The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization

An automated methodology for whole-body, multimodality tracking of individual cancer lesions

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

Improving performance robustness of subject-based brain segmentation software

Purpose

Artificial intelligence (AI)-based image analysis tools to quantify the brain have become commercialized. However, insufficient data for learning and scanner specificity is a limitation for achieving high quality. In the present study, the performance of personalized brain segmentation software when applied to multicenter data using an AI model trained on data from a single institution was improved.

Methods

Preindicators of brain white matter (WM) information from the training dataset were utilized for preprocessing. During learning, data of cognitively normal (CN) individuals from a single center were utilized, and data of CN individuals and Alzheimer disease (AD) patients enrolled in multiple centers were considered the test set.

Results

The preprocessing based on the preindicator (dice similarity coefficient [DSC], 0.8567) resulted in a better performance than without (DSC, 0.7921). The standard deviation (SD) of the WM region intensity (DSC, 0.8303) had a more substantial influence on the performance than the average intensity (DSC, 0.6591). When the SD of the test data WM intensity was smaller than the learning data, the performance improved (0.03 increase in lower SD, 0.05 decrease in higher SD). Furthermore, preindicator-based pretreatment increased the correlation of mean cortical thickness of the entire gray matter between Atroscan and FreeSurfer, and data augmentation without preprocessing did not.Both preindicator processing and data augmentation improved the correlation coefficient from 0.7584 to 0.8165.

Conclusion

Data augmentation and preindicator-based preprocessing of training data can improve the performance of AI-based brain segmentation software, both increasing the generalizability and stability of brain segmentation software.

Generation of acoustic-Brownian noise in nuclear magnetic resonance under non-equilibrium thermal fluctuations

We present an analytical study on generation of acoustic-Brownian noise in nuclear magnetic resonance (NMR) induced as a result of thermal fluctuations of the magnetic moments under non-equilibrium thermal interactions which has not been explored independent of Nyquist–Johnson noise until now. The mechanism of physical coupling between non-equilibrium thermal fluctuations and magnetic moments is illustrated using Lighthill’s formulation on suspension dynamics. We discover that unlike Nyquist–Johnson noise which has a uniform spectral density across a range of frequencies, the spectral dependence of acoustic-Brownian noise decreases with an increase in frequency and resembles Brownian noise associated with a particle in a potential well. The results have applications in the field of image enhancement algorithm as well as noise reduction instrumentation in NMR systems.

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy

RATIONALE AND OBJECTIVES

This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy.

MATERIALS AND METHODS

We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision.

RESULTS

Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland.

CONCLUSIONS

Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols.

Magnetic Resonance Imaging (MRI) and Digital Subtraction Angiography Investigation of Childhood Moyamoya Disease

Because digital subtraction angiography (DSA) is not an ideal angiographic examination for moyamoya disease in the pediatric population, magnetic resonance angiography (MRA) provides a noninvasive contrast-free angiographic examination; whereas magnetic resonance imaging (MRI) provides superior spatial resolution and soft-tissue contrast for lesion assessment. Ninety patients with moyamoya disease were examined by MRI and DSA to assess the distribution of lesions and their diagnostic agreement between modalities. MRI examination revealed 439 lesions. Punctate lesions were the most abundant, followed by patchy lesions. These lesions generally covered a smaller area than the abnormal-vascular corresponding brain parenchyma. Steno-occlusive changes at bilateral anterior, medial, and posterior cerebral arteries were identified by MRA and DSA. MRI showed moderate agreement in identifying lesions after steno-occlusive changes in anterior and medial cerebral arteries, and good agreement in posterior cerebral arteries; 6% to 11% of cases were misdiagnosed by MRA.

Regional brain volume reduction and cognitive outcomes in preterm children at low risk at 9 years of age

OBJECTIVE

More information is needed on "low-risk" preterm infants' neurological outcome so that they can be included in follow-up programs. A prospective study was performed to examine the regional brain volume changes compared to term children and to assess the relationship between the regional brain volumes to cognitive outcome of the low-risk preterm children at 9 years of age.

