Multiparametric MRI tissue characterization in clinical stroke with correlation to clinical outcome: part 2

Rapid whole brain 3D T2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability

PURPOSE

To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation.

METHODS

A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T.

RESULTS

The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping.

CONCLUSION

The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.

How often does white matter hyperintensity volume regress in cerebral small vessel disease?

BACKGROUND AND OBJECTIVES

It has been suggested that white matter hyperintensity lesions (WMHs), which typically progress over time, can also regress, and that this might be associated with favorable cognitive performance. We determined the prevalence of WMH regression in patients with cerebral small vessel disease (SVD) and examined which demographic, clinical, and radiological markers were associated with this regression.

METHODS

We used semi-automated lesion marking methods to quantify WMH volume at multiple timepoints in three cohorts with symptomatic SVD; two with moderate-to-severe symptomatic SVD (the SCANS observational cohort and the control arm of the PRESERVE interventional trial) and one with mild-to-moderate SVD (the RUN DMC observational cohort). Mixed-effects ordered logistic regression models were used to test which factors predicted participants to show WMH regression.

RESULTS

No participants (0/98) in SCANS, 6/42 (14.3%) participants in PRESERVE, and 6/276 (2.2%) in RUN DMC showed WMH regression. On multivariate analysis, only lower WMH volume (OR: 0.36, 95% CI: 0.23-0.56) and better white matter microstructural integrity assessed by fractional anisotropy using diffusion tensor imaging (OR: 1.55, 95% CI: 1.07-2.24) predicted participant classification as regressor versus stable or progressor.

DISCUSSION

Only a small proportion of participants demonstrated WMH regression across the three cohorts, when a blinded standardized assessment method was used. Subjects who showed regression had less severe imaging markers of disease at baseline. Our results show that lesion regression is uncommon in SVD and unlikely to be a major factor affecting the use of WMH quantification as an outcome for clinical trials.

A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours

Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored. A cohort of 17 brain tumour patients was measured on a 3T hybrid MR-PET scanner with a standard clinical MRI protocol, to which the proposed multi-parametric diffusion protocol was subsequently added. For comparison purposes, standard perfusion and a full diffusion kurtosis protocol were acquired. Simultaneous amino-acid (¹⁸F-FET) PET enabled the identification of active tumour tissue. The metrics derived from the proposed protocol included perfusion fraction, pseudo-diffusivity, apparent diffusivity, and apparent kurtosis. These metrics were compared to the corresponding metrics from the dedicated acquisitions: cerebral blood volume and flow, mean diffusivity and mean kurtosis. Simulations were carried out to assess the influence of fitting methods and noise levels on the estimation of the parameters. The diffusion and kurtosis metrics obtained from the proposed protocol show strong to very strong correlations with those derived from the conventional protocol. However, a bias towards lower values was observed. The pseudo-perfusion parameters showed very weak to weak correlations compared to their perfusion counterparts. In conclusion, we introduce a clinically applicable protocol for measuring multiple parameters and demonstrate its relevance to pathological tissue characterisation.

MCA-DN: Multi-path convolution leveraged attention deep network for salvageable tissue detection in ischemic stroke from multi-parametric MRI

BACKGROUND AND OBJECTIVE

Accurate and timely treatment of ischemic stroke can restore the blood flow in the affected area and reduce the risk of disability and death. Identification and localisation of both direct and collateral blood flow restriction from MRI using computational intelligence play a crucial role in assisting manual diagnosis decisions in stroke treatment.

METHOD

A novel multi-path convolution leveraged attention based deep network (MCA-DN) is proposed to address this challenge. MCA-DN combines multi-path convolution derived attention making different weighted filters in each attention convolution sub-path, with interactions on the same level of abstraction. This facilitates the network to focus on voxels with enhanced weighted activations, directing to a plausible lesion. Such a proposition of acquiring attention by embedding multiple filter paths, also prioritizes the selective activation of multi-parametric MRI sequences. The multi-path convolution assisted attention block allows the network layers to gain more insights on the input tensor, enabling the expansion of hypothesis search space with a controlled parameter count.

RESULTS

The algorithm is evaluated on 139 patients of 3 datasets with 4 sub-datasets, including 2 benchmarked challenge datasets of ISLES-2015, 2017. MCA-DN achieved parametric measures of Dice similarity coefficient: 77.3 %, sensitivity: 82.8 %, and specificity: 98.8 %, for stroke segmentation, outperforming the five state-of-the-art methods in the field with encouraging success.

CONCLUSION

Competitive performance of the MCA-DN demonstrates immense potential to assist patient-specific stroke treatment planning by estimating the benefit of reperfusion.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

CT perfusion in hyper-acute ischemic stroke: the acid test for COVID-19 fear

PURPOSE

The fear of COVID-19 infection may discourage patients from going to the hospital even in case of sudden onset of disabling symptoms. There is growing evidence of the reduction of stroke admissions and higher prevalence of severe clinical presentation. Yet, no studies have investigated the perfusion pattern of acute strokes admitted during the lockdown. We aimed to evaluate the effects of the COVID-19 pandemic on hyper-acute stroke CT perfusion (CTP) pattern during the first months of the pandemic in Italy.

METHODS

In this retrospective observational study, we analyzed CTP images and clinical data of ischemic stroke patients admitted between 9 March and 2 June 2020 that underwent CTP (n = 30), to compare ischemic volumes and clinical features with stroke patients admitted during the same period in 2019 (n = 51). In particular, CTP images were processed to calculate total hypoperfused volumes, core volumes, and mismatch. The final infarct volumes were calculated on follow-up CT.

