scan duration and is particularly useful in ultra-low field (ULF) MRI studies and for non-compliant populations (e.g., children, the elderly, or those with movement disorders) where long scan times are impractical. Methods: To improve upon a previous tetrahedral encoding approach, this study used a deep learning (DL) model to predict parallel and radial diffusivities and the principal eigenvector of the diffusion tensor with four tetrahedrally-arranged diffusion-weighted measurements. Synthetic data were generated for model training, covering a range of diffusion tensors with uniformly distributed eigenvectors and eigenvalues. Separate DL models were trained to predict diffusivities and principal eigenvectors, then evaluated on a digital phantom and in vivo data collected at 64 mT. Results: The DL models outperformed the previous tetrahedral encoding method in estimating diffusivities, fractional anisotropy, and principal eigenvectors, with significant improvements in ULF experiments, confirming the DL approach's feasibility in low SNR scenarios. However, the models had limitations when the tensor's principal eigenvector aligned with the scanner's axes Conclusion: The study demonstrates the potential of using DL to perform DT-MRI with only four directions in ULF environments, effectively reducing scan durations and addressing numerical instability seen in previous methods. These findings open new possibilities for ULF DT-MRI applications in research and clinical settings, particularly in pediatric neuroimaging
{"title":"Machine-Learning Enhanced Diffusion Tensor Imaging with Four Encoding Directions","authors":"Joshua Mawuli Ametepe, James Gholam, Leandro Beltrachini, Mara Cercignani, Derek Jones","doi":"10.1101/2024.08.19.24312228","DOIUrl":"https://doi.org/10.1101/2024.08.19.24312228","url":null,"abstract":"Purpose: This study aims to reduce Diffusion Tensor MRI (DT-MRI) scan time by minimizing diffusion-weighted measurements. Using machine learning, DT-MRI parameters are accurately estimated with just four tetrahedrally-arranged diffusion-encoded measurements, instead of the usual six or more. This significantly shortens scan duration and is particularly useful in ultra-low field (ULF) MRI studies and for non-compliant populations (e.g., children, the elderly, or those with movement disorders) where long scan times are impractical. Methods: To improve upon a previous tetrahedral encoding approach, this study used a deep learning (DL) model to predict parallel and radial diffusivities and the principal eigenvector of the diffusion tensor with four tetrahedrally-arranged diffusion-weighted measurements. Synthetic data were generated for model training, covering a range of diffusion tensors with uniformly distributed eigenvectors and eigenvalues. Separate DL models were trained to predict diffusivities and principal eigenvectors, then evaluated on a digital phantom and in vivo data collected at 64 mT. Results: The DL models outperformed the previous tetrahedral encoding method in estimating diffusivities, fractional anisotropy, and principal eigenvectors, with significant improvements in ULF experiments, confirming the DL approach's feasibility in low SNR scenarios. However, the models had limitations when the tensor's principal eigenvector aligned with the scanner's axes Conclusion: The study demonstrates the potential of using DL to perform DT-MRI with only four directions in ULF environments, effectively reducing scan durations and addressing numerical instability seen in previous methods. These findings open new possibilities for ULF DT-MRI applications in research and clinical settings, particularly in pediatric neuroimaging","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227580","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}
Background: Idiopathic hypertrophic pachymeningitis (HP) is a rare chronic inflammatory condition without an identifiable cause characterized by fibrous thickening of the dura mater, which can involve the extraocular muscles (EOM). Objective: To evaluate volumetric changes of EOM in idiopathic HP patients compared with healthy controls (HC) and study the correlation with ocular motility disturbance. Materials and methods: A retrospective study of 22 diagnosed idiopathic HP patients and 22 age-matched, sex-matched HC underwent a 3T MRI scan from January 1, 2017, to December 31, 2022. EOM was manually segmented from the T1W image using 3D Slicer software, and volume was calculated using FSL software. T-tests and Mann-Whitney U tests were used to compare EOM volumes between the idiopathic HP and control groups. Pearson's correlation coefficient was then used to assess the correlation between ocular motility and EOM enlargement. Results: In idiopathic HP patients, the average EOM volumes, including the medial rectus (MR), inferior rectus (IR), inferior oblique (IO), right lateral rectus (LR), right superior oblique (SO), and left superior rectus (SR) muscles, were significantly larger compared to those in HC, particularly in the left IR and both MR. However, there was no significant correlation between the enlargement of these 9 EOMs and the extraocular movement limitation. Conclusion: In idiopathic HP patients, significantly larger EOM volumes were found compared to control subjects. This enlargement could be due to the diffuse infiltrative histopathology potentially involving microstructures in the EOM. Extraocular movement limitations may be related to cranial nerve involvement.