PATIENTS

Subjects comprised 22 preterm children who were determined to be at low risk for neurodevelopmental deficits with a gestational age between 28 and 33 weeks without a major neonatal morbidity in the neonatal period and 24 age-matched term control children term and matched for age, sex, and parental educational and occupational status.

METHODS

Regional volumetric analysis was performed for cerebellum, hippocampus, and corpus callosum area. Cognitive outcomes of both preterm and control subjects were assessed by Weschler Intelligence Scale for Children Revised (Turkish version), and attention and executive functions were assessed by Wisconsin Card Sorting Test and Stroop Test TBAG version.

RESULTS

Low-risk preterm children showed regional brain volume reduction in cerebellum, hippocampus, and corpus callosum area and achieved statistical significance when compared with term control. When the groups were compared for all WISC-R subscale scores, preterm children at low risk had significantly lower scores on information, vocabulary, similarities, arithmetics, picture completion, block design, object assembly, and coding compared to children born at term. Preterm and term groups were compared on the Stroop Test for mistakes and corrections made on each card, the time spent for completing each card, and total mistakes and corrections. In the preterm group, we found a positive correlation between regional volumes with IQ, attention, and executive function scores. Additionally, a significant correlation was found between cerebellar volume and attention and executive function scores in the preterm group.

CONCLUSION

Low-risk preterm children achieve lower scores in neurophysiological tests than children born at term. Preterm birth itself has a significant impact on regional brain volumes and cognitive outcome of children at 9 years of age. It is a risk factor for regional brain volume reductions in preterm children with low risk for neurodevelopmental deficits. The significant interaction between cerebellar volume reduction and executive function and attention may suggest that even in preterm children at low risk can have different trajectories in the growth and development of overall brain structure.

A Neonatal Bimodal MR-CT Head Template

Neonatal MR templates are appropriate for brain structural analysis and spatial normalization. However, they do not provide the essential accurate details of cranial bones and fontanels-sutures. Distinctly, CT images provide the best contrast for bone definition and fontanels-sutures. In this paper, we present, for the first time, an approach to create a fully registered bimodal MR-CT head template for neonates with a gestational age of 39 to 42 weeks. Such a template is essential for structural and functional brain studies, which require precise geometry of the head including cranial bones and fontanels-sutures. Due to the special characteristics of the problem (which requires inter-subject inter-modality registration), a two-step intensity-based registration method is proposed to globally and locally align CT images with an available MR template. By applying groupwise registration, the new neonatal CT template is then created in full alignment with the MR template to build a bimodal MR-CT template. The mutual information value between the CT and the MR template is 1.17 which shows their perfect correspondence in the bimodal template. Moreover, the average mutual information value between normalized images and the CT template proposed in this study is 1.24±0.07. Comparing this value with the one reported in a previously published approach (0.63±0.07) demonstrates the better generalization properties of the new created template and the superiority of the proposed method for the creation of CT template in the standard space provided by MR neonatal head template. The neonatal bimodal MR-CT head template is freely downloadable from https://www.u-picardie.fr/labo/GRAMFC.

Automated Detection of Lupus White Matter Lesions in MRI

Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.

The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer's disease

We proposed a diffeomorphometry-based statistical pipeline to study the regional shape change rates of the bilateral hippocampus, amygdala, and ventricle in mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared with healthy controls (HC), using sequential magnetic resonance imaging (MRI) scans of 713 subjects (3,123 scans in total). The subgroup shape atrophy rates of the bilateral hippocampus and amygdala, as well as the expansion rates of the bilateral ventricles, for a majority of vertices were found to follow the order of AD>MCI>HC. The bilateral hippocampus and the left amygdala were subsegmented into multiple functionally meaningful subregions with the help of high-field MRI scans. The largest group differences in localized shape atrophy rates on the hippocampus were found to occur in CA1, followed by subiculum, CA2, and finally CA3/dentate gyrus, which is consistent with the neurofibrillary tangle accumulation trajectory. Highly nonuniform group differences were detected on the amygdala; vertices on the core amygdala (basolateral and lateral nucleus) revealed much larger atrophy rates, whereas those on the noncore amygdala (mainly centromedial) displayed similar or even smaller atrophy rates in AD relative to HC. The temporal horns of the ventricles were observed to have the largest localized ventricular expansion rate differences; with the AD group showing larger localized expansion rates on the anterior horn and the body part of the ventricles as well. Significant correlations were observed between the localized shape change rates of each of these six structures and the cognitive deterioration rates as quantified by the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section increase rate and the Mini Mental State Examination decrease rate.