RESULTS

Significantly higher total CTP hypoperfused volume (83.3 vs 18.5 ml, p = 0.003), core volume (27.8 vs 1.0 ml, p < 0.001), and unfavorable mismatch (0.51 vs 0.91, p < 0.001) were found during the COVID-19 period compared to no-COVID-19 one. The more unfavorable perfusion pattern at admission resulted in higher infarct volume on follow-up CT during COVID-19 (35.5 vs 3.0 ml, p < 0.001). During lockdown, a reduction of stroke admissions (- 37%) and a higher prevalence of severe clinical presentation (NIHSS ≥ 10; 53% vs 36%, p = 0.029) were observed.

CONCLUSION

The results of CTP analysis provided a better insight in the higher prevalence of major severity stroke patients during the COVID-19 period.

Machine learning identifies stroke features between species

Identification and localization of ischemic stroke (IS) lesions is routinely performed to confirm diagnosis, assess stroke severity, predict disability and plan rehabilitation strategies using magnetic resonance imaging (MRI). In basic research, stroke lesion segmentation is necessary to study complex peri-infarction tissue changes. Moreover, final stroke volume is a critical outcome evaluated in clinical and preclinical experiments to determine therapy or intervention success. Manual segmentations are performed but they require a specialized skill set, are prone to inter-observer variation, are not entirely objective and are often not supported by histology. The task is even more challenging when dealing with large multi-center datasets, multiple experimenters or large animal cohorts. On the other hand, current automatized segmentation approaches often lack histological validation, are not entirely user independent, are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation.

Methods: We induced IS using the middle cerebral artery occlusion model on two rat cohorts. We acquired apparent diffusion coefficient (ADC) and T2-weighted (T2W) images at 24 h and 1-week after IS induction. Subsets of the animals at 24 h and 1-week post IS were evaluated using histology and immunohistochemistry. Using a Gaussian mixture model, we segmented voxel-wise interactions between ADC and T2W parameters at 24 h using one of the rat cohorts. We then used these segmentation results to train a random forest classifier, which we applied to the second rat cohort. The algorithms' stroke segmentations were compared to manual stroke delineations, T2W and ADC thresholding methods and the final stroke segmentation at 1-week. Volume correlations to histology were also performed for every segmentation method. Metrics of success were calculated with respect to the final stroke volume. Finally, the trained random forest classifier was tested on a human dataset with a similar temporal stroke on-set. Manual segmentations, ADC and T2W thresholds were again used to evaluate and perform comparisons with the proposed algorithms' output.

Results: In preclinical rat data our framework significantly outperformed commonly applied automatized thresholding approaches and segmented stroke regions similarly to manual delineation. The framework predicted the localization of final stroke regions in 1-week post-stroke MRI with a median Dice similarity coefficient of 0.86, Matthew's correlation coefficient of 0.80 and false positive rate of 0.04. The predicted stroke volumes also strongly correlated with final histological stroke regions (Pearson correlation = 0.88, P < 0.0001). Lastly, the stroke region characteristics identified by our framework in rats also identified stroke lesions in human brains, largely outperforming thresholding approaches in stroke volume prediction (P<0.01).

Conclusion: Our findings reveal that the segmentation produced by our proposed framework using 24 h MRI rat data strongly correlated with the final stroke volume, denoting a predictive effect. In addition, we show for the first time that the stroke imaging features can be directly translated between species, allowing identification of acute stroke in humans using the model trained on animal data. This discovery reduces the gap between the clinical and preclinical fields, unveiling a novel approach to directly co-analyze clinical and preclinical data. Such methods can provide further biological insights into human stroke and highlight the differences between species in order to help improve the experimental setups and animal models of the disease.

Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis-treated ischemic stroke: a wireless EEG study

Owing to the large inter-subject variability, early post-stroke prognosis is challenging, and objective biomarkers that can provide further prognostic information are still needed. The relation between quantitative EEG parameters in pre-thrombolysis hyper-acute phase and outcomes has still to be investigated. Hence, possible correlations between early EEG biomarkers, measured on bedside wireless EEG, and short-term/long-term functional and morphological outcomes were investigated in thrombolysis-treated strokes. EEG with a wireless device was performed in 20 patients with hyper-acute (< 4.5 h from onset) anterior ischemic stroke before reperfusion treatment. The correlations between outcome parameters (i.e., 7-day/12-month National Institutes of Health Stroke Scale NIHSS, 12-month modified Rankin Scale mRS, final infarct volume) and the pre-treatment EEG parameters were studied. Relative delta power and alpha power, delta/alpha (DAR), and (delta+theta)/(alpha+beta) (DTABR) ratios significantly correlated with NIHSS 7-day (rho = 0.80, − 0.81, 0.76, 0.75, respectively) and NIHSS 12-month (0.73, − 0.78, 0.74, 0.73, respectively), as well as with final infarct volume (0.75, − 0.70, 0.78, 0.62, respectively). A good outcome in terms of mRS ≤ 2 at 12 months was associated with DAR parameter (p = 0.008). The neurophysiological biomarkers obtained by non-invasive and portable technique as wireless EEG in the early pre-treatment phase may contribute as objective parameters to the short/long-term outcome prediction pivotal to better establish the treatment strategies.

Graphical abstract

Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering

Automated estimation of ischemic stroke evolution across different brain anatomical regions has immense potential to revolutionize stroke treatment. Multi-sequence Magnetic Resonance Imaging (MRI) techniques provide information to characterize abnormal tissues based on their anatomy and physical properties. Asymmetry of the right and left hemispheres of the brain is an important cue for abnormality estimation but using it alone is susceptible to occasional error due to self-asymmetry of the brain. A precise estimate of the symmetry axis is therefore essential for accurate asymmetry identification, which holds the key to the proposed method. The proposed symmetry determined superpixel based hierarchical clustering (SSHC) method initially estimates the lesion from inter-hemispheric asymmetry. This asymmetry further determines the thresholding parameter for hierarchically clustering the superpixels leading to an automated and accurate lesion delineation. A multi-sequence MRI based pipeline also combines the estimations from individual sequences. SSHC is evaluated on different sequences of the Loma Linda University (LLU) dataset with 26 patients and the Ischemic Stroke Lesion Segmentation (ISLES'15) dataset with 28 patients. SSHC eliminates the need for manual determination of threshold for combining the superpixel clusters and is more reliable as it derives the information from the quick estimation of asymmetry. SSHC outperforms the state-of-the-art resulting in a high Dice similarity score of 0.704±0.27 and a recall of 0.85±0.01 which are 6% and 35% respectively higher than the challenge winning method. SSHC thus demonstrates a promising potential in the automated detection of (sub-)acute adult ischemic stroke.

Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

BACKGROUND AND PURPOSE

Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps.

MATERIALS AND METHODS

Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm³).

RESULTS

An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001).

CONCLUSIONS

Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields

Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized trials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence.

Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871 ± 0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Background

Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH.

Methods

We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation.

Results

Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way.

Conclusion

Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.

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A fast method for detection of haematoma and oedema in haemorrhagic stroke is presented.

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To our knowledge, it is the first method to use magnetic resonance images only.

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The introduced method is fully automated, training-free and unsupervised.

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Comparable results with respect to human expert annotations are achieved.

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Method has potential to inform clinical practice, with increased clinical MRI use.

Stroke Longitudinal Volumetric Measures Correlate with the Behavioral Score in Non-Human Primates

Stroke is the second leading cause of death worldwide. Brain imaging data from experimental rodent stroke models suggest that size and location of the ischemic lesion relate to behavioral outcome. However, such a relationship between these two variables has not been established in Non-Human Primate (NHP) models. Thus, we aimed to evaluate whether size, location, and severity of stroke following controlled Middle Cerebral Artery Occlusion (MCAO) in NHP model correlated to neurological outcome. Forty cynomolgus macaques underwent MCAO, after four mortalities, thirty-six subjects were followed up during the longitudinal study. Structural T2 scans were obtained by magnetic resonance imaging (MRI) prior to, 48 h, and 30 days post-MCAO. Neurological function was assessed with the Non-human Primate Stroke Scale (NHPSS). T2 whole lesion volume was calculated per subject. At chronic stages, remaining brain volume was computed, and the affected hemisphere parceled into 50 regions of interest (ROIs). Whole and parceled volumetric measures were analyzed in relation to the NHPSS score. The longitudinal lesion volume evaluation showed a positive correlation with the NHPSS score, whereas the remaining brain volume negatively correlated with the NHPSS. Following ROI parcellation, NHPSS outcome correlated with frontal, temporal, occipital, and middle white matter, as well as the internal capsule, and the superior temporal and middle temporal gyri, and the caudate nucleus. These results represent an important step in stroke translational research by demonstrating close similarities between the NHP stroke model and the clinical characteristics following a human stroke and illustrating significant areas that could represent targets for novel neuroprotective strategies.

Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion

Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.

Arterial spin labeling perfusion magnetic resonance imaging of non-human primates

Non-human primates (NHPs) resemble most aspects of humans in brain physiology and anatomy and are excellent animal models for translational research in neuroscience, biomedical research and pharmaceutical development. Cerebral blood flow (CBF) offers essential physiological information of the brain to examine the abnormal functionality in NHP models with cerebral vascular diseases and neurological disorders or dementia. Arterial spin labeling (ASL) perfusion MRI techniques allow for high temporal and spatial CBF measurement and are intensively used in studies of animals and humans. In this article, current high-resolution ASL perfusion MRI techniques for quantitative evaluation of brain physiology and function in NHPs are described and their applications and limitation are discussed as well.

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates

Background

Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T₂ and diffusion MRI indices into MRI ‘tissue signatures’, (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures.

Results

An improved iterative self-organizing data analysis algorithm was used to combine T₂, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis.

Conclusion

These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

A fast multiparameter MRI approach for acute stroke assessment on a 3T clinical scanner: preliminary results in a non-human primate model with transient ischemic occlusion

Many MRI parameters have been explored and demonstrated the capability or potential to evaluate acute stroke injury, providing anatomical, microstructural, functional, or neurochemical information for diagnostic purposes and therapeutic development. However, the application of multiparameter MRI approach is hindered in clinic due to the very limited time window after stroke insult. Parallel imaging technique can accelerate MRI data acquisition dramatically and has been incorporated in modern clinical scanners and increasingly applied for various diagnostic purposes. In the present study, a fast multiparameter MRI approach including structural T1-weighted imaging (T1W), T2-weighted imaging (T2W), diffusion tensor imaging (DTI), T2-mapping, proton magnetic resonance spectroscopy, cerebral blood flow (CBF), and magnetization transfer (MT) imaging, was implemented and optimized for assessing acute stroke injury on a 3T clinical scanner. A macaque model of transient ischemic stroke induced by a minimal interventional approach was utilized for evaluating the multiparameter MRI approach. The preliminary results indicate the surgical procedure successfully induced ischemic occlusion in the cortex and/or subcortex in adult macaque monkeys (n=4). Application of parallel imaging technique substantially reduced the scanning duration of most MRI data acquisitions, allowing for fast and repeated evaluation of acute stroke injury. Hence, the use of the multiparameter MRI approach with up to five quantitative measures can provide significant advantages in preclinical or clinical studies of stroke disease.

Quantitative temporal profiles of penumbra and infarction during permanent middle cerebral artery occlusion in rats

The basic premise of neuroprotection in acute stroke is the presence of salvageable tissue, but the spatiotemporal volume profiles of the penumbra and infarction remain poorly defined in preclinical animal models of acute stroke used to evaluate therapies for clinical application. Our aim was to define these profiles using magnetic resonance imaging (MRI) quantitative cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) for dual-parameter voxel analysis in the rat suture permanent middle cerebral artery occlusion (pMCAO) model. Eleven male Sprague Dawley rats were subjected to pMCAO with MRI measurements of quantitative CBF and ADC at baseline, over the first 4 h (n=9) and at 7, 14, and 21 days (n=4). Voxel analysis of CBF and ADC was used to characterize brain tissue ischemic transitions. Penumbra, core, and hyperemic infarction volumes were significantly elevated (P<0.05) and unchanged over the first 4 h of pMCAO while the total lesion volume progressively rose. At 7, 14, and 21 days, tissue compartment transitions reflected infarction, tissue cavitation, and selective ischemic neuronal necrosis. Anatomical distribution of penumbra and core revealed marked heterogeneity with penumbra scattered within core and penumbra persisting even after 4 h of permanent MCAO.