{"title":"Analysis of Extraocular muscle volumes in idiopathic Hypertrophic Pachymeningitis patients","authors":"Suppakul Kitkamolwat, Supichaya Soonthornpusit, Akarawit Eiamsamarng, Natthapon Rattanathamsakul, Niphon Chirapapaisan, Chanon Ngamsombat","doi":"10.1101/2024.08.18.24312196","DOIUrl":"https://doi.org/10.1101/2024.08.18.24312196","url":null,"abstract":"Background: Idiopathic hypertrophic pachymeningitis (HP) is a rare chronic inflammatory condition without an identifiable cause characterized by fibrous thickening of the dura mater, which can involve the extraocular muscles (EOM). Objective: To evaluate volumetric changes of EOM in idiopathic HP patients compared with healthy controls (HC) and study the correlation with ocular motility disturbance. Materials and methods: A retrospective study of 22 diagnosed idiopathic HP patients and 22 age-matched, sex-matched HC underwent a 3T MRI scan from January 1, 2017, to December 31, 2022. EOM was manually segmented from the T1W image using 3D Slicer software, and volume was calculated using FSL software. T-tests and Mann-Whitney U tests were used to compare EOM volumes between the idiopathic HP and control groups. Pearson's correlation coefficient was then used to assess the correlation between ocular motility and EOM enlargement. Results: In idiopathic HP patients, the average EOM volumes, including the medial rectus (MR), inferior rectus (IR), inferior oblique (IO), right lateral rectus (LR), right superior oblique (SO), and left superior rectus (SR) muscles, were significantly larger compared to those in HC, particularly in the left IR and both MR. However, there was no significant correlation between the enlargement of these 9 EOMs and the extraocular movement limitation. Conclusion: In idiopathic HP patients, significantly larger EOM volumes were found compared to control subjects. This enlargement could be due to the diffuse infiltrative histopathology potentially involving microstructures in the EOM. Extraocular movement limitations may be related to cranial nerve involvement.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181593","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}
Pub Date : 2024-08-19DOI: 10.1101/2024.08.19.24312210
Yanyan Kong, Lei Cao, Boyan He, Zhongwen Zhou, Minmin Zhang, Qian Zhang, Qian Wang, Wei Wang, Haoxiang Zhu, Jianfei Xiao, Axel Rominger, Yihui Guan, Haibo Tan, Ruiqing Ni
Purpose: Amyloidosis is underdiagnosed in light-chain amyloidosis (AL) and hereditary transthyretin amyloidosis (hATTR), as well as plasma cell dyscrasias (PCD). We aimed to investigate the utility of [18F]florbetapir (FBP) and [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) for the early detection and evaluation of organ involvement in systemic amyloidosis. Methods: We retrospectively included 83 participants, including 38 AL patients, 8 hATTR polyneuropathy patients, 28 PCD patients and 9 healthy controls. Whole-body PET/CT using [18F]FBP was performed in all participants, [18F]FDG PET was performed in 37 patients with AL and PCD, and the results were analyzed by visual and quantitative assessment. Biochemical, serum, and urine assays and histological analysis of tissue biopsies were performed. Results: [18F]FBP SUV and TBR analysis showed comparable uptake in AL and hATTR-PN patients (p.A117S, p.V50M, p.K55N, p.T69AM, or p.H76R mutation carriers) and greater uptake than in PCD patients and control patients. Different regional [18F]FBP and [18F]FDG distributions were observed among the PCD, AL, and hATTR-PN groups. Both [18F]FBP and [18F]FDG enabled the detection of amyloidosis in patients with PCD before clinical detection of AL. [18F]FBP SUV and visual analysis provide comparable measures of organ involvement and were comparable to [18F]FDG and clinical assessment. Conclusions: [18F]FBP PET detected organ amyloidosis in PCD, AL and hATTR-PN patients with high sensitivity and specificity and was more sensitive than [18F]FDG. Visual analysis and SUV analysis of [18F]FBP PET data provide comparable methods for evaluating organ involvement and are useful for noninvasively assisting in the early and accurate detection of systemic amyloidosis.
