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Christopher P. Hess

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8 papers

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YNIMG Journal 2023 Journal Article

Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease

Jingwen Yao
Melanie A. Morrison
Angela Jakary
Sivakami Avadiappan
Yicheng Chen
Johanna Luitjens
Julia Glueck
Theresa Driscoll

Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD. We compared an iterative least-squares-based method (iLSQR), iterative methods that use regularization, single-step approaches, and deep learning-based techniques. Their performance was evaluated by comparing: (1) deviations from a multiple-orientation QSM reference; (2) visual appearance of QSM maps and the presence of artifacts; (3) susceptibility in subcortical brain regions with age; (4) regional brain susceptibility with published postmortem brain iron quantification; and (5) susceptibility in HD-affected basal ganglia regions between HD subjects and healthy controls. We found that single-step QSM methods with either total variation or total generalized variation constraints (SSTV/SSTGV) and the single-step deep learning method iQSM generally provided the best performance in terms of correlation with iron deposition and were better at differentiating between healthy controls and premanifest HD individuals, while deep learning QSM methods trained with multiple-orientation susceptibility data created QSM maps that were most similar to the multiple orientation reference and with the best visual scores.

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YNIMG Journal 2020 Journal Article

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field

Yicheng Chen
Angela Jakary
Sivakami Avadiappan
Christopher P. Hess
Janine M. Lupo

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.

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YNICL Journal 2018 Journal Article

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning

Melanie A. Morrison
Seyedmehdi Payabvash
Yicheng Chen
Sivakami Avadiappan
Mihir Shah
Xiaowei Zou
Christopher P. Hess
Janine M. Lupo

Background and purpose: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data. Materials and methods: Existing computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images. Results: The initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99]. Conclusions: This development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).

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YNIMG Journal 2016 Journal Article

NEOCIVET: Towards accurate morphometry of neonatal gyrification and clinical applications in preterm newborns

Hosung Kim
Claude Lepage
Romir Maheshwary
Seun Jeon
Alan C. Evans
Christopher P. Hess
A. James Barkovich
Duan Xu

Cerebral cortical folding becomes dramatically more complex in the fetal brain during the 3rd trimester of gestation; the process continues in a similar fashion in children who are born prematurely. To quantify this morphological development, it is necessary to extract the interface between gray matter and white matter, which is particularly challenging due to changing tissue contrast during brain maturation. We employed the well-established CIVET pipeline to extract this cortical surface, with point correspondence across subjects, using a surface-based spherical registration. We then developed a variant of the pipeline, called NEOCIVET, that quantified cortical folding using mean curvature and sulcal depth while addressing the well-known problems of poor and temporally-varying gray/white contrast as well as motion artifact in neonatal MRI. NEOCIVET includes: i) a tissue classification technique that analyzed multi-atlas texture patches using the nonlocal mean estimator and subsequently applied a label fusion approach based on a joint probability between templates, ii) neonatal template construction based on age-specific sub-groups, and iii) masking of non-interesting structures using label-fusion approaches. These techniques replaced modules that might be suboptimal for regional analysis of poor-contrast neonatal cortex. The proposed segmentation method showed more accurate results in subjects with various ages and with various degrees of motion compared to state-of-the-art methods. In the analysis of 158 preterm-born neonates, many with multiple scans (n =231; 26–40weeks postmenstrual age at scan), NEOCIVET identified increases in cortical folding over time in numerous cortical regions (mean curvature: +0. 003/week; sulcal depth: +0. 04mm/week) while folding did not change in major sulci that are known to develop early (corrected p <0. 05). The proposed pipeline successfully mapped cortical structural development, supporting current models of cerebral morphogenesis, and furthermore, revealed impairment of cortical folding in extremely preterm newborns relative to relatively late preterm newborns, demonstrating its potential to provide biomarkers of prematurity-related developmental outcome.

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YNICL Journal 2015 Journal Article

Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T

Qiuting Wen
Douglas A.C. Kelley
Suchandrima Banerjee
Janine M. Lupo
Susan M. Chang
Duan Xu
Christopher P. Hess
Sarah J. Nelson

