Optimization of MRI-based scoring scales of brain injury severity in children with unilateral cerebral palsy.

BACKGROUND: Several scoring systems for measuring brain injury severity have been developed to standardize the classification of MRI results, which allows for the prediction of functional outcomes to help plan effective interventions for children with cerebral palsy. OBJECTIVE: The aim of this study is to use statistical techniques to optimize the clinical utility of a recently proposed template-based scoring method by weighting individual anatomical scores of injury, while maintaining its simplicity by retaining only a subset of scored anatomical regions. MATERIALS AND METHODS: Seventy-six children with unilateral cerebral palsy were evaluated in terms of upper limb motor function using the Assisting Hand Assessment measure and injuries visible on MRI using a semiquantitative approach. This cohort included 52 children with periventricular white matter injury and 24 with cortical and deep gray matter injuries. A subset of the template-derived cerebral regions was selected using a data-driven region selection algorithm. Linear regression was performed using this subset, with interaction effects excluded. RESULTS: Linear regression improved multiple correlations between MRI-based and Assisting Hand Assessment scores for both periventricular white matter (R squared increased to 0.45 from 0, P < 0.0001) and cortical and deep gray matter (0.84 from 0.44, P < 0.0001) cohorts. In both cohorts, the data-driven approach retained fewer than 8 of the 40 template-derived anatomical regions. CONCLUSION: The equal or better prediction of the clinically meaningful Assisting Hand Assessment measure using fewer anatomical regions highlights the potential of these developments to enable enhanced quantification of injury and prediction of patient motor outcome, while maintaining the clinical expediency of the scoring approach.

Distance informed Track-Weighted Imaging (diTWI): a framework for sensitising streamline information to neuropathology.

Track-Weighted Imaging (TWI), where voxel intensity is based on image metrics encoded along streamline trajectories, provides a mechanism to study white matter disease. However, with generalised streamline weighting, it is difficult to localise the precise anatomical source and spread of injury or neuropathology. This limitation can be overcome by modulating the voxel weight based on the distance of the voxel from a given anatomical location along the tract, which we term diTWI: distance informed Track-Weighted Imaging. The location of known neuropathology can be delineated on any given imaging modality (e.g. MRI or PET). To demonstrate the clinical utility of this approach, we measured tumour cell infiltration along WM fibre tracts in 13 patients with newly diagnosed glioblastoma and 1 patient with Anaplastic Astrocytoma. TWI and diTWI maps were generated using information obtained from dynamic contrast enhanced MRI (area under the curve, AUC) and diffusivity maps (ADC and FA) with tumour boundaries automatically extracted using a logistic regression classifier. The accuracy of the derived tumour volumes was compared to those generated using 3,4-dihydroxy-6-[(18)F]-fluoro-l-phenylalanine (FDOPA) PET imaging. The accuracy of the tumour volumes generated from the diTWI maps was superior to volumes derived from the TWI, geometric distance or baseline AUC, FA and ADC maps. The relative overlap and relative dissimilarity rates for the diTWI generated tumour volumes after classification were found to be 82.3±15.3% (range 69.1-91.9) and 16.9±8.8% (range 7.9-37.5), respectively. These findings show that diTWI maps provide a useful framework for localising neuropathological processes occurring along WM pathways.

Central nervous system involvement in systemic lupus erythematosus: an imaging challenge.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving multiple organs. One of the main sites of SLE morbidity is the central nervous system (CNS), specifically the brain. In this article we review several imaging modalities used for CNS examination in SLE patients. These modalities are categorized as morphological and functional. Special attention is given to magnetic resonance imaging (MRI) and its specific sequences such as diffusion-weighted imaging (DWI), diffuse tensor imaging (DTI) and magnetic resonance spectroscopy (MRS). These modalities allow us to better understand CNS involvement in SLE patients, its pathophysiology and consequences.

New spatiotemporal features for improved discrimination of benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging of the breast.

OBJECTIVES: The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast. METHODS: A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features. RESULTS: Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06. CONCLUSIONS: Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy.

Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy.