Classification of lesion area in stroke patients during the subacute phase: a multiparametric MRI study

PURPOSE

Stroke imaging studies during the acute phase are likely to precede several vascular brain mechanisms, which have an important role in patient outcome. The aim of this study was to identify within the lesion area during the subacute phase (≥1 day) reactive tissue, which may have the potential for recovery.

METHODS

Twenty seven stroke patients from two cohorts were included. MRI performed during the subacute phase included conventional, perfusion and diffusion imaging. In cohort I, unsupervised multiparametric classification of the lesion area was performed. In cohort II threshold based classification was performed during the subacute phase, and radiological outcome was assessed at follow-up scan.

RESULTS

Three tissue classes were identified in cohort I, referred to as irreversibly damaged, intermediary, and reactive tissue. Based on threshold values defined in cohort I, the reactive tissue was identified in 11/13 patients in cohort II, and showed tissue preservation/partial recovery in 9/11 patients at follow-up scan. The irreversibly damaged tissue was identified in 7/13 patients in cohort II, and predicted tissue necrosis in all cases.

CONCLUSION

Identification of reactive tissue following stroke during the subacute phase can improve radiological assessment, contribute to the understanding of brain recovery processes and has implications for new therapeutic approaches.

A review of current imaging methods used in stroke research

Stroke is a serious healthcare problem with high mortality and long-term disability. However, to date, our ability to prevent and cure stroke remains limited. One important goal in stroke research is to identify the extent and location of lesion for treatment. In addition, accurately differentiating salvageable tissue from infarct and evaluating therapeutic efficacies are indispensible. These objectives could potentially be met with the assistance of modern neuroimaging techniques. This paper reviews current imaging methods commonly used in ischemic stroke research. These methods include positron emission tomography, computed tomography, T1 MRI, T2 MRI, diffusion and perfusion MRI, diffusion tensor imaging, blood-brain barrier permeability MRI, pH-weighted MRI, and functional MRI.

An analysis of T2 mapping on brain tumors

PURPOSE

Vasogenic edema on glioblastoma multiforme (GBM) or a metastatic brain tumor (METS) may have different T2 relaxation time values because it involves an increased water component. In this study, we assessed the diagnostic utility of T2 mapping techniques in distinguishing GBM from METS.

MATERIALS AND METHODS

We studied a glioblastoma (GBM) patient and a metastatic brain tumor (METS) patient who had not undergone previous surgery or treatment. All MR imaging was carried out using a 3.0-T whole-body unit, and axial T2 maps were generated with five TEs (TE = 20, 40, 60, 80, and 100 ms). Data were analyzed by using image processing and analysis software.

RESULTS

The T2 map of a GBM case showed that the -peritumoral area at a T2 relaxation time of 120-160 ms is prominent compared with the area at 210-240 ms. In contrast, the peritumoral area at 210-240 ms was prominent compared with the area at 120-160 ms in a METS case.

CONCLUSION

The distribution of T2 relaxation time in the peritumoral area shows different patterns in glioblastomas and metastatic brain tumors.

Magnetic resonance imaging of perfusion-diffusion mismatch in rodent and non-human primate stroke models

Stroke is a leading cause of death and long-term disability. Non-invasive magnetic resonance imaging (MRI) has been widely used for the early detection of ischemic stroke and the longitudinal monitoring of novel treatment strategies. Recent advances in MRI techniques have enabled improved sensitivity and specificity to detecting ischemic brain injury and monitoring functional recovery. This review describes recent progresses in the development and application of multimodal MRI and image analysis techniques to study experimental stroke in rats and non-human primates.

Multiparametric MRI and CT models of infarct core and favorable penumbral imaging patterns in acute ischemic stroke

BACKGROUND AND PURPOSE

Objective imaging methods to identify optimal candidates for late recanalization therapies are needed. The study goals were (1) to develop magnetic resonance imaging (MRI) and computed tomography (CT) multiparametric, voxel-based predictive models of infarct core and penumbra in acute ischemic stroke patients, and (2) to develop patient-level imaging criteria for favorable penumbral pattern based on good clinical outcome in response to successful recanalization.

METHODS

An analysis of imaging and clinical data was performed on 2 cohorts of patients (one screened with CT, the other with MRI) who underwent successful treatment for large vessel, anterior circulation stroke. Subjects were divided 2:1 into derivation and validation cohorts. Pretreatment imaging parameters independently predicting final tissue infarct and final clinical outcome were identified.

RESULTS

The MRI and CT models were developed and validated from 34 and 32 patients, using 943 320 and 1 236 917 voxels, respectively. The derivation MRI and 2-branch CT models had an overall accuracy of 74% and 80%, respectively, and were independently validated with an accuracy of 71% and 79%, respectively. The imaging criteria of (1) predicted infarct core ≤90 mL and (2) ratio of predicted infarct tissue within the at-risk region ≤70% identified patients as having a favorable penumbral pattern with 78% to 100% accuracy.

CONCLUSIONS

Multiparametric voxel-based MRI and CT models were developed to predict the extent of infarct core and overall penumbral pattern status in patients with acute ischemic stroke who may be candidates for late recanalization therapies. These models provide an alternative approach to mismatch in predicting ultimate tissue fate.

T2 relaxometry with indirect echo compensation from highly undersampled data

PURPOSE

To develop an algorithm for fast and accurate T2 estimation from highly undersampled multi-echo spin-echo data.

