This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for realtime settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae. Magn Reson Med 65:823-836, 2011. V C 2010 Wiley-Liss, Inc.

We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor. The image sets used in this paper consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively, and were captured from representative areas of hematoxylin and eosin-stained tissue retrieved from tissue microarray cores or whole sections. The primary contribution of this paper is to aggregate color, texture, and morphometric cues at the global and histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We compared the performance of Gaussian, k-nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm. On diagnosis, using a five-fold cross-validation estimate, an accuracy of 96.7% was obtained. On Gleason grading, the achieved accuracy of classification into low- and high-grade classes was 81.0%.

Background and Purpose Despite being the gold standard technique for stroke assessment, conventional diffusion magnetic resonance imaging (dMRI) provides only partial information about tissue microstructure. Diffusional kurtosis imaging (DKI) is an advanced dMRI method that yields, in addition to conventional diffusion information, the diffusional kurtosis (K), which may help improve characterization of tissue microstructure. In particular, this additional information permits the description of white matter (WM) in terms of WM-specific diffusion metrics (WMM). The goal of this study is to elucidate possible biophysical mechanisms underlying ischemia using these new WMM. Methods We performed a retrospective review of clinical and DKI data of forty-four acute/subacute ischemic stroke patients. Patients with a history of brain neoplasm or intracranial hemorrhages were excluded from this study. ROI analysis was performed to measure percent change of diffusion metrics in ischemic WM lesions compared to the contralateral hemisphere. Results K maps exhibit distinct ischemic lesion heterogeneity that is not apparent on apparent diffusion coefficient (ADC) maps. K metrics also have significantly higher absolute percent change than complementary conventional diffusion metrics. Our WMM reveal an increase in axonal density and a larger decrease in the intra-axonal (Da) compared to extra-axonal (De) diffusion microenvironment of the ischemic WM lesion. Conclusions The well-known decrease in the ADC of WM following ischemia is found to be mainly driven by a significant drop in Da. Our results suggest that ischemia preferentially alters intra-axonal environment, consistent with a proposed mechanism of focal enlargement of axons known as axonal swelling or beading.

BackgroundIt has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabilistic tractography and graph theory based network analysis.Methods12 patients with unilateral MTLE and hippocampal sclerosis (five left and seven right MTLE) and 26 healthy controls were studied. The connectivity of 10 bilateral limbic regions of interest was mapped with probabilistic tractography, and the probabilistic fibre density between each pair of regions was used as the measure of their weighted structural connectivity. Binary connectivity matrices were then obtained from the weighted connectivity matrix using a range of fixed density thresholds. Graph theory based properties of nodes (degree, local efficiency, clustering coefficient and betweenness centrality) and the network (global efficiency and average clustering coefficient) were calculated from the weight and binary connectivity matrices of each subject and compared between patients and controls.ResultsMTLE was associated with a regional reduction in fibre density compared with controls. Paradoxically, patients exhibited (1) increased limbic network clustering and (2) increased nodal efficiency, degree and clustering coefficient in the ipsilateral insula, superior temporal region and thalamus. There was also a significant reduction in clustering coefficient and efficiency of the ipsilateral hippocampus, accompanied by increased nodal degree.ConclusionsThese results suggest that MTLE is associated with reorganisation of the limbic system. These results corroborate the concept of MTLE as a network disease, and may contribute to the understanding of network excitability dynamics in epilepsy and MTLE.

By application of the MRI method of diffusional kurtosis imaging, a substantially increased diffusional kurtosis was observed within the cerebral ischemic lesions of three stroke subjects, 13–26 h following the onset of symptoms. This increase is interpreted as probably reflecting a higher degree of diffusional heterogeneity in the lesions when compared with normal-appearing contralateral tissue. In addition, for two of the subjects with white matter infarcts, the increase had a strong fiber tract orientational dependence. It is proposed that this effect is consistent with a large drop in the intra-axonal diffusivity, possibly related to either axonal varicosities or alterations associated with the endoplasmic reticulum.

BACKGROUND AND PURPOSE Cognitive impairment is frequent among patients with mild traumatic brain injury despite the absence of detectable damage on conventional MR imaging. In this study, the quantitative MR imaging techniques DTI, DKI, and ASL were used to measure changes in the structure and function in the thalamus and WM of patients with MTBI during a short follow-up period, to determine whether these techniques can be used to investigate relationships with cognitive performance and to predict outcome. MATERIALS AND METHODS Twenty patients with MTBI and 16 controls underwent MR imaging at 3T and a neuropsychological battery designed to yield measures for attention, concentration, executive functioning, memory, learning, and information processing. MK, FA, MD, and CBF were measured in the thalamus by using region-of-interest analysis and in WM by using tract-based spatial statistics. Analyses were performed comparing regional imaging measures of subject groups and the results of testing of their associations with neuropsychological performance. RESULTS Patients with MTBI exhibited significant differences from controls for DTI, DKI, and ASL measures in the thalamus and various WM regions both within 1 month after injury and >9 months after injury. At baseline, DTI and DKI measures in the thalamus and various WM regions were significantly associated with performance in different neuropsychological domains, and cognitive impairment was significantly associated with MK in the thalamus and FA in optic radiations. CONCLUSIONS Combined application of DTI, DKI, and ASL to study MTBI might be useful for investigating dynamic changes in the thalamus and WM as well as cognitive impairment during a short follow-up period, though the small number of patients examined did not predict outcome.

MTLE is associated with network rearrangement within, but not restricted to, the temporal lobe ipsilateral to the onset of seizures. Networks involving key components of the medial temporal lobe and structures traditionally not removed during surgery may be associated with seizure control after surgical treatment of MTLE.

BACKGROUND AND PURPOSE Along with cortical abnormalities, white matter microstructural changes such as axonal loss and myelin breakdown are implicated in the pathogenesis of Alzheimer disease. Recently, a white matter model was introduced that relates non-Gaussian diffusional kurtosis imaging metrics to characteristics of white matter tract integrity, including the axonal water fraction, the intra-axonal diffusivity, and the extra-axonal axial and radial diffusivities. MATERIALS AND METHODS This study reports these white matter tract integrity metrics in subjects with amnestic mild cognitive impairment (n = 12), Alzheimer disease (n = 14), and age-matched healthy controls (n = 15) in an effort to investigate their sensitivity, diagnostic accuracy, and associations with white matter changes through the course of Alzheimer disease. RESULTS With tract-based spatial statistics and region-of-interest analyses, increased diffusivity in the extra-axonal space (extra-axonal axial and radial diffusivities) in several white matter tracts sensitively and accurately discriminated healthy controls from those with amnestic mild cognitive impairment (area under the receiver operating characteristic curve = 0.82–0.95), while widespread decreased axonal water fraction discriminated amnestic mild cognitive impairment from Alzheimer disease (area under the receiver operating characteristic curve = 0.84). Additionally, these white matter tract integrity metrics in the body of the corpus callosum were strongly correlated with processing speed in amnestic mild cognitive impairment (r= |0.80–0.82|, P< .001). CONCLUSIONS These findings have implications for the course and spatial progression of white matter degeneration in Alzheimer disease, suggest the mechanisms by which these changes occur, and demonstrate the viability of these white matter tract integrity metrics as potential neuroimaging biomarkers of the earliest stages of Alzheimer disease and disease progression.