BACKGROUND: In fMRI decoding, temporal embedding of spatial features of the brain allows the incorporation of brain activity dynamics into the multivariate pattern classification process, and provides enriched information about stimulus-specific response patterns and potentially improved prediction accuracy. NEW METHOD: This study investigates the possibility of enhancing the classification performance by exploring temporal embedding, to identify the optimum combination of spatiotemporal features based on their classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby study (Haxby et al., 2001). Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 s. COMPARISON WITH EXISTING METHODS: A wide range of spatiotemporal observations were created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers prediction accuracies for these combinations were then compared with the single spatial multivariate pattern approach that uses only a single temporal observation. RESULTS: Our findings showed that, on average, spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent. CONCLUSIONS: As expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until -4 s after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design optimal approaches that incorporate spatiotemporal dependencies into feature selection for decoding.

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients.

Increasing feasibility and utility of (18)F-FDOPA PET for the management of glioma.

INTRODUCTION: Despite radical treatment therapies, glioma continues to carry with it a uniformly poor prognosis. Patients diagnosed with WHO Grade IV glioma (glioblastomas; GBM) generally succumb within two years, even those with WHO Grade III anaplastic gliomas and WHO Grade II gliomas carry prognoses of 2-5 and 2 years, respectively. PET imaging with (18)F-FDOPA allows in vivo assessment of the metabolism of glioma relative to surrounding tissues. The high sensitivity of (18)F-DOPA imaging grants utility for a number of clinical applications. METHODS: A collection of published work about (18)F-FDOPA PET was made and a critical review was discussed and written. RESULTS: A number of research papers have been published demonstrating that in conjunction with MRI, (18)F-FDOPA PET provides greater sensitivity and specificity than these modalities in detection, grading, prognosis and validation of treatment success in both primary and recurrent gliomas. In further comparisons with (11)C-MET, (18)F-FLT, (18)F-FET and MRI, (18)F-FDOPA has shown similar or better efficacy. Recently synthesis cassettes have become available, making (18)F-FDOPA more accessible. CONCLUSIONS: According to the available data, (18)F-FDOPA PET is a viable radiotracer for imaging and treatment planning of gliomas. ADVANCES IN KNOWLEDGE AND IMPLICATION FOR PATIENT CARE: (18)F-FDOPA PET appears to be a viable radiopharmaceutical for the diagnosis and treatment planning of gliomas cases, improving on that of MRI and (18)F-FDG PET.

Hypoxia imaging in gliomas with 18F-fluoromisonidazole PET: toward clinical translation.

There is significant interest in the development of improved image-guided therapy for neuro-oncology applications. Glioblastomas (GBM) in particular present a considerable challenge because of their pervasive nature, propensity for recurrence, and resistance to conventional therapies. MRI is routinely used as a guide for planning treatment strategies. However, this imaging modality is not able to provide images that clearly delineate tumor boundaries and affords only indirect information about key tumor pathophysiology. With the emergence of PET imaging with new oncology radiotracers, mapping of tumor infiltration and other important molecular events such as hypoxia is now feasible within the clinical setting. In particular, the importance of imaging hypoxia levels within the tumoral microenvironment is gathering interest, as hypoxia is known to play a central role in glioma pathogenesis and resistance to treatment. One of the hypoxia radiotracers known for its clinical utility is (18)F-fluoromisodazole ((18)F-FMISO). In this review, we highlight the typical causes of treatment failure in gliomas that may be linked to hypoxia and outline current methods for the detection of hypoxia. We also provide an overview of the growing body of studies focusing on the clinical translation of (18)F-FMISO PET imaging, strengthening the argument for the use of (18)F-FMISO hypoxia imaging to help optimize and guide treatment strategies for patients with glioblastoma.

Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method.