METHODS

The algorithm combines a model-based reconstruction with a signal decay based on the slice-resolved extended phase graph (SEPG) model with the goal of reconstructing T2 maps from highly undersampled radial multi-echo spin-echo data with indirect echo compensation. To avoid problems associated with the nonlinearity of the SEPG model, principal component decomposition is used to linearize the signal model. The proposed CUrve Reconstruction via principal component-based Linearization with Indirect Echo compensation (CURLIE) algorithm is used to estimate T2 curves from highly undersampled data. T2 maps are obtained by fitting the curves to the SEPG model.

RESULTS

Results on phantoms showed T2 biases (1.9% to 18.4%) when indirect echoes are not taken into account. The T2 biases were reduced (< 3.2%) when the CURLIE reconstruction was performed along with SEPG fitting even for high degrees of undersampling (4% sampled). Experiments in vivo for brain, liver, and heart followed the same trend as the phantoms.

CONCLUSION

The CURLIE reconstruction combined with SEPG fitting enables accurate T2 estimation from highly undersampled multi-echo spin-echo radial data thus, yielding a fast T2 mapping method without errors caused by indirect echoes.

Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal

Over the last 15 years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have been widely used to investigate different aspects of evolution of acute or subacute to late stage ischemic stroke in both human and animal data. Yet, a wave of biology-dependent and imaging-dependent issues is still untackled pointing towards the key question: "how does an ischemic stroke evolve?" Paving the way for potential answers to this question, both magnetic resonance (MRI) and CT (computed tomography) images have been used to visualize the lesion extent, either with or without spatial distinction between dead and salvageable tissue. Combining diffusion and perfusion imaging modalities may provide the possibility of predicting further tissue recovery or eventual necrosis. Going beyond these basic thresholding techniques, in this critical appraisal, we explore different semi-automatic or fully automatic 2D/3D medical image analysis methods and mathematical models applied to human, animal (rats/rodents) and/or synthetic ischemic stroke to tackle one of the following three problems: (1) segmentation of infarcted and/or salvageable (also called penumbral) tissue, (2) prediction of final ischemic tissue fate (death or recovery) and (3) dynamic simulation of the lesion core and/or penumbra evolution. To highlight the key features in the reviewed segmentation and prediction methods, we propose a common categorization pattern. We also emphasize some key aspects of the methods such as the imaging modalities required to build and test the presented approach, the number of patients/animals or synthetic samples, the use of external user interaction and the methods of assessment (clinical or imaging-based). Furthermore, we investigate how any key difficulties, posed by the evolution of stroke such as swelling or reperfusion, were detected (or not) by each method. In the absence of any imaging-based macroscopic dynamic model applied to ischemic stroke, we have insights into relevant microscopic dynamic models simulating the evolution of brain ischemia in the hope to further promising and challenging 4D imaging-based dynamic models. By depicting the major pitfalls and the advanced aspects of the different reviewed methods, we present an overall critique of their performances and concluded our discussion by suggesting some recommendations for future research work focusing on one or more of the three addressed problems.

Evaluating effects of normobaric oxygen therapy in acute stroke with MRI-based predictive models

Background

Voxel-based algorithms using acute multiparametric-MRI data have been shown to accurately predict tissue outcome after stroke. We explored the potential of MRI-based predictive algorithms to objectively assess the effects of normobaric oxygen therapy (NBO), an investigational stroke treatment, using data from a pilot study of NBO in acute stroke.

Methods

The pilot study of NBO enrolled 11 patients randomized to NBO administered for 8 hours, and 8 Control patients who received room-air. Serial MRIs were obtained at admission, during gas therapy, post-therapy, and pre-discharge. Diffusion/perfusion MRI data acquired at admission (pre-therapy) was used in generalized linear models to predict the risk of lesion growth at subsequent time points for both treatment scenarios: NBO or Control.

Results

Lesion volume sizes 'during NBO therapy' predicted by Control-models were significantly larger (P = 0.007) than those predicted by NBO models, suggesting that ischemic lesion growth is attenuated during NBO treatment. No significant difference was found between the predicted lesion volumes at later time-points. NBO-treated patients, despite showing larger lesion volumes on Control-models than NBO-models, tended to have reduced lesion growth.

Conclusions

This study shows that NBO has therapeutic potential in acute ischemic stroke, and demonstrates the feasibility of using MRI-based algorithms to evaluate novel treatments in early-phase clinical trials.

Imaging Stroke Evolution after Middle Cerebral Artery Occlusion in Non-human Primates

This article reviews imaging approaches applied to the study of stroke in nonhuman primates. We briefly survey the various surgical and minimally invasive experimental stroke models in nonhuman primates, followed by a summary of studies using computed tomography, positron emission tomography and magnetic resonance imaging and spectroscopy to monitor stroke from the hyperacute phase (within minutes of the onset of cerebral ischemia) to the chronic phase (1 month and beyond).

Magnetic resonance characterization of ischemic tissue metabolism

Magnetic resonance imaging (MRI) and spectroscopy (MRS) are versatile diagnostic techniques capable of characterizing the complex stroke pathophysiology, and hold great promise for guiding stroke treatment. Particularly, tissue viability and salvageability are closely associated with its metabolic status. Upon ischemia, ischemic tissue metabolism is disrupted including altered metabolism of glucose and oxygen, elevated lactate production/accumulation, tissue acidification and eventually, adenosine triphosphate (ATP) depletion and energy failure. Whereas metabolism impairment during ischemic stroke is complex, it may be monitored non-invasively with magnetic resonance (MR)-based techniques. Our current article provides a concise overview of stroke pathology, conventional and emerging imaging and spectroscopy techniques, and data analysis tools for characterizing ischemic tissue damage.

Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke

In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T₁-weighted – T1WI, T₂-weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).

