Faculty Bibliography

PURPOSE/OBJECTIVE:To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. THEORY AND METHODS/METHODS:We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. RESULTS:We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports. CONCLUSION/CONCLUSIONS:The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

The purpose of this paper is to confirm previous reports that identified magnetization transfer (MT) as an inherent driver of longitudinal relaxation in brain tissue by asserting a substantial difference between the $T_1$ relaxation times of the free and the semi-solid spin pools. Further, we aim to identify an avenue towards the quantification of these relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e. with a nominal resolution of 1mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned 4 people with relapsing-remitting multiple sclerosis (MS) and 4 healthy controls with this pulse sequence and estimated $T_1^f \approx 1.90$s and $T_1^s \approx 0.327$s for the free and semi-solid spin pool of healthy WM, respectively, confirming previous reports and questioning the commonly used assumptions $T_1^s = T_1^f$ or $T_1^s = 1$s. Further, we estimated a fractional size of the semi-solid spin pool of $m_0^s \approx 0.202$, which is larger than previously assumed. An analysis of $T_1^f$ in normal appearing white matter revealed statistically significant differences between individuals with MS and controls. In conclusion, we confirm that longitudinal spin relaxation in brain tissue is dominated by MT and that the hybrid state facilitates a voxel-wise fit of the unconstrained MT model, which enables the analysis of subtle neurodegeneration.

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.

White matter fiber reconstructions based on seeking local maxima of Orientation Distribution Functions (ODFs) typically fail to identify fibers crossing at narrow angles below 45°. ODF-Fingerprinting (ODF-FP) replaces the ODF maxima localization mechanism with pattern matching, allowing the use of all information stored in ODFs. In this work, we study the ability of ODF-FP to reconstruct fibers crossing at varied angles spanning 10°-90° in physical diffusion phantoms composed of textile tubes with 0.8μm diameter, approaching the anatomical scale of axons. Our results show that ODF-FP is able to correctly identify 80 ± 8% of the crossing fibers regardless of the crossing angle and provide the highest average reconstruction accuracy.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

OBJECTIVE:Maximal safe resection is the goal of surgical treatment for high-grade glioma (HGG). Deep-seated hemispheric gliomas present a surgical challenge due to safety concerns and previously were often considered inoperable. The authors hypothesized that use of tubular retractors would allow resection of deep-seated gliomas with an acceptable safety profile. The purpose of this study was to describe surgical outcomes and survival data after resection of deep-seated HGG with stereotactically placed tubular retractors, as well as to discuss the technical advances that enable such procedures. METHODS:This is a retrospective review of 20 consecutive patients who underwent 22 resections of deep-seated hemispheric HGG with the Viewsite Brain Access System by a single surgeon. Patient demographics, survival, tumor characteristics, extent of resection (EOR), and neurological outcomes were recorded. Cannulation trajectories and planned resection volumes depended on the relative location of white matter tracts extracted from diffusion tractography. The surgical plans were designed on the Brainlab system and preoperatively visualized on the Surgical Theater virtual reality SNAP platform. Volumetric assessment of EOR was obtained on the Brainlab platform and confirmed by a board-certified neuroradiologist. RESULTS:Twenty adult patients (18 with IDH-wild-type glioblastomas and 2 with IDH-mutant grade IV astrocytomas) and 22 surgeries were included in the study. The cohort included both newly diagnosed (n = 17; 77%) and recurrent (n = 5; 23%) tumors. Most tumors (64%) abutted the ventricular system. The average preoperative and postoperative tumor volumes measured 33.1 ± 5.3 cm3 and 15.2 ± 5.1 cm3, respectively. The median EOR was 93%. Surgical complications included 2 patients (10%) who developed entrapment of the temporal horn, necessitating placement of a ventriculoperitoneal shunt; 1 patient (5%) who suffered a wound infection and pulmonary embolus; and 1 patient (5%) who developed pneumonia. In 2 cases (9%) patients developed new permanent visual field deficits, and in 5 cases (23%) patients experienced worsening of preoperative deficits. Preoperative neurological or cognitive deficits remained the same in 9 cases (41%) and improved in 7 (32%). The median overall survival was 14.4 months in all patients (n = 20) and in the newly diagnosed IDH-wild-type glioblastoma group (n = 16). CONCLUSIONS:Deep-seated HGGs, which are surgically challenging and frequently considered inoperable, are amenable to resection through tubular retractors, with an acceptable safety profile. Such cytoreductive surgery may allow these patients to experience an overall survival comparable to those with more superficial tumors.

Conventional MR imaging does not discriminate basal ganglia and thalamic internal anatomy well. Radiology reports describe anatomic locations but not specific functional structures. Functional neurosurgery uses indirect targeting based on commissural coordinates or atlases that do not fully account for individual variability. We describe innovative MR imaging sequences that improve the visualization of normal anatomy in this complex brain region and may increase our understanding of basal ganglia and thalamic function. Better visualization also may improve treatments for movement disorders and other emerging functional neurosurgery targets. We aim to provide an accessible review of the most clinically-relevant neuroanatomy within the thalamus and basal ganglia.

Human brainstem internal anatomy is intricate, complex, and essential to normal brain function. The brainstem is affected by stroke, multiple sclerosis, and most neurodegenerative diseases-a 1-mm focus of pathologic condition can have profound clinical consequences. Unfortunately, detailed internal brainstem anatomy is difficult to see with conventional MRI sequences. We review normal brainstem anatomy visualized on widely available clinical 3-T MRI scanners using fast gray matter acquisition T1 inversion recovery, probabilistic diffusion tractography, neuromelanin, and susceptibility-weighted imaging. Better anatomic localization using these recent innovations improves our ability to diagnose, localize, and treat brainstem diseases. We aim to provide an accessible review of the most clinically relevant brainstem neuroanatomy.