Recent technological progress in the multiband echo planer imaging (MB EPI) technique enables accelerated MR diffusion weighted imaging (DWI) and allows whole brain, multi-b-value diffusion imaging to be acquired within a clinically feasible time. However, its applications at 7 T have been limited due to B1 field inhomogeneity and increased susceptibility artifact. It is an ongoing debate whether DWI at 7 T can be performed properly in patients, and a systematic SNR comparison for multiband spin-echo EPI between 3 T and 7 T has not been methodically studied. The goal of this study was to use MB EPI at 7 T in order to obtain 90-directional multi-shell DWI within a clinically feasible acquisition time for patients with glioma. This study included an SNR comparison between 3 T and 7 T, and the application of B1 mapping and distortion correction procedures for reducing the impact of variations in B0 and B1. The optimized multiband sequence was applied in 20 patients with glioma to generate both DTI and NODDI maps for comparison of values in tumor and normal appearing white matter (NAWM). Our SNR analysis showed that MB EPI at 7 T was comparable to that at 3 T, and the data quality acquired in patients was clinically acceptable. NODDI maps provided unique contrast within the T2 lesion that was not seen in anatomical images or DTI maps. Such contrast may reflect the complexity of tissue compositions associated with disease progression and treatment effects. The ability to consistently obtain high quality diffusion data at 7 T will contribute towards the implementation of a comprehensive brain MRI examination at ultra-high field.

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YNIMG Journal 2015 Journal Article

Effects of rejecting diffusion directions on tensor-derived parameters

Yiran Chen
Olga Tymofiyeva
Christopher P. Hess
Duan Xu

Diffusion Tensor Imaging (DTI) is adversely affected by subject motion. It is necessary to discard the corrupted images before diffusion parameter estimation. However, the consequences of rejecting those images are not well understood. In this study, we investigated the effects of excluding one or more volumes of diffusion weighted images by analyzing the changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and the primary eigenvector (V1). Based on the full set of diffusion images acquired by the Jones30 diffusion scheme, we generated incomplete sets of at least six in three different ways: random, uniform and clustered rejections. The results showed that MD was not significantly affected by rejecting diffusion directions. In the cases of random rejections, FA, AD, RD and V1 were overestimated more greatly with increasing number of rejections and the overestimations were worse in low FA regions than high FA regions. For uniform rejections, at which the remaining diffusion directions are evenly distributed on a sphere, little change was observed in FA and in V1. Clustered rejections, on the other hand, displayed the most significant overestimation of the parameters, and the resulting accuracy depended on the relative orientation of the underlying fibers with respect to the excluded directions. In practice, if diffusion direction data is excluded, it is important to note the number and location of directions rejected, in order to make a more precise analysis of the data.

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YNICL Journal 2013 Journal Article

Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images

Wei Bian
Christopher P. Hess
Susan M. Chang
Sarah J. Nelson
Janine M. Lupo

Recent interest in exploring the clinical relevance of cerebral microbleeds (CMBs) has motivated the search for a fast and accurate method to detect them. Visual inspection of CMBs on MR images is a lengthy, arduous task that is highly prone to human error because of their small size and wide distribution throughout the brain. Several computer-aided CMB detection algorithms have recently been proposed in the literature, but their diagnostic accuracy, computation time, and robustness are still in need of improvement. In this study, we developed and tested a semi-automated method for identifying CMBs on minimum intensity projected susceptibility-weighted MR images that are routinely used in clinical practice to visually identify CMBs. The algorithm utilized the 2D fast radial symmetry transform to initially detect putative CMBs. Falsely identified CMBs were then eliminated by examining geometric features measured after performing 3D region growing on the potential CMB candidates. This algorithm was evaluated in 15 patients with brain tumors who exhibited CMBs on susceptibility-weighted images due to prior external beam radiation therapy. Our method achieved heightened sensitivity and acceptable amount of false positives compared to prior methods without compromising computation speed. Its superior performance and simple, accelerated processing make it easily adaptable for detecting CMBs in the clinic and expandable to a wide array of neurological disorders.

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YNIMG Journal 2008 Journal Article

Probabilistic streamline q-ball tractography using the residual bootstrap

Jeffrey I. Berman
SungWon Chung
Pratik Mukherjee
Christopher P. Hess
Eric T. Han
Roland G. Henry

Q-ball imaging has the ability to discriminate multiple intravoxel fiber populations within regions of complex white matter architecture. This information can be used for fiber tracking; however, diffusion MR is susceptible to noise and multiple other sources of uncertainty affecting the measured orientation of fiber bundles. The proposed residual bootstrap method utilizes a spherical harmonic representation for high angular resolution diffusion imaging (HARDI) data in order to estimate the uncertainty in multimodal q-ball reconstructions. The accuracy of the q-ball residual bootstrap technique was examined through simulation. The residual bootstrap method was then used in combination with q-ball imaging to construct a probabilistic streamline fiber tracking algorithm. The residual bootstrap q-ball fiber tracking algorithm is capable of following the corticospinal tract and corpus callosum through regions of crossing white matter tracts in the centrum semiovale. This fiber tracking algorithm is an improvement upon prior diffusion tensor methods and the q-ball data can be acquired in a clinically feasible time frame.

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