Comparison between diffusion-weighted imaging, T2-weighted, and postcontrast T1-weighted imaging after MR-guided, high intensity, focused ultrasound treatment of uterine leiomyomata: preliminary results

PURPOSE

To investigate the comparison between diffusion-weighted imaging (DWI), T2-weighted imaging, (T2WI) and contrast T1-weighted imaging (cT1WI) in uterine leiomyoma following treatment by magnetic resonance imaging-guided, high intensity focused ultrasound surgery (MRg-HIFUS).

METHODS

Twenty one patients (45 +/- 5 yrs) with clinical symptoms of uterine leiomyoma (fibroids) were treated by MRg-HIFUS using an integrated 1.5T MRI-HIFUS system. MRI parameters consisted of DWI, T2WI, and T1-weighted fast spoiled gradient echo before and after contrast. The post-MRg-HIFUS treatment volume in the fibroid was assessed by cT1WI and DWI. Trace apparent diffusion coefficient maps were constructed for quantitative analysis. The regions of the treated uterine tissue were defined by a semisupervised segmentation method called the "eigenimage filter," using both cT1WI and DWI. Signal-to-noise ratios were determined for the T2WI pretreatment images. Segmented regions were tested by a similarity index for congruence. Descriptive, regression, and Bland-Altman statistics were calculated.

RESULTS

All the patients exhibited heterogeneously increased DWI signal intensity localized in the treated fibroid regions and were colocalized with the cT1WI defined area. The mean pretreatment T2WI signal intensity ratios were T2WI/muscle = 1.8 +/- 0.7 and T2WI/myometrium = 0.7 +/- 0.4. The congruence between the regions was significant, with a similarity of 84% and a difference of 8% between the regions. Regression analyses of the cT1WI and DWI segmented treatment volume were found to be significantly correlated (r2 = 0.94, p < 0.05) with the linear equation, (cT1WI) = 1.1 (DWI)-0.66. There is good agreement between the regions defined by cT1WI and DWI in most of the cases as shown from the Bland-Altman plots.

CONCLUSIONS

Diffusion-weighted imaging exhibited excellent agreement, congruence, and correlation with the cT1WI-defined region of treatment in uterine fibroid. Therefore, DWI could be useful as an adjunct for assessing treatment of uterine fibroids by MRg-HIFUS.

Sex, aging, and preexisting cerebral ischemic disease in patients with aortic stenosis

BACKGROUND

Patients undergoing cardiac surgery have a high frequency of preexisting cerebral ischemic lesions, the presence of which appears to predict cognitive sequelae. Patients undergoing aortic valve replacement for aortic stenosis (AS) incur an exceptionally high risk for perioperative cerebral ischemia. The extreme risk in this subgroup may arise from the preexisting burden of cerebral ischemic disease. We tested the hypotheses that increasing age, female sex, coronary artery disease, and the severity of AS are predictive of the severity of preexisting cerebral ischemic lesions.

METHODS

A total of 95 subjects were included in this study. Subjects were imaged on 1.5 Tesla magnetic resonance imaging scanners to obtain multimodal image sets which were used for the automatic segmentation of cerebral lesion volume. The dependence of lesion volume upon age, sex, coronary artery disease, and the severity of AS were tested.

RESULTS

The results demonstrate a strong correlation between aging, female sex, and white matter and ischemia-like lesion volume in patients with aortic stenosis.

CONCLUSIONS

Women and those of advanced age presenting for aortic valve replacement for AS may incur a particularly high risk for postoperative neurologic sequelae due to an exceptional preexisting burden of cerebral ischemic disease.

Development of T2-relaxation values in regional brain sites during adolescence

Brain tissue changes accompany multiple neurodegenerative and developmental conditions in adolescents. Complex processes that occur in the developing brain with disease can be evaluated accurately only against normal aging processes. Normal developmental changes in different brain areas alter tissue water content, which can be assessed by magnetic resonance (MR) T2 relaxometry. We acquired proton-density (PD) and T2-weighted images from 31 subjects (mean age±S.D., 17.4±4.9 years; 18 male), using a 3.0-T MR imaging scanner. Voxel-by-voxel T2-relaxation values were calculated, and whole-brain T2-relaxation maps constructed and normalized to a common space template. We created a set of regions of interest (ROIs) over cortical gray and white matter, basal ganglia, amygdala, thalamic, hypothalamic, pontine and cerebellar sites, with sizes of ROIs varying from 12 to 243 mm(3); regional T2-relaxation values were determined from these ROIs and normalized T2-relaxation maps. Correlations between R2 (1/T2) values in these sites and age were assessed with Pearson's correlation procedures, and gender differences in regional T2-relaxation values were evaluated with independent-samples t tests. Several brain regions, but not all, showed principally positive correlations between R2 values and age; negative correlations emerged in the cerebellar peduncles. No significant differences in T2-relaxation values emerged between males and females for those areas, except for the mid pons and left occipital white matter; males showed higher T2-relaxation values over females. The findings indicate that T2-relaxation values vary with development between brain structures, and emphasize the need to correct for such age-related effects during any determination of potential changes from control values.

Multiparametric magnetic resonance imaging of brain disorders

Magnetic resonance imaging (MRI) has been shown to improve the diagnosis and management of patients with brain disorders. Multiparametric MRI offers the possibility of noninvasively assessing multiple facets of pathophysiological processes that exist simultaneously, thereby further assisting in patient treatment management. Voxel-based analysis approaches, such as tissue theme mapping, have the benefit over volumetric approaches in being able to identify spatially heterogeneous colocalized changes on multiple parametric MR images that are not readily discernible. Tissue theme maps seem to be a promising tool for integrating the plethora of novel imaging contrasts that are being developed for the noninvasive investigation of the different stages of disease progression into easily interpretable maps of brain injury. We describe here various implementations for combining multiparametric imaging and their merits in the evaluation of brain diseases.