{"title":"Head-to-head comparison of [18F]florbetapir and [18F]FDG PET for the early detection of amyloidosis in systemic amyloidosis and plasma cell dyscrasias","authors":"Yanyan Kong, Lei Cao, Boyan He, Zhongwen Zhou, Minmin Zhang, Qian Zhang, Qian Wang, Wei Wang, Haoxiang Zhu, Jianfei Xiao, Axel Rominger, Yihui Guan, Haibo Tan, Ruiqing Ni","doi":"10.1101/2024.08.19.24312210","DOIUrl":"https://doi.org/10.1101/2024.08.19.24312210","url":null,"abstract":"Purpose:\u0000Amyloidosis is underdiagnosed in light-chain amyloidosis (AL) and hereditary transthyretin amyloidosis (hATTR), as well as plasma cell dyscrasias (PCD). We aimed to investigate the utility of [18F]florbetapir (FBP) and [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) for the early detection and evaluation of organ involvement in systemic amyloidosis.\u0000Methods:\u0000We retrospectively included 83 participants, including 38 AL patients, 8 hATTR polyneuropathy patients, 28 PCD patients and 9 healthy controls. Whole-body PET/CT using [18F]FBP was performed in all participants, [18F]FDG PET was performed in 37 patients with AL and PCD, and the results were analyzed by visual and quantitative assessment. Biochemical, serum, and urine assays and histological analysis of tissue biopsies were performed.\u0000Results:\u0000[18F]FBP SUV and TBR analysis showed comparable uptake in AL and hATTR-PN patients (p.A117S, p.V50M, p.K55N, p.T69AM, or p.H76R mutation carriers) and greater uptake than in PCD patients and control patients. Different regional [18F]FBP and [18F]FDG distributions were observed among the PCD, AL, and hATTR-PN groups. Both [18F]FBP and [18F]FDG enabled the detection of amyloidosis in patients with PCD before clinical detection of AL. [18F]FBP SUV and visual analysis provide comparable measures of organ involvement and were comparable to [18F]FDG and clinical assessment.\u0000Conclusions:\u0000[18F]FBP PET detected organ amyloidosis in PCD, AL and hATTR-PN patients with high sensitivity and specificity and was more sensitive than [18F]FDG. Visual analysis and SUV analysis of [18F]FBP PET data provide comparable methods for evaluating organ involvement and are useful for noninvasively assisting in the early and accurate detection of systemic amyloidosis.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223837","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}
Pub Date : 2024-08-14DOI: 10.1101/2024.08.13.24311671
Logan X Zhang, Thomas F Kirk, Martin S Craig, Michael A Chappell
Data normalisation is an important approach to reduce inter-subject variability in group studies, but care must be taken when choosing a normalisation strategy to not introduce further confounds or artefacts into the data. Normalisation of arterial spin labelling perfusion measurements remains challenging, especially in the context of Alzheimer's disease, where there may be global hypoperfusion present. We propose that using the thalamus as a reference region for normalisation could improve the detectability of hypoperfusion in Alzheimer's disease and alleviate the pseudo-hyperperfusion artefacts caused by the commonly-used strategy of normalisation using global mean perfusion. Evaluation on an Alzheimer's disease dataset found this strategy was able to reduce coefficient of variation in perfusion measurements by around 60% and yield increases in statistical power of comparisons against healthy controls.