INTRODUCTION: Accurate knowledge of O(6)-methylguanine methyltransferase (MGMT) gene promoter subtype in patients with glioblastoma (GBM) is important for treatment. However, this test is not always available. Pre-operative diffusion MRI (dMRI) can be used to probe tumour biology using the apparent diffusion coefficient (ADC); however, its ability to act as a surrogate to predict MGMT status has shown mixed results. We investigated whether this was due to variations in the method used to analyse ADC. METHODS: We undertook a retrospective study of 32 patients with GBM who had MGMT status measured. Matching pre-operative MRI data were used to calculate the ADC within contrast enhancing regions of tumour. The relationship between ADC and MGMT was examined using two published ADC methods. RESULTS: A strong trend between a measure of 'minimum ADC' and methylation status was seen. An elevated minimum ADC was more likely in the methylated compared to the unmethylated MGMT group (U = 56, P = 0.0561). In contrast, utilising a two-mixture model histogram approach, a significant reduction in mean measure of the 'low ADC' component within the histogram was associated with an MGMT promoter methylation subtype (P < 0.0246). CONCLUSION: This study shows that within the same patient cohort, the method selected to analyse ADC measures has a significant bearing on the use of that metric as a surrogate marker of MGMT status. Thus for dMRI data to be clinically useful, consistent methods of data analysis need to be established prior to establishing any relationship with genetic or epigenetic profiling.

An algorithm for microscopic specimen delineation and focus candidate selection.

In this paper, we compare four field-of-view (FOV) metrics that, when applied to a low-resolution image of a microscope slide, are capable of both accurately delineating the specimen and selecting a subset of focus candidate FOVs required for construction of high-resolution focus map. The metrics evaluated are: threshold index (TI) that measures image intensity; normalised auto-correlation index (NACI) that measures spatial image similarity; auto-phase correlation index (APCI) that measures image phase diversity; and entropy index (EI) that measures the predictability of image intensities. Experiments are undertaken on a data set of forty slides including PAP stained Thin-prep cervical cytology and breast fine-needle aspiration slides and haematoxylin and eosin (HE) stained histology slides. These slides were scanned on an automated bright-field microscope and chosen to be indicative of a variety pathology specimens, containing artefacts such as excess coverslip glue and ink markers. Results are presented on the performance of each metric for correct ranking/segmentation of foreground (specimen) from background, and subsequently selecting focus candidate FOVs characteristic of the specimen's focal plane(s). The experimental results demonstrate that while NACI, APCI and EI are all effective at specimen delineation, only APCI is capable of effectively selecting superior focus candidates and ignoring artefacts.

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images.

BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.. PURPOSE: The purpose of this study is to introduce and validate a semiautomated method for measuring IVD height and mean sagittal area (and volume) from MR images to determine if it can replace the manual assessment and enable analyses of large MR cohorts. STUDY DESIGN/SETTING: This study compares semiautomated and manual measurements and assesses their reliability and agreement using data from repeated MR examinations. METHODS: Seven healthy asymptomatic males underwent 1.5-T MR examinations of the lumbar spine involving sagittal T2-weighted fast spin-echo images obtained at baseline, pre-exercise, and postexercise conditions. Measures of the mean height and the mean sagittal area of lumbar IVDs (L1-L2 to L4-L5) were compared for two segmentation approaches: a conventional manual method (10-15 minutes to process one IVD) and a specifically developed semiautomated method (requiring only a few mouse clicks to process each subject). RESULTS: Both methods showed strong test-retest reproducibility evaluated on baseline and pre-exercise examinations with strong intraclass correlations for the semiautomated and manual methods for mean IVD height (intraclass correlation coefficient [ICC]=0.99, 0.98) and mean IVD area (ICC=0.98, 0.99), respectively. A bias (average deviation) of 0.38 mm (4.1%, 95% confidence interval 0.18-0.59 mm) was observed between the manual and semiautomated methods for the IVD height, whereas there was no statistically significant difference for the mean IVD area (0.1%±3.5%). The semiautomated and manual methods both detected significant exercise-induced changes in IVD height (0.20 and 0.28 mm) and mean IVD area (5.7 and 8.3 mm(2)), respectively. CONCLUSIONS: The presented semiautomated method provides an alternative to time- and expertise-intensive manual procedures for analysis of larger, cross-sectional, interventional, and longitudinal MR studies for morphometric analyses of lumbar IVDs.