A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies

INTRODUCTION

Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies.

METHODS

The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections.

RESULTS

Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher.

CONCLUSION

Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies.

Communicating hydrocephalus following eosinophilic meningitis is pathogenic for chronic Viliuisk encephalomyelitis in Northeastern Siberia

BACKGROUND

Viliuisk encephalomyelitis (VE) is an endemic neurological disease in Northeast Siberia and generally considered to be a chronic encephalomyelitis of unknown origin actually spreading in the Sakha (Yakutian) Republic.

METHODOLOGY AND PRINCIPLE FINDINGS

In search for the pathophysiology and causative agent of VE, we performed a cross-sectional study on clinical, serological and neuroimaging data on chronic VE patients during two medical expeditions to three villages within the Viliuiski river basin in the Republic of Sakha in 2000 and to the capital Yakutsk in 2006. The severity of the core clinical picture with predominant sensory ataxia, gait apraxia, lower limb spasticity, cognitive impairment and bladder dysfunction correlated with the degree of MRI findings showing enlargement of inner ventricular spaces as in communicating hydrocephalus. Laboratory studies revealed transient eosinophilia during the preceding acute meningitis-like phase, but no ongoing inflammatory process in the CSF. We found immune reactions against Toxocara canis in the majority of chronic VE patients but rarely in controls (P = 0.025; Fisher's exact test). Histological analysis of subacute to subchronic VE brain samples showed eosinophilic infiltrations with no signs of persistent Toxocara canis infection.

CONCLUSIONS AND SIGNIFICANCE

Our data showed that pressure by the communicating hydrocephalus as a mechanical factor is the major pathogenic mechanism in chronic VE, most likely triggered by eosinophilic meningitis. There are no signs for an ongoing inflammatory process in chronic VE. The past eosinophilic reaction in VE might be caused by Toxocara ssp. infection and might therefore represent the first hint for an initial cause leading to the development of chronic VE. Our data provide a framework for future studies and potential therapeutic interventions for this enigmatic epidemic neurological disease potentially spreading in Sakha Republic.

Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury

The primary and secondary damage to neural tissue inflicted by traumatic brain injury is a leading cause of death and disability. The secondary processes, in particular, are of great clinical interest because of their potential susceptibility to intervention. We address the dynamics of tissue degeneration in cortico-subcortical circuits after severe brain injury by assessing volume change in individual thalamic nuclei over the first six-months post-injury in a sample of 25 moderate to severe traumatic brain injury patients. Using tensor-based morphometry, we observed significant localized thalamic atrophy over the six-month period in antero-dorsal limbic nuclei as well as in medio-dorsal association nuclei. Importantly, the degree of atrophy in these nuclei was predictive, even after controlling for full-brain volume change, of behavioral outcome at six-months post-injury. Furthermore, employing a data-driven decision tree model, we found that physiological measures, namely the extent of atrophy in the anterior thalamic nucleus, were the most predictive variables of whether patients had regained consciousness by six-months, followed by behavioral measures. Overall, these findings suggest that the secondary non-mechanical degenerative processes triggered by severe brain injury are still ongoing after the first week post-trauma and target specifically antero-medial and dorsal thalamic nuclei. This result therefore offers a potential window of intervention, and a specific target region, in agreement with the view that specific cortico-thalamo-cortical circuits are crucial to the maintenance of large-scale network neural activity and thereby the restoration of cognitive function after severe brain injury.