{"title":"Thalamus normalisation improves detectability of hypoperfusion via arterial spin labelling in an Alzheimer's disease cohort","authors":"Logan X Zhang, Thomas F Kirk, Martin S Craig, Michael A Chappell","doi":"10.1101/2024.08.13.24311671","DOIUrl":"https://doi.org/10.1101/2024.08.13.24311671","url":null,"abstract":"Data normalisation is an important approach to reduce inter-subject variability in group studies, but care must be taken when choosing a normalisation strategy to not introduce further confounds or artefacts into the data. Normalisation of arterial spin labelling perfusion measurements remains challenging, especially in the context of Alzheimer's disease, where there may be global hypoperfusion present. We propose that using the thalamus as a reference region for normalisation could improve the detectability of hypoperfusion in Alzheimer's disease and alleviate the pseudo-hyperperfusion artefacts caused by the commonly-used strategy of normalisation using global mean perfusion. Evaluation on an Alzheimer's disease dataset found this strategy was able to reduce coefficient of variation in perfusion measurements by around 60% and yield increases in statistical power of comparisons against healthy controls.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181597","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}
Objective: To explore the correlation between enlarged perivascular spaces (EPVS) in the basal ganglia (BG-EPVS) and centrum semiovale (CSO-EPVS) and changes in adult brain cerebral blood flow (CBF). Methods: This cross-sectional single-center cohort study included individuals with varying degrees of EPVS, divided into the BG and CSO based on the established rating scale. Subsequently, the arterial spin labeling (ASL) sequence and its post-processing operation were utilized to obtain CBF values for different grades of BG-EPVS and CSO-EPVS. Logistic regression was conducted to identify risk factors associated with BG-EPVS and CSO-EPVS, and correlation analysis was employed to explore the associations between different grades of BG-EPVS and CSO-EPVS with CBF of the whole brain and specific regions of interest. Results: The regression analysis revealed that BG-EPVS was associated with age (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.15), hypertension (4.91,1.55-15.6), and periventricular white matter hyperintensities (PVWMH) (4.34,1.46-12.95). Conversely, CSO-EPVS was linked to hypertension (4.40,1.43-13.57), drinking history (2.84,1.08-7.45), sleep duration (2.01,1.19-3.40), and PVWMH (12.20,3.83-38.85). Correlation analysis revealed a negative correlation between BG-EPVS and the CBF of the whole brain (r=-0.28, p=0.00) and most brain regions, except for the brain stem (r=-0.19, p=0.05). Conversely, CSO-EPVS was negatively correlated with CBF of temporal lobe white matter (r=-0.25, p=0.01); however, the significance was lost after FDR correction. CSO-EPVS was not correlated with CBF across various brain regions. Conclusion: Brain CBF decreased with the increasing severity of BG-EPVS, suggesting that BG-EPVS could serve as an imaging marker for reflecting the changes in brain CBF and an effective indicator for early ischemic stroke.
{"title":"Brain cerebral blood flow with MRI-visible enlarged perivascular space in adults","authors":"Chunyan Yu, Baijie Wang, Qiyuan Sun, Huiyan Huo, Lingyan Zhang, Du Hongyan","doi":"10.1101/2024.08.12.24311906","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311906","url":null,"abstract":"Objective: To explore the correlation between enlarged perivascular spaces (EPVS) in the basal ganglia (BG-EPVS) and centrum semiovale (CSO-EPVS) and changes in adult brain cerebral blood flow (CBF).\u0000Methods: This cross-sectional single-center cohort study included individuals with varying degrees of EPVS, divided into the BG and CSO based on the established rating scale. Subsequently, the arterial spin labeling (ASL) sequence and its post-processing operation were utilized to obtain CBF values for different grades of BG-EPVS and CSO-EPVS. Logistic regression was conducted to identify risk factors associated with BG-EPVS and CSO-EPVS, and correlation analysis was employed to explore the associations between different grades of BG-EPVS and CSO-EPVS with CBF of the whole brain and specific regions of interest. Results: The regression analysis revealed that BG-EPVS was associated with age (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.15), hypertension (4.91,1.55-15.6), and periventricular white matter hyperintensities (PVWMH) (4.34,1.46-12.95). Conversely, CSO-EPVS was linked to hypertension (4.40,1.43-13.57), drinking history (2.84,1.08-7.45), sleep duration (2.01,1.19-3.40), and PVWMH (12.20,3.83-38.85). Correlation analysis revealed a negative correlation between BG-EPVS and the CBF of the whole brain (r=-0.28, p=0.00) and most brain regions, except for the brain stem (r=-0.19, p=0.05). Conversely, CSO-EPVS was negatively correlated with CBF of temporal lobe white matter (r=-0.25, p=0.01); however, the significance was lost after FDR correction. CSO-EPVS was not correlated with CBF across various brain regions.\u0000Conclusion: Brain CBF decreased with the increasing severity of BG-EPVS, suggesting that BG-EPVS could serve as an imaging marker for reflecting the changes in brain CBF and an effective indicator for early ischemic stroke.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181595","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}