Amorphous regions-of-interest projection method for simplified longitudinal comparison of dynamic regions in cancer imaging.

Tumors are typically analyzed as a single unit, despite their biologically heterogeneous nature. This limits correlations that can be drawn between regional variation and treatment outcome. Furthermore, despite the availability of high resolution 3-D medical imaging techniques, local outcomes, (e.g., tumor growth), are not easily measured. This paper proposes a method that uses streamlines to divide a 3-D region of interest (e.g., tumor) into units where local properties can be measured over the paths of growth. The parameters such as directional length and mean intensity can be measured locally at sequential time points and then compared. The method is evaluated on synthetic objects, simulated tumors, and medical images of brain tumors. The evaluations suggest that the method is suitable for mapping amorphous dynamic objects.

Dual acquisition of (18)F-FMISO and (18)F-FDOPA.

Metabolic imaging using positron emission tomography (PET) has found increasing clinical use for the management of infiltrating tumours such as glioma. However, the heterogeneous biological nature of tumours and intrinsic treatment resistance in some regions means that knowledge of multiple biological factors is needed for effective treatment planning. For example, the use of (18)F-FDOPA to identify infiltrative tumour and (18)F-FMISO for localizing hypoxic regions. Performing multiple PET acquisitions is impractical in many clinical settings, but previous studies suggest multiplexed PET imaging could be viable. The fidelity of the two signals is affected by the injection interval, scan timing and injected dose. The contribution of this work is to propose a framework to explicitly trade-off signal fidelity with logistical constraints when designing the imaging protocol. The particular case of estimating (18)F-FMISO from a single frame prior to injection of (18)F-FDOPA is considered. Theoretical experiments using simulations for typical biological scenarios in humans demonstrate that results comparable to a pair of single-tracer acquisitions can be obtained provided protocol timings are carefully selected. These results were validated using a pre-clinical data set that was synthetically multiplexed. The results indicate that the dual acquisition of (18)F-FMISO and (18)F-FDOPA could be feasible in the clinical setting. The proposed framework could also be used to design protocols for other tracers.

lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain.

We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.

Denoising of dynamic contrast-enhanced MR images using dynamic nonlocal means.

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.

A new denoising method for dynamic contrast-enhanced MRI.

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. The algorithm is called Dynamic Non-Local Means and is a novel variation on the Non-Local Means (NL-Means) algorithm. It exploits the redundancy of information in the DCE-MRI sequence of images. An evaluation of the performance of the algorithm relative to six other denoising algorithms-Gaussian filtering, the original NL-Means algorithm, bilateral filtering, anisotropic diffusion filtering, the wavelets adaptive multiscale products threshold method, and the traditional wavelet thresholding method-is also presented. The evaluation was performed by two groups of expert observers-18 signal/image processing experts, and 9 clinicians (8 radiographers and 1 radiologist)-using real DCE-MRI data. The results of the evaluation provide evidence, at the alpha=0.05 level of significance, that both groups of observers deem the DNLM algorithm to perform visually better than all of the other algorithms.

An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast.

This paper presents an empirical evaluation of the goodness-of-fit (GOF) of four parametric models of contrast enhancement for dynamic resonance imaging of the breast: the Tofts, Brix, and Hayton pharmacokinetic models, and a novel empiric model. The goodness-of-fit of each model was evaluated with respect to: (i) two model-fitting algorithms (Levenberg-Marquardt and Nelder-Mead) and two fitting tolerances; and (ii) temporal resolution. In the first case the GOF was measured using data from three dynamic contrast-enhanced (DCE) MRI data sets from routine clinical examinations: one case with benign enhancement, one with malignant enhancement, and one with normal findings. Results are presented for fits to both the whole breast volume and to a selected region of interest. In the second case the GOF was measured by first fitting the models to several temporally sub-sampled versions of a custom high temporal resolution data set (subset of the breast volume containing a malignant lesion), and then comparing the fitted results to the original full temporal resolution data. Our results demonstrate that under the various optimization conditions considered, in general, both the proposed empiric model and the Hayton model fit the data equally well and that both of these models fit the data better than the Tofts and Brix models.