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Performed acute and chronic structural MRI in 25 severe TBI patients

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Tensor brain morphometry (TBM) shows localized thalamic acute-to-chronic atrophy.

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Anterior, medio- and lateral-dorsal nuclei are the most significant.

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Atrophy in these nuclei predicts 6-month outcome scores (GOSe).

Cerebellar volume is linked to cognitive function in temporal lobe epilepsy: a quantitative MRI study

INTRODUCTION

Chronic intractable temporal lobe epilepsy (TLE) is associated with certain comorbidities including cognitive impairment. A less common condition among patients with TLE is intermittent explosive disorder (IED), a specific form of aggressive behavior that has been linked to low intelligence and structural pathology in the amygdala. We aimed to identify other neuroanatomical substrates of both cognitive dysfunction and IED in patients with TLE, with special focus on the cerebellum, a brain region known to participate in functional networks involved in neuropsychological and affective processes.

METHODS

Magnetic resonance imaging-based volumetric data from 60 patients with temporal lobe epilepsy (36 with and 24 without IED) were evaluated. Cerebellar, hippocampal, and total brain volumes were processed separately. In a total of 50 patients, the relationship between volumetric measurements and clinical and neuropsychological data (full-scale, verbal, and performance intelligence quotients) was analyzed.

RESULTS

Intermittent explosive disorder in patients with TLE was not significantly linked to any of the regional volumes analyzed. However, cognitive performance showed a significant association both with total brain volume and cerebellar volume measurements, whereby the left cerebellar volume showed the strongest association. A deviation from normal cerebellar volumes was related to lower intelligence. Of note, left cerebellar volume was influenced by age and duration of epilepsy. Hippocampal volumes had a minor influence on cognitive parameters.

CONCLUSION

Our findings suggest that cerebellar volume is not linked to IED in patients with TLE but is significantly associated with cognitive dysfunction. Our findings support recent hypotheses proposing that the cerebellum has a relevant functional topography.

Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI

BACKGROUND AND PURPOSE

Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels.

MATERIALS AND METHODS

We used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects.

RESULTS

To assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99% (95% CI [97%, 100%]) at the voxel level.

CONCLUSIONS

This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.

A chance-constrained programming level set method for longitudinal segmentation of lung tumors in CT

This paper presents a novel stochastic level set method for the longitudinal tracking of lung tumors in computed tomography (CT). The proposed model addresses the limitations of registration based and segmentation based methods for longitudinal tumor tracking. It combines the advantages of each approach using a new probabilistic framework, namely Chance-Constrained Programming (CCP). Lung tumors can shrink or grow over time, which can be reflected in large changes of shape, appearance and volume in CT images. Traditional level set methods with a priori knowledge about shape are not suitable since the tumors are undergoing random and large changes in shape. Our CCP level set model allows to introduce a flexible prior to track structures with a highly variable shape by permitting a constraint violation of the prior up to a specified probability level. The chance constraints are computed from two given points by the user or from segmented tumors from a reference image. The reference image can be one of the images studied or an external template. We present a numerical scheme to approximate the solution of the proposed model and apply it to track lung tumors in CT. Finally, we compare our approach with a Bayesian level set. The CCP level set model gives the best results: it is more coherent with the manual segmentation.

Change detection and classification in brain MR images using change vector analysis

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Cognitive, biochemical, and imaging profile of patients suffering from idiopathic normal pressure hydrocephalus

INTRODUCTION

It has still not been clearly established whether the cognitive deficits of idiopathic normal pressure hydrocephalus (iNPH) are caused by a disturbance in cerebrospinal fluid (CSF) dynamics or an underlying metabolic disturbance.

OBJECTIVE

To identify the possible associations between biochemical markers, the neuroimaging characteristics, and cognitive deficits of patients undergoing investigations for possible iNPH.