Pub Date : 2024-08-14DOI: 10.1101/2024.08.11.24304174
Dev Desai, Maria Eleni Malafi, Hetvi Shah, Aneri Parikh, Abhijay B Shah, Vismit Gami, Parth Gupta
Introduction: - CEUS has become an emerging radio diagnostic technique of modern times. The use of these contrasts offers a way better alternative than materials that cause radiation. Thyroid nodules are notorious for their effect on normal physiology and the routine best diagnostic modality apart from biopsy is Radioactive Iodine. Aim:- To conduct a diagnostic test accuracy meta-analysis to understand the role of CEUS in diagnosing thyroid nodules. Methodology:- According to Prisma guidelines, literature on the topic was found using the Keywords CEUS, Thyroid Nodule, and Radioactive Iodine. Two independent reviewers conducted a quality check on the papers and decided on the studies that should be included. Any discrepancies were solved by a third reviewer. Meta Disc, Review Manager, and Excel were used to analyze the extracted data from the selected studies as per the inclusion and exclusion criteria. Biopsy was taken as a Reference Gold Standard. Result:- A total of 47 RCTs with 5,527 patients were selected for the study. The pooled sensitivity of CEUS is 0.87, with a CI of 95% in a range of 0.86 to 0.88. The specificity of CEUS is 0.84, with a CI of 95% in a range of 0.82 to 0.85. The summary of the ROC curve shows that the area under the curve for CEUS was 0.9292 and the overall diagnostic odds ratio (DOR) was 40.59. Conclusion:- It can be concluded from the results that CEUS can be used as a Screening tool for high suspicion groups but it is still not a perfect test. The newer generation of Contrasts may yield higher accuracy but for the currently available contrasts, Biopsy remains the best tool for a definitive and accurate diagnosis. Keywords:- Thyroid Nodule, Contrast-Enhanced Ultrasound, Benign, Malignant, Prognosis
{"title":"Enhancing Thyroid Nodule Assessment: Leveraging Contrast-Enhanced Ultrasonography as a Screening Modality - A Meta-Analysis","authors":"Dev Desai, Maria Eleni Malafi, Hetvi Shah, Aneri Parikh, Abhijay B Shah, Vismit Gami, Parth Gupta","doi":"10.1101/2024.08.11.24304174","DOIUrl":"https://doi.org/10.1101/2024.08.11.24304174","url":null,"abstract":"Introduction: - CEUS has become an emerging radio diagnostic technique of modern times. The use of these contrasts offers a way better alternative than materials that cause radiation. Thyroid nodules are notorious for their effect on normal physiology and the routine best diagnostic modality apart from biopsy is Radioactive Iodine. Aim:- To conduct a diagnostic test accuracy meta-analysis to understand the role of CEUS in diagnosing thyroid nodules. Methodology:- According to Prisma guidelines, literature on the topic was found using the Keywords CEUS, Thyroid Nodule, and Radioactive Iodine. Two independent reviewers conducted a quality check on the papers and decided on the studies that should be included. Any discrepancies were solved by a third reviewer. Meta Disc, Review Manager, and Excel were used to analyze the extracted data from the selected studies as per the inclusion and exclusion criteria. Biopsy was taken as a Reference Gold Standard. Result:- A total of 47 RCTs with 5,527 patients were selected for the study. The pooled sensitivity of CEUS is 0.87, with a CI of 95% in a range of 0.86 to 0.88. The specificity of CEUS is 0.84, with a CI of 95% in a range of 0.82 to 0.85. The summary of the ROC curve shows that the area under the curve for CEUS was 0.9292 and the overall diagnostic odds ratio (DOR) was 40.59. Conclusion:- It can be concluded from the results that CEUS can be used as a Screening tool for high suspicion groups but it is still not a perfect test. The newer generation of Contrasts may yield higher accuracy but for the currently available contrasts, Biopsy remains the best tool for a definitive and accurate diagnosis. Keywords:- Thyroid Nodule, Contrast-Enhanced Ultrasound, Benign, Malignant, Prognosis","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181596","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}
Pub Date : 2024-08-07DOI: 10.1101/2024.08.07.24311608
Hannes Schreiter, Jessica Eberle, Lorenz A. Kapsner, Dominique Hadler, Sabine Ohlmeyer, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sebastian Bickelhaupt, Andrzej Liebert