Correlating lesion size and location to deficits after ischemic stroke: the influence of accounting for altered peri-necrotic tissue and incidental silent infarcts

BACKGROUND

Investigators frequently quantify and evaluate the location and size of stroke lesions to help uncover cerebral anatomical correlates of deficits observed after first-ever stroke. However, it is common to discover silent infarcts such as lacunes in patients identified clinically as 'first-ever' stroke, and it is unclear if including these incidental findings may impact lesion-based investigations of brain-behaviour relationships. There is also debate concerning how to best define the boundaries of necrotic stroke lesions that blend in an ill-defined way into surrounding tissue, as it is unclear whether including this altered peri-necrotic tissue region may influence studies of brain-behaviour relationships. Therefore, for patients with clinically overt stroke, we examined whether including altered peri-necrotic tissue and incidental silent strokes influenced either lesion volume correlations with a measure of sensorimotor impairment or the anatomical localization of this impairment established using subtraction lesion analysis.

METHODS

Chronic stroke lesions of 41 patients were manually traced from digital T1-MRI to sequentially include the: necrotic lesion core, altered peri-necrotic tissue, silent lesions in the same hemisphere as the index lesion, and silent lesions in the opposite hemisphere. Lesion volumes for each region were examined for correlation with motor impairment scores, and subtraction analysis was used to highlight anatomical lesion loci associated with this deficit.

RESULTS

For subtraction lesion analysis, including peri-necrotic tissue resulted in a larger region of more frequent damage being seen in the basal ganglia. For correlational analysis, only the volume of the lesion core was significantly associated with motor impairment scores (r = -0.35, p = 0.025). In a sub-analysis of patients with small subcortical index lesions, adding silent lesions in the opposite hemisphere to the volume of the index stroke strengthened the volume-impairment association.

CONCLUSIONS

Including peri-necrotic tissue strengthened lesion localization analysis, but the influence of peri-necrotic tissue and incidental lesions on lesion volume correlations with motor impairment was negligible barring a small index lesion. Overall, the potential influence of incidental lesions and peri-necrotic tissue on brain-behaviour relationships may depend on the characteristics of the index stroke and on whether one is examining the relationship between lesion volume and impairment or lesion location and impairment.

Diffusion-weighted imaging with apparent diffusion coefficient mapping and spectroscopy in prostate cancer

Prostate cancer is a major health problem, and the exploration of noninvasive imaging methods that have the potential to improve specificity while maintaining high sensitivity is still critically needed. Tissue changes induced by tumor growth can be visualized by magnetic resonance imaging (MRI) methods. Current MRI methods include conventional T2-weighted imaging, diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping and magnetic resonance spectroscopy (MRS). Techniques such as DWI/ADC provide functional information about the behavior of water molecules in tissue; MRS can provide biochemical information about the presence or absence of certain metabolites, such as choline, creatine, and citrate. Finally, vascular parameters can be investigated using dynamic contrast-enhanced MRI. Moreover, with whole-body MRI and DWI, metastatic disease can be evaluated in 1 session and may provide a way to monitor treatment. Therefore, when combining these various methods, a multiparametric data set can be built to assist in the detection, localization, assessment of prostate cancer aggressiveness, and tumor staging. Such a comprehensive approach offers more power to evaluate prostate disease than any single measure alone. In this article, we focus on the role of DWI/ADC and MRS in the detection and characterization using both in vivo and ex vivo imaging of prostate pathology.

MRI-based thermal dosimetry and diffusion-weighted imaging of MRI-guided focused ultrasound thermal ablation of uterine fibroids

PURPOSE

To investigate tissue changes observed in diffusion-weighted imaging (DWI) and its relation to contrast imaging, thermal dosimetry, and changes in the apparent diffusion coefficient (ADC) after MRI-guided focused ultrasound surgery (MRgFUS) of uterine fibroids.

MATERIALS AND METHODS

Imaging data were analyzed from 45 fibroids in 42 women treated with MRgFUS. The areas of the hyperintense regions in DWI and of nonperfused regions in T1-weighted contrast enhanced imaging (both acquired immediately after treatment) were compared with each other and to thermal dosimetry based estimates. Changes in ADC were also calculated.

RESULTS

Hyperintense regions were observed in 35/45 fibroids in DWI. When present, the areas of these regions were comparable on average to the thermal dose estimates and to the nonperfused regions, except for in several large treatments in which the nonperfused region extended beyond the treated area. ADC increased in 19 fibroids and decreased in the others.

CONCLUSION

DWI changes, which includes changes in both in T2 and ADC, may be useful in many cases to delineate the treated region resulting from MRgFUS. However, clear DWI changes were not always observed, and in some large treatments, the extent of the nonperfused region was under estimated. ADC changes immediately after MRgFUS were unpredictable.

Predictive value of ischemic lesion volume assessed with magnetic resonance imaging for neurological deficits and functional outcome poststroke: A critical review of the literature

OBJECTIVE

Ischemic lesion volume is assumed to be an important predictor of poststroke neurological deficits and functional outcome. This critical review examines the methodological quality of MRI studies and the predictive value of hemispheric infarct volume for neurological deficits (at body function level) and functional outcome (at activities level).

METHODS

Using Medline, PiCarta, and Embase to identify studies, 13 of the 747 identified studies met the authors' inclusion criteria. Subsequently, studies were tested for adherence to the key methodological criteria for internal, statistical, and external validity. Each criterion was weighted binary, and studies with 6 points or more were judged to be valid for assessing the predictive value of MRI for outcome.

RESULTS

The 13 included studies had several methodological weaknesses with respect to internal validity, and none of them took lesion location into account. Only a few used outcome measures according to the International Classification of Functioning, Disability and Health and followed patients beyond 6 months. Correlation coefficients between MRI lesion volume and outcomes were higher for outcomes defined at body function level (National Institutes of Health Stroke Scale; median 0.67; range: 0.57-0.91) than for those defined at the level of activities (Barthel Index; median -0.49; range: -0.33 to -0.74).