METHODS

A CSF sample obtained during a lumbar puncture from 10 patients with iNPH was analyzed for several biochemical markers (lactate, 8-isoprostane, vascular endothelial growth factor [VEGF], neurofilament heavy protein, glial fibrillary acidic protein, amyloid beta 1-42, and total tau). All patients underwent a battery of neuropsychological testing and imaging as part of their selection process for their suitability for CSF diversion surgical procedure. Volumetric analysis of imaging was carried out measuring the ventricular volume (VV), intracranial volume (ICV), periventricular lucencies, deep white matter hyperintensities, and white matter (WM) volume, as well as their ratios.

RESULTS

A significant negative correlation of preoperative symptom duration and total tau levels (R = -0.841, P = .002) was found. There was a significant positive correlation (R = 0.648, P = .043) between the levels of VEGF and the VV/ICV ratio. There was a significant positive correlation of the levels of glial fibrillary acidic protein and the VV/deep white matter hyperintensities ratio (R = 0.828, P = .006). A significant negative correlation was observed between the levels of neurofilament heavy protein and the VV/ICV ratio (R = -0.657, P = .039) and the WM volume (R = -0.778, P = .023). Lactate levels were lower for patients performing in the normal range on the Recognition Memory Test for faces. Patients who performed better in the Recognition Memory Test words test had higher ICV volumes. All the patients in this study showed below normal performance when the subcortical function was assessed.

CONCLUSION

The positive correlation of VEGF with the severity of ventriculomegaly may indicate that this is because of the transmantle pressure gradient; this response may not be because of hypoxia but represents an attempt at neuroregeneration. The degree of reactive gliosis correlates inversely with the severity of WM lesions. Neuronal degeneration is negatively correlated with the volume of the WM in these patients. The small association of volumetry and the cognitive profile of these patients may be consistent with a direct biochemical disturbance being responsible for the cognitive deficit observed. Ongoing studies with set protocols for neuropsychological assessment and volumetric analysis are warranted to further elucidate on the preliminary results of the current study.

Fully automated atlas-based MR imaging volumetry in Huntington disease, compared with manual volumetry

BACKGROUND AND PURPOSE

The atrophy of the caudate is considered the hallmark of HD-associated neurodegeneration and has high potential as a biomarker in structural MR imaging. This study aimed at comparing automated and manual caudate volumetry.

MATERIALS AND METHODS

In this cross-sectional volumetric study in 40 patients with HD and 30 healthy controls, a fully automated caudate measurement by ABV was used for the first time in HD and was directly compared with manual delineation as the generally accepted criterion standard of volumetry.

RESULTS

It could be shown that both techniques were able to separate patients and controls to a similar degree. The differences between the 2 volumetric measurements ranged within the limits of agreement; the systematically lower values by manual volumetry were caused by the different assessment of the dorsal caudate tail, which is hard to delineate manually.

CONCLUSIONS

ABV may be used instead of manual volumetry to quantify caudate volume loss. Additionally, the ABV technique has the advantage of being much faster, is less laborious, and is free of a subjective region-of interest definition. ABV might serve as a tool in potential future clinical trials of disease-modifying treatments in HD.

Essential head tremor is associated with cerebellar vermis atrophy: a volumetric and voxel-based morphometry MR imaging study

BACKGROUND AND PURPOSE

Our aim was to investigate the presence of brain gray matter (GM) abnormalities in patients with different forms of essential tremor (ET).

MATERIALS AND METHODS

We used optimized voxel-based morphometry (VBM) and manually traced single region-of-interest analysis in 50 patients with familial ET and in 32 healthy subjects. Thirty patients with ET had tremor of the arms (a-ET), whereas the remaining 20 patients had both arm and head tremor (h-ET).

RESULTS

VBM showed marked atrophy of the cerebellar vermis in the patients with h-ET with respect to healthy subjects (P(corrected) < .001). Patients with a-ET showed a trend toward a vermal GM volume loss that did not reach a significant difference with respect to healthy controls (P(uncorrected) < .01). The region-of-interest analysis showed a reduction of the cerebellar volume (CV) in the h-ET group (98.2 +/- 13.6 mm(3)) compared with healthy controls (110.5 +/- 15.5 mm(3), P < .012) as well as in the entire vermal area (790.3 +/- 94.5 mm(2), 898.6 +/- 170.6 mm(2), P < .04 in h-ET and control groups, respectively).