Breast Magnetic Resonance Imaging (MRI) examinations routinely include contrast-agent based dynamic contrast-enhanced (DCE) acquisitions. Expanding the accessibility and personalization of breast MRI might be supported amongst others by advancing non-contrast-enhanced MRI, such as virtual dynamic contrast-enhanced techniques (vDCE) utilizing neural networks. This IRB-approved retrospective study includes n=540 breast MRI examinations acquired on a single 3T MRI scanner. Two 2D U-Net architectures were trained using non-contrast-enhanced MRI acquisitions including T1w, T2w and multi-b-value diffusion weighted imaging acquisitions as inputs and either a single (SCO-Net) or multiple (MCO-Net) time points of a DCE series as ground truth. The neural networks predicted a vDCE series corresponding to five consecutive DCE time points. Across all time points, no significant differences in structural similarity index (SSIM) could be found between the SCO-Net and MCO-Net, both achieving a mean SSIM of 0.86. For peak-signal-to-noise-ratio and normalized root-mean-square error, significantly better results could be observed for the MCO-Net reaching scores of 24.42dB and 0.087 respectively. Comparison of manual segmentations of findings on DCE and vDCE images reached a DICE score of 0.61 and an intersection over union (IoU) of 0.47 without significant differences between SCO-Net and MCO-Net. These findings suggest a technical feasibility of generating vDCE image series from unenhanced input acquisitions using neural networks. However, the analysis does not allow drawing any conclusion on the clinical assessment of lesion specific curve kinetics, which need to be assessed prior determining on the feasibility of deriving diagnostically meaningful enhancement characteristics in individual lesions.
{"title":"Virtual dynamic contrast enhanced breast MRI using 2D U-Net Architectures.","authors":"Hannes Schreiter, Jessica Eberle, Lorenz A. Kapsner, Dominique Hadler, Sabine Ohlmeyer, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sebastian Bickelhaupt, Andrzej Liebert","doi":"10.1101/2024.08.07.24311608","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311608","url":null,"abstract":"Breast Magnetic Resonance Imaging (MRI) examinations routinely include contrast-agent based dynamic contrast-enhanced (DCE) acquisitions. Expanding the accessibility and personalization of breast MRI might be supported amongst others by advancing non-contrast-enhanced MRI, such as virtual dynamic contrast-enhanced techniques (vDCE) utilizing neural networks. This IRB-approved retrospective study includes n=540 breast MRI examinations acquired on a single 3T MRI scanner. Two 2D U-Net architectures were trained using non-contrast-enhanced MRI acquisitions including T1w, T2w and multi-b-value diffusion weighted imaging acquisitions as inputs and either a single (SCO-Net) or multiple (MCO-Net) time points of a DCE series as ground truth. The neural networks predicted a vDCE series corresponding to five consecutive DCE time points. Across all time points, no significant differences in structural similarity index (SSIM) could be found between the SCO-Net and MCO-Net, both achieving a mean SSIM of 0.86. For peak-signal-to-noise-ratio and normalized root-mean-square error, significantly better results could be observed for the MCO-Net reaching scores of 24.42dB and 0.087 respectively. Comparison of manual segmentations of findings on DCE and vDCE images reached a DICE score of 0.61 and an intersection over union (IoU) of 0.47 without significant differences between SCO-Net and MCO-Net. These findings suggest a technical feasibility of generating vDCE image series from unenhanced input acquisitions using neural networks. However, the analysis does not allow drawing any conclusion on the clinical assessment of lesion specific curve kinetics, which need to be assessed prior determining on the feasibility of deriving diagnostically meaningful enhancement characteristics in individual lesions.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941565","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}
Pub Date : 2024-08-07DOI: 10.1101/2024.08.02.24311435
Naitik Mohanty, Morteza Sarmadi
