The Laboratory of Mathematics in Imaging (LMI) is focused on the application of mathematical theory, analysis, modeling, and signal processing to medical imaging. Research projects within the group cover both novel theoretical contributions and translational clinical efforts. The research team combine strengths in computer science and mathematics with radiology, neuroscience, and novel MRI sequence developmentLearn more

Recent Publications

Time dependence in diffusion MRI predicts tissue outcome in ischemic stroke patients

Lampinen B, Lätt J, Wasselius J, van Westen D, Nilsson M. Time dependence in diffusion MRI predicts tissue outcome in ischemic stroke patients. Magn Reson Med. 2021.Abstract
PURPOSE: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. METHODS: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. RESULTS: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. CONCLUSIONS: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
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The ventral pathway of the human brain: a continuous, single association tract system

Weiller C, Reisert M, Peto I, Hennig J, Makris N, Petrides M, Rijntjes M, Egger K. The ventral pathway of the human brain: a continuous, single association tract system. Neuroimage. 2021 :117977.Abstract
The brain hemispheres can be divided into an upper dorsal and a lower ventral system. Each system consists of distinct cortical regions connected via long association tracts. The tracts cross the central sulcus or the limen insulae to connect the frontal lobe with the posterior brain. The dorsal stream is associated with sensorimotor mapping. The ventral stream serves structural analysis and semantics in different domains, as visual, acoustic or space processing. How does the prefrontal cortex, regarded as the platform for the highest level of integration, incorporate information from these different domains? In the current view, the ventral pathway consists of several separate tracts, related to different modalities. Originally the assumption was that the ventral path is a continuum, covering all modalities. The latter would imply a very different anatomical basis for cognitive and clinical models of processing. To further define the ventral connections, we used cutting-edge in vivo global tractography on high-resolution diffusion tensor imaging (DTI) data from 100 normal subjects from the human connectome project and ex vivo preparation of fiber bundles in the extreme capsule of 8 humans using the Klingler technique. Our data showed that ventral stream tracts, traversing through the extreme capsule, form a continuous band of fibers that fan out anteriorly to the prefrontal cortex, and posteriorly to temporal, occipital and parietal cortical regions. Introduction of additional volumes of interest in temporal and occipital lobes differentiated between the inferior fronto-occipital fascicle (IFOF) and uncinate fascicle (UF). Unequivocally, in both experiments, in all subjects a connection between the inferior frontal and middle-to-posterior temporal cortical region, otherwise known as the temporo-frontal extreme capsule fascicle (ECF) from nonhuman primate brain-tracing experiments was identified. In the human brain, this tract connects the language domains of Broca's area and Wernicke's area. The differentiation in the three tracts, IFOF, UF and ECF seems arbitrary, all three pass through the extreme capsule. Our data show that the ventral pathway represents a continuum. The three tracts merge seamlessly and streamlines showed considerable overlap in their anterior and posterior course. Terminal maps identified prefrontal cortex in the frontal lobe and association cortices in temporal, occipital and parietal lobes as streamline endings. This anatomical substrate potentially facilitates the prefrontal cortex to integrate information across different domains and modalities.
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Progression of traction bronchiectasis/bronchiolectasis in interstitial lung abnormalities is associated with increased all-cause mortality: Age Gene/Environment Susceptibility-Reykjavik Study

Hino T, Hida RY, Nishino M, Lu J, Putman RK, Gudmundsson EF, Estepar RS. Progression of traction bronchiectasis/bronchiolectasis in interstitial lung abnormalities is associated with increased all-cause mortality: Age Gene/Environment Susceptibility-Reykjavik Study. Eur J Radiol Open. 2021;8 :100334.Abstract

Purpose: The aim of this study is to assess the role of traction bronchiectasis/bronchiolectasis and its progression as a predictor for early fibrosis in interstitial lung abnormalities (ILA).

