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Pascal Spincemaille

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11 papers

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YNIMG Journal 2023 Journal Article

LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping

Jinwei Zhang
Pascal Spincemaille
Hang Zhang
Thanh D. Nguyen
Chao Li
Jiahao Li
Ilhami Kovanlikaya
Mert R. Sabuncu

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.

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YNICL Journal 2022 Journal Article

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps

Hang Zhang
Thanh D. Nguyen
Jinwei Zhang
Melanie Marcille
Pascal Spincemaille
Yi Wang
Susan A. Gauthier
Elizabeth M. Sweeney

BACKGROUND AND PURPOSE: Chronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim + lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim + lesions on QSM. METHODS: QSM and T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI of the brain were collected at 3 T for 172 MS patients. Rim + lesions were manually annotated by two human experts, followed by consensus from a third expert, for a total of 177 rim + and 3986 rim negative (rim-) lesions. Our automated rim + detection algorithm, QSMRim-Net, consists of a two-branch feature extraction network and a synthetic minority oversampling network to classify rim + lesions. The first network branch is for image feature extraction from the QSM and T2-FLAIR, and the second network branch is a fully connected network for QSM lesion-level radiomic feature extraction. The oversampling network is designed to increase classification performance with imbalanced data. RESULTS: On a lesion-level, in a five-fold cross validation framework, the proposed QSMRim-Net detected rim + lesions with a partial area under the receiver operating characteristic curve (pROC AUC) of 0.760, where clinically relevant false positive rates of less than 0.1 were considered. The method attained an area under the precision recall curve (PR AUC) of 0.704. QSMRim-Net out-performed other state-of-the-art methods applied to the QSM on both pROC AUC and PR AUC. On a subject-level, comparing the predicted rim + lesion count and the human expert annotated count, QSMRim-Net achieved the lowest mean square error of 0.98 and the highest correlation of 0.89 (95% CI: 0.86, 0.92). CONCLUSION: This study develops a novel automated deep neural network for rim + MS lesion identification using T2-FLAIR and QSM images.

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YNICL Journal 2021 Journal Article

ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Hang Zhang
Jinwei Zhang
Chao Li
Elizabeth M. Sweeney
Pascal Spincemaille
Thanh D. Nguyen
Susan A. Gauthier
Yi Wang

Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.

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AAAI Conference 2021 Conference Paper

Efficient Folded Attention for Medical Image Reconstruction and Segmentation

Hang Zhang
Jinwei Zhang
Rongguang Wang
Qihao Zhang
Pascal Spincemaille
Thanh D. Nguyen
Yi Wang

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed for performance enhancement. However, the large size of 3D volume images poses a great computational challenge to traditional attention methods. In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images. The main idea is that we apply tensor folding and unfolding operations to construct four small sub-affinity matrices to approximate the original affinity matrix. Through four consecutive sub-attention modules of FA, each element in the feature tensor can aggregate spatial-channel information from all other elements. Compared to traditional attention methods, with the moderate improvement of accuracy, FA can substantially reduce the computational complexity and GPU memory consumption. We demonstrate the superiority of our method on two challenging tasks for 3D MIR and MIS, which are quantitative susceptibility mapping and multiple sclerosis lesion segmentation.

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YNICL Journal 2021 Journal Article

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

Po-Jui Lu
Youngjin Yoo
Reza Rahmanzadeh
Riccardo Galbusera
Matthias Weigel
Pascal Ceccaldi
Thanh D. Nguyen
Pascal Spincemaille

INTRODUCTION: During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. METHODS: GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. RESULTS: In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. CONCLUSIONS: This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.

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YNICL Journal 2021 Journal Article

Increased risk for cerebral small vessel disease is associated with quantitative susceptibility mapping in HIV infected and uninfected individuals

Kyle D. Murray
Md Nasir Uddin
Madalina E. Tivarus
Bogachan Sahin
Henry Z. Wang
Meera V. Singh
Xing Qiu
Lu Wang

The aim of this study was to assess, in the context of cerebral small vessel disease (CSVD), whether cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age ± SD = 53.2 ± 10.9 years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynolds scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.

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YNIMG Journal 2020 Journal Article

Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction

Jinwei Zhang
Zhe Liu
Shun Zhang
Hang Zhang
Pascal Spincemaille
Thanh D. Nguyen
Mert R. Sabuncu
Yi Wang

Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.

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YNIMG Journal 2019 Journal Article

3D texture analyses within the substantia nigra of Parkinson's disease patients on quantitative susceptibility maps and R2∗ maps

Gaiying Li
Guoqiang Zhai
Xinxin Zhao
Hedi An
Pascal Spincemaille
Kelly M. Gillen
Yixuan Ku
Yi Wang

Iron accumulation in the substantia nigra (SN) is spatially heterogeneous, yet no study has quantitatively evaluated how the texture of quantitative susceptibility maps (QSM) and R2∗ might evolve with Parkinson's disease (PD) and healthy controls (HC). The aim of this study was to discriminate between patients with PD and HC using texture analysis in the SN from QSM and R2∗ maps. QSM and R2∗ maps were obtained from 28 PD patients and 28 HC on a clinical 3T MR imaging scanner using 3D multi-echo gradient-echo sequence. The first- and second- order texture features of the QSM and R2∗ images were obtained to evaluate group differences using two-tailed t-test. After correction for multiple comparisons, for the first-order analysis, the susceptibility of SN from patients with PD was significantly greater (p = 0. 017) compared with the SN from HC. For the second-order texture analysis, angular second moment, entropy, and sum of entropy showed significant differences in QSM (p < 0. 001) and R2∗ maps (p < 0. 01). In addition, correlation, contrast, sum of variance and difference of variance, significantly separated the subject groups in QSM maps (p < 0. 05) but not in R2∗ images. Receiver operating characteristic analysis showed that entropy and sum of entropy of the QSM maps in the SN yielded the highest performance for differentiating PD patients from HC (area under the curve = 0. 89). In conclusion, most first- and second- order QSM texture features successfully distinguished PD patients from HC and significantly outperformed R2∗ texture analysis. The second-order texture features were more accurate and sensitive than first-order texture features for classifying PD patients.

