Yiming Xiao

Evaluating intensity normalization on MRIs of human brain with multiple sclerosis

Intensity normalization is an important pre-processing step in the study and analysis of Magnetic Resonance Images (MRI) of human brains. As most parametric supervised automatic image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. One of the fast and accurate approaches proposed for intensity normalization is that of Nyul and colleagues. In this work, we present, for the first time, an extensive validation of this approach in real clinical domain where even after intensity inhomogeneity correction that accounts for scanner-specific artifacts, the MRI volumes can be affected from variations such as data heterogeneity resulting from multi-site multi-scanner acquisitions, the presence of multiple sclerosis (MS) lesions and the stage of disease progression in the brain. Using the distributional divergence criteria, we evaluate the effectiveness of the normalization in rendering, under the distributional assumptions of segmentation approaches, intensities that are more homogenous for the same tissue type while simultaneously resulting in better tissue type separation. We also demonstrate the advantage of the decile based piece-wise linear approach on the task of MS lesion segmentation against a linear normalization approach over three image segmentation algorithms: a standard Bayesian classifier, an outlier detection based approach and a Bayesian classifier with Markov Random Field (MRF) based post-processing. Finally, to demonstrate the independence of the effectiveness of normalization from the complexity of segmentation algorithm, we evaluate the Nyul method against the linear normalization on Bayesian algorithms of increasing complexity including a standard Bayesian classifier with Maximum Likelihood parameter estimation and a Bayesian classifier with integrated data priors, in addition to the above Bayesian classifier with MRF based post-processing to smooth the posteriors. In all relevant cases, the observed results are verified for statistical relevance using significance tests.

Multicontrast multiecho FLASH MRI for targeting the subthalamic nucleus

The subthalamic nucleus (STN) is one of the most common stimulation targets for treating Parkinsons disease using deep brain stimulation (DBS). This procedure requires precise placement of the stimulating electrode. Common practice of DBS implantation utilizes microelectrode recording to locate the sites with the correct electrical response after an initial location estimate based on a universal human brain atlas that is linearly scaled to the patients anatomy as seen on the preoperative images. However, this often results in prolonged surgical time and possible surgical complications since the small-sized STN is difficult to visualize on conventional magnetic resonance (MR) images and its intersubject variability is not sufficiently considered in the atlas customization. This paper proposes a multicontrast, multiecho MR imaging (MRI) method that directly delineates the STN and other basal ganglia structures through five co-registered image contrasts (T1-weighted navigation image, R2 map, susceptibility-weighted imaging (phase, magnitude and fusion image)) obtained within a clinically acceptable time. The image protocol was optimized through both simulation and in vivo experiments to obtain the best image quality. Taking advantage of the multiple echoes and high readout bandwidths, no interimage registration is required since all images are produced in one acquisition, and image distortion and chemical shift are reduced. This MRI protocol is expected to mitigate some of the shortcomings of the state-of-the-art DBS implantation methods.

Multi-contrast unbiased MRI atlas of a Parkinson’s disease population

PurposeParkinson’s disease (PD) is the second leading neurodegenerative disease after Alzheimer’s disease. In PD research and its surgical treatment, such as deep brain stimulation (DBS), anatomical structural identification and references for spatial normalization are essential, and human brain atlases/templates are proven highly instrumental. However, two shortcomings affect current templates used for PD. First, many templates are derived from a single healthy subject that is not sufficiently representative of the PD-population anatomy. This may result in suboptimal surgical plans for DBS surgery and biased analysis for morphological studies. Second, commonly used mono-contrast templates lack sufficient image contrast for some subcortical structures (i.e., subthalamic nucleus) and biochemical information (i.e., iron content), a valuable feature in current PD research.MethodsWe employed a novel T1–T2* fusion MRI that visualizes both cortical and subcortical structures to drive groupwise registration to create co-registered multi-contrast (T1w, T2*w, T1–T2* fusion, phase, and an R2* map) unbiased templates from 15 PD patients, and a high-resolution histology-derived 3D atlas is co-registered. For validation, these templates are compared against the Colin27 template for landmark registration and midbrain nuclei segmentation.ResultsWhile the T1w, T2*w, and T1–T2* fusion templates provide excellent anatomical details for both cortical and subcortical structures, the phase and R2* map contain the biochemical features. By one-way ANOVA tests, our templates significantly (

Towards computer-assisted deep brain stimulation targeting with multiple active contacts

p<0.05

Atlas-Based segmentation of the subthalamic nucleus, red nucleus, and substantia nigra for deep brain stimulation by incorporating multiple MRI contrasts

p<0.05) outperform the Colin27 template in the registration-based tasks.ConclusionThe proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.

Automatic SWI Venography Segmentation Using Conditional Random Fields

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinsons disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre‐surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label‐fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored. Hum Brain Mapp 35:4330–4344, 2014.

A Prospective Evaluation of Computer-Assisted Deep Brain Stimulation Trajectory Planning

We present a novel method for preoperative computer-assisted deep brain stimulation (DBS) electrode targeting that takes into account the multiplicity of available contacts and their polarity. Our framework automatically evaluates the efficacy of many possible electrode orientations to optimize the interplay between the extracellular electric field, created from distinct arrangements of active contacts, and anatomical structures responsible for therapeutic and potential side effects. Experimental results on subthalamic DBS cases suggest bipolar configurations provide more flexibility and control on the spread of electric field and, consequently, are most robust to targeting imprecision. Visualization of predicted efficacy maps provides surgeons with complementary feedback that can bridge the gap between insertion safety and optimal therapeutic efficacy. Overall, this work adds a new dimension to preoperative DBS planning and suggests new insights regarding multi-target stimulation.

Optimal Gaussian mixture models of tissue intensities in brain MRI of patients with multiple-sclerosis

PurposeParkinson’s disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI.MethodsA dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1–T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique.ResultsThe proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature.ConclusionOur dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson’s disease. This method can be extended for multiple alternative image contrasts and other fields of applications.