CONCLUSIONS

Methodological shortcomings of most studies confound the prognostic value of MRI in predicting stroke outcome, and few studies have focused on functional outcome. Future studies should investigate the added value of MRI volume over clinical neurological variables in predicting functional outcome beyond 6 months poststroke.

Volumetric analysis of liver metastases in computed tomography with the fuzzy C-means algorithm

Tumor size is often determined from computed tomography (CT) images to assess disease progression. A study was conducted to demonstrate the advantages of the fuzzy C-means (FCM) algorithm for volumetric analysis of colorectal liver metastases in comparison with manual contouring. Intra-and interobserver variability was assessed for manual contouring and the FCM algorithm in a study involving contrast-enhanced helical CT images of 43 hypoattenuating liver lesions from 15 patients with a history of colorectal cancer. Measurement accuracy and interscan variability of the FCM and manual methods were assessed in a phantom study using paraffin pseudotumors. In the clinical imaging study, intra-and interobserver variability was reduced using the FCM algorithm as compared with manual contouring (P = 0.0070 and P = 0.0019, respectively). Accuracy of the measurement of the pseudotumor volume was improved using the FCM method as compared with the manual method (P = 0.047). Interscan variability of the pseudotumor volumes was measured using the FCM method as compared with the manual method (P = 0.04). The FCM algorithm volume was highly correlated with the manual contouring volume (r = 0.9997). Finally, the shorter time spent in calculating tumor volume using the FCM method versus the manual contouring method was marginally statistically significant (P = 0.080). These results suggest that the FCM algorithm has substantial advantages over manual contouring for volumetric measurement of colorectal liver metastases from CT.

Statistical prediction of tissue fate in acute ischemic brain injury

An algorithm was developed to statistically predict ischemic tissue fate on a pixel-by-pixel basis. Quantitative high-resolution (200 x 200 microm) cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) were measured on acute stroke rats subjected to permanent middle cerebral artery occlusion and an automated clustering (ISODATA) technique was used to classify ischemic tissue types. Probability and probability density profiles were derived from a training data set (n=6) and probability maps of risk of subsequent infarction were computed in another group of animals (n=6) as ischemia progressed. Predictions were applied to overall tissue fate. Performance measures (sensitivity, specificity, and receiver operating characteristic) showed that prediction made based on combined ADC+CBF data outperformed those based on ADC or CBF data alone. At the optimal operating points, combined ADC+CBF predicted tissue infarction with 86%+/-4% sensitivity and 89%+/-6% specificity. More importantly, probability of infarct (P(I)) for different ISODATA-derived ischemic tissue types were also computed: (1) For the 'normal' cluster in the ischemic right hemisphere, P(I) based on combined ADC+CBF data (P(I)[ADC+CBF]) accurately reflected tissue fate, whereas P(I)[ADC] and P(I)[CBF] overestimated infarct probability. (2) For the 'perfusion-diffusion mismatch' cluster, P(I)[ADC+CBF] accurately predicted tissue fate, whereas P(I)[ADC] underestimated and P(I)[CBF] overestimated infarct probability. (3) For the core cluster, P(I)[ADC+CBF], P(I)[ADC], and P(I)[CBF] prediction were high and similar ( approximately 90%). This study shows an algorithm to statistically predict overall, normal, ischemic core, and 'penumbral' tissue fate using early quantitative perfusion and diffusion information. It is suggested that this approach can be applied to stroke patients in a computationally inexpensive manner.

Uterine fibroids: diffusion-weighted MR imaging for monitoring therapy with focused ultrasound surgery--preliminary study

PURPOSE

To prospectively determine the feasibility of using diffusion-weighted (DW) imaging and apparent diffusion coefficient (ADC) mapping before (baseline) and after treatment and at 6-month follow-up to monitor magnetic resonance (MR) image-guided focused ultrasound surgical ablation of uterine fibroids.

MATERIALS AND METHODS

Informed consent was obtained from patients before treatment with our study protocol, as approved by the institutional review board, and the study complied with the Health Insurance Portability and Accountability Act. Fourteen patients (mean age, 46 years +/- 5 [standard deviation]) who underwent DW imaging were enrolled in this study, and 12 of 14 completed the inclusive MR examination with DW imaging at 6-month follow-up. Treatment was performed by one radiologist with a modified MR image-guided focused ultrasound surgical system coupled with a 1.5-T MR imager. Pre- and posttreatment and 6-month follow-up MR images were obtained by using phase-sensitive T1-weighted fast spoiled gradient-recalled acquisition, T1-weighted contrast material-enhanced, and DW imaging sequences. Total treatment time was 1-3 hours. Trace ADC maps were constructed for quantitative analysis. Regions of interest localized to areas of hyperintensity on DW images were drawn on postcontrast images, and quantitative statistics were obtained from treated and nontreated uterine tissue before and after treatment and at 6-month follow-up. Statistical analysis was performed with analysis of variance. Differences with P < .05 were considered statistically significant.

RESULTS

T1-weighted contrast-enhancing fibroids selected for treatment had no hyperintense or hypointense signal intensity changes on the DW images or ADC maps before treatment. Considerably increased signal intensity changes that were localized within the treated areas were noted on DW images. Mean baseline ADC value in fibroids was 1504 mm(-6)/sec2 +/- 290. Posttreatment ADC values for nontreated fibroid tissue (1685 mm(-6)/sec2 +/- 468) differed from posttreatment ADC values for fibroid tissue (1078 mm(-6)/sec2 +/- 293) (P = .001). A significant difference (P < .001) between ADC values for treated (1905 mm(-6)/sec2 +/- 446) and nontreated (1437 mm(-6)/sec2 +/- 270) fibroid tissue at 6-month follow-up was observed.

CONCLUSION

DW imaging and ADC mapping are feasible for identification of ablated tissue after focused ultrasound treatment of uterine fibroids.