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YNIMG Journal 2015 Journal Article

Age and sex related differences in subcortical brain iron concentrations among healthy adults

Ninni Persson
Jianlin Wu
Qing Zhang
Ting Liu
Jing Shen
Ruyi Bao
Mingfei Ni
Tian Liu

Age and sex can influence brain iron levels. We studied the influence of these variables on deep gray matter magnetic susceptibilities. In 183 healthy volunteers (44. 7±14. 2years, range 20–69, ♀ 49%), in vivo quantitative susceptibility mapping (QSM) at 1. 5T was performed to estimate brain iron accumulation in the following regions of interest (ROIs): caudate nucleus (Cd), putamen (Pt), globus pallidus (Gp), thalamus (Th), pulvinar (Pul), red nucleus (Rn), substantia nigra (Sn) and the cerebellar dentate nuclei (Dn). We gauged the influence of age and sex on magnetic susceptibility by specifying a series of structural equation models. The distributions of susceptibility varied in degree across the structures, conforming to histologic findings (Hallgren and Sourander, 1958), with the highest degree of susceptibility in the Gp and the lowest in the Th. Iron increase correlated across several ROIs, which may reflect an underlying age-related process. Advanced age was associated with a particularly strong linear rise of susceptibility in the striatum. Nonlinear age trends were found in the Rn, where they were the most pronounced, followed by the Pul and Sn, while minimal nonlinear trends were observed for the Pt, Th, and Dn. Moreover, sex related variations were observed, so that women showed lower levels of susceptibility in the Sn after accounting for age. Regional susceptibility of the Pul increased linearly with age in men but exhibited a nonlinear association with age in women with a leveling off starting from midlife. Women expected to be post menopause (+51years) showed lower total magnetic susceptibility in the subcortical gray matter. The current report not only is consistent with previous reports of age related variations of brain iron, but also adds to the current knowledge by reporting age-related changes in less studied, smaller subcortical nuclei. This is the first in-vivo report to show lower total subcortical brain iron levels selectively in women from midlife, compared to men and younger women. These results encourage further assessment of sex differences in brain iron. We anticipate that age and sex are important co-factors to take into account when establishing a baseline level for differentiating pathologic neurodegeneration from healthy aging. The variations in regional susceptibility reported herein should be evaluated further using a longitudinal study design to determine within-person changes in aging.

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YNIMG Journal 2013 Journal Article

Magnetic susceptibility anisotropy: Cylindrical symmetry from macroscopically ordered anisotropic molecules and accuracy of MRI measurements using few orientations

Cynthia Wisnieff
Tian Liu
Pascal Spincemaille
Shuai Wang
Dong Zhou
Yi Wang

White matter is an essential component of the central nervous system and is of major concern in neurodegenerative diseases such as multiple sclerosis (MS). Recent MRI studies have explored the unique anisotropic magnetic properties of white matter using susceptibility tensor imaging. However, these measurements are inhibited in practice by the large number of different head orientations needed to accurately reconstruct the susceptibility tensor. Adding reasonable constraints reduces the number of model parameters and can help condition the tensor reconstruction from a small number of orientations. The macroscopic magnetic susceptibility is decomposed as a sum of molecular magnetic polarizabilities, demonstrating that macroscopic order in molecular arrangement is essential to the existence of and symmetry in susceptibility anisotropy and cylindrical symmetry is a natural outcome of an ordered molecular arrangement. Noise propagation in the susceptibility tensor reconstruction is analyzed through its condition number, showing that the tensor reconstruction is highly susceptible to the distribution of acquired subject orientations and to the tensor symmetry properties, with a substantial over- or under-estimation of susceptibility anisotropy in fiber directions not favorably oriented with respect to the acquired orientations. It was found that a careful acquisition of three non-coplanar orientations and the use of cylindrical symmetry guided by diffusion tensor imaging allowed reasonable estimation of magnetic susceptibility anisotropy in certain major white matter tracts in the human brain.

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YNIMG Journal 2012 Journal Article

Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map

Jing Liu
Tian Liu
Ludovic de Rochefort
James Ledoux
Ildar Khalidov
Weiwei Chen
A. John Tsiouris
Cynthia Wisnieff

The magnetic susceptibility of tissue can be determined in gradient echo MRI by deconvolving the local magnetic field with the magnetic field generated by a unit dipole. This Quantitative Susceptibility Mapping (QSM) problem is unfortunately ill-posed. By transforming the problem to the Fourier domain, the susceptibility appears to be undersampled only at points where the dipole kernel is zero, suggesting that a modest amount of additional information may be sufficient for uniquely resolving susceptibility. A Morphology Enabled Dipole Inversion (MEDI) approach is developed that exploits the structural consistency between the susceptibility map and the magnitude image reconstructed from the same gradient echo MRI. Specifically, voxels that are part of edges in the susceptibility map but not in the edges of the magnitude image are considered to be sparse. In this approach an L1 norm minimization is used to express this sparsity property. Numerical simulations and phantom experiments are performed to demonstrate the superiority of this L1 minimization approach over the previous L2 minimization method. Preliminary brain imaging results in healthy subjects and in patients with intracerebral hemorrhages illustrate that QSM is feasible in practice.

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