Alzheimer's disease (AD) presents a significant societal challenge, with no current cure and an increasing prevalence among older adults. This study addresses the pressing need for early detection by harnessing the potential of machine learning applied to longitudinal MRI data. The dataset, sourced from the Open Access Series of Imaging Studies (OASIS) project, comprises MRI records of 150 subjects aged 60 to 96, each scanned at least once. Notably, 72 subjects were classified as 'Nondemented,' 64 as 'Demented,' and 14 underwent a transition from 'Nondemented' to 'Demented,' forming the 'Converted' category. What we propose is to develop a machine learning sound model capable of predicting the progression of mild cognitive impairment, leveraging key biomarkers extracted from MRI data. The chosen biomarkers include years of education (EDUC), socioeconomic status (SES), Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF). Prior work in the field is referenced, highlighting studies that predominantly focused on raw MRI data analysis. In contrast, this study introduces a unique approach by utilizing a curated set of biomarkers, allowing for a more targeted and potentially interpretable model. Machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest Classifier, and AdaBoost are employed, with performance measured using established metrics. Information about severity and state are stored during the EADDLS module and used for ADmod. ADmod uses the stored MRI data during the EADDLS module to model the growth of amyloid β build-up in the brain using convolution, resulting in both generalizable approaches and patient-specific approaches. There have been numerous mathematical instantiations to model amyloid β build-up using partial differential equations (or PDEs), these however have remained unincorporated due to prolonged runtimes and storage limitations along with those of pre-set conditions. We propose a novel amyloid β growth model using deep encoder-decoder networks in conjunction with convolution. The study contributes to the growing body of research in early Alzheimer's detection, offering insights, results, and a discussion of limitations. The conclusion outlines a unique approach, emphasizes the practical implementation of the proposed model, acknowledges limitations, and suggests avenues for further research. Early detection of AD can significantly better the patient's quality of care and lead to future preventative or risk assessment measures.
{"title":"A quantitative analysis of Alzheimers Disease and construction of an early Alzheimers detection deep learning system (EADDLS) using MRI data via machine learning along with ADmod: spatiotemporal-aware brain-amyloidβ growth model, using deep encoder-decoder networks about MRI","authors":"Naitik Mohanty, Morteza Sarmadi","doi":"10.1101/2024.08.02.24311435","DOIUrl":"https://doi.org/10.1101/2024.08.02.24311435","url":null,"abstract":"Alzheimer's disease (AD) presents a significant societal challenge, with no current cure and an increasing prevalence among older adults. This study addresses the pressing need for early detection by harnessing the potential of machine learning applied to longitudinal MRI data. The dataset, sourced from the Open Access Series of Imaging Studies (OASIS) project, comprises MRI records of 150 subjects aged 60 to 96, each scanned at least once. Notably, 72 subjects were classified as 'Nondemented,' 64 as 'Demented,' and 14 underwent a transition from 'Nondemented' to 'Demented,' forming the 'Converted' category. What we propose is to develop a machine learning sound model capable of predicting the progression of mild cognitive impairment, leveraging key biomarkers extracted from MRI data. The chosen biomarkers include years of education (EDUC), socioeconomic status (SES), Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF). Prior work in the field is referenced, highlighting studies that predominantly focused on raw MRI data analysis. In contrast, this study introduces a unique approach by utilizing a curated set of biomarkers, allowing for a more targeted and potentially interpretable model. Machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest Classifier, and AdaBoost are employed, with performance measured using established metrics. Information about severity and state are stored during the EADDLS module and used for ADmod. ADmod uses the stored MRI data during the EADDLS module to model the growth of amyloid β build-up in the brain using convolution, resulting in both generalizable approaches and patient-specific approaches. There have been numerous mathematical instantiations to model amyloid β build-up using partial differential equations (or PDEs), these however have remained unincorporated due to prolonged runtimes and storage limitations along with those of pre-set conditions. We propose a novel amyloid β growth model using deep encoder-decoder networks in conjunction with convolution. The study contributes to the growing body of research in early Alzheimer's detection, offering insights, results, and a discussion of limitations. The conclusion outlines a unique approach, emphasizes the practical implementation of the proposed model, acknowledges limitations, and suggests avenues for further research. Early detection of AD can significantly better the patient's quality of care and lead to future preventative or risk assessment measures.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941567","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}
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