Methods: Three hundred twenty-seven ILA participants out of 5764 in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study who had undergone chest CT twice with an interval of approximately five-years were enrolled in this study. Traction bronchiectasis/bronchiolectasis index (TBI) was classified on a four-point scale: 0, ILA without traction bronchiectasis/bronchiolectasis; 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; 2, ILA with mild to moderate traction bronchiectasis; 3, ILA and severe traction bronchiectasis and/or honeycombing. Traction bronchiectasis (TB) progression was classified on a five-point scale: 1, Improved; 2, Probably improved; 3, No change; 4, Probably progressed; 5, Progressed. Overall survival (OS) among participants with different TB Progression Score and between the TB progression group and No TB progression group was also investigated. Hazard radio (HR) was estimated with Cox proportional hazards model.

Results: The higher the TBI at baseline, the higher TB Progression Score (P < 0.001). All five participants with TBI = 3 at baseline progressed; 46 (90 %) of 51 participants with TBI = 2 progressed. TB progression was also associated with shorter OS with statistically significant difference (adjusted HR = 1.68, P < 0.001).

Conclusion: TB progression was visualized on chest CT frequently and clearly. It has the potential to be the predictor for poorer prognosis of ILA.

Keywords: AGES-Reykjavik Study, Age Gene/Environment Susceptibility-Reykjavik Study; Age Gene/Environment Susceptibility-Reykjavik Study; BMI, body mass index; HR, hazard ratio; ILA, interstitial lung abnormalities; ILD, interstitial lung disease; Interstitial lung abnormality; OS, overall survival; Pulmonary fibrosis; TB, traction bronchiectasis; TBI, traction bronchiectasis/bronchiolecetasis index; TBI-R2, traction bronchiectasis/bronchiolecetasis index on Round 2; Traction bronchiectasis; Usual interstitial pneumonia.

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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Zhang F, Breger A, Cho KIK, Ning L, Westin C-F, O'Donnell LJ, Pasternak O. Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI. Neuroimage. 2021 :117934.Abstract
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
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QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

Avila RS, Fain SB, Hatt C, Armato SG, Mulshine JL, Gierada D, Silva M, Lynch DA, Hoffman EA, Ranallo FN, et al. QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications. Clin Imaging. 2021;77 :151-157.Abstract
As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.
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Age at First Exposure to Tackle Football is Associated with Cortical Thickness in Former Professional American Football Players

Kaufmann D, Sollmann N, Kaufmann E, Veggeberg R, Tripodis Y, Wrobel PP, Kochsiek J, Martin BM, Lin AP, Coleman MJ, et al. Age at First Exposure to Tackle Football is Associated with Cortical Thickness in Former Professional American Football Players. Cereb Cortex. 2021.Abstract
Younger age at first exposure (AFE) to repetitive head impacts while playing American football increases the risk for later-life neuropsychological symptoms and brain alterations. However, it is not known whether AFE is associated with cortical thickness in American football players. Sixty-three former professional National Football League players (55.5 ± 7.7 years) with cognitive, behavioral, and mood symptoms underwent neuroimaging and neuropsychological testing. First, the association between cortical thickness and AFE was tested. Second, the relationship between clusters of decreased cortical thickness and verbal and visual memory, and composite measures of mood/behavior and attention/psychomotor speed was assessed. AFE was positively correlated with cortical thickness in the right superior frontal cortex (cluster-wise P value [CWP] = 0.0006), the left parietal cortex (CWP = 0.0003), and the occipital cortices (right: CWP = 0.0023; left: CWP = 0.0008). A positive correlation was found between cortical thickness of the right superior frontal cortex and verbal memory (R = 0.333, P = 0.019), and the right occipital cortex and visual memory (R = 0.360, P = 0.012). In conclusion, our results suggest an association between younger AFE and decreased cortical thickness, which in turn is associated with worse neuropsychological performance. Furthermore, an association between younger AFE and signs of neurodegeneration later in life in symptomatic former American football players seems likely.
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