CONCLUSIONS

Atrophy of the cerebellar vermis detected in patients with h-ET strongly supports the evidence for the involvement of the cerebellum in the pathophysiology of ET. The lack of a significant CV loss observed in patients with a-ET suggests that a-ET and h-ET might represent distinct subtypes of the same disease.

Are amygdalar volume alterations in children with Tourette syndrome due to ADHD comorbidity?

Recent studies have shown that changes in the basal ganglia circuitry and limbic loops may play an important role both in Tourette syndrome (TS) and attention-deficit-hyperactivity disorder (ADHD). This study aimed to investigate in vivo possible morphological alterations of the amygdala as a key component of the limbic system. Amygdalar and total brain volumes were measured in three-dimensional magnetic resonance imaging data sets of 17 male patients with TS (mean age 11 y 8 mo [SD 2 y]; range 9-16 y) and 17 age-matched comparison children (mean age 12 y 6 mo (SD 2 y 1 mo); range 9-17 y) by volume-of-interest-based volumetry. Eight members of the TS group also fulfilled the diagnostic criteria for ADHD. A significant decrease in the left-hemispheric amygdalar volumes and in the proportions of amygdalar to total brain volumes was observed in members of the TS group compared with the comparison group. Amygdalar volumes did not correlate with tic severity, but with behavioural impairment and especially with symptoms of ADHD. The amygdalar volume reduction might be the pathoanatomical correlate of an impaired input of the amygdala to the striatum and frontal cortex. Future studies should investigate if the involvement of the amygdala is due to TS or rather caused by the genetically-linked most frequent comorbidity ADHD.

Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

Diffusion tensor MRI (DTI) has been widely used to investigate brain microstructural changes in pathological conditions as well as for normal development and aging. In particular, longitudinal changes are vital to the understanding of progression but these studies are typically designed for specific regions of interest. To analyze changes in these regions traditional statistical methods are often employed to elucidate group differences which are measured against the variability found in a control cohort. However, in some cases, rather than collecting multiple subjects into two groups, it is necessary and more informative to analyze the data for individual subjects. There is also a need for understanding changes in a single subject without prior information regarding the spatial distribution of the pathology, but no formal statistical framework exists for these voxel-wise analyses of DTI. In this study, we present PERVADE (permutation voxel-wise analysis of diffusion estimates), a whole brain analysis method for detecting localized FA changes between two separate points in time of any given subject, without any prior hypothesis about where changes might occur. Exploiting the nature of DTI that it is calculated from multiple diffusion-weighted images of each region, permutation testing, a non-parametric hypothesis testing technique, was modified for the analysis of serial DTI data and implemented for voxel-wise hypothesis tests of diffusion metric changes, as well as for suprathreshold cluster analysis to correct for multiple comparisons. We describe PERVADE in detail and present results from Monte Carlo simulation supporting the validity of the technique as well as illustrative examples from a healthy subject and patients in the early stages of multiple sclerosis.

Comparative evaluation of similarity measures for the rigid registration of multi-modal head images

Image registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc. In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such as mutual information, significantly depends on which image is the floating and which is the target.

Amygdala size in amyotrophic lateral sclerosis without dementia: an in vivo study using MRI volumetry

Background

Evidence for extra-motor involvement in non-demented patients with amyotrophic lateral sclerosis (ALS) has been provided by multiple studies, in particular neuropathological studies have demonstrated neuronal loss in the amygdala. The aim of this study was to investigate possible alterations of amygdala volumes in vivo.

Methods

Twenty-two moderately disabled patients with definite ALS without cognitive or behavioural deficits and 22 age-matched healthy controls were included. Amygdala and total brain volumes were measured by region-of-interest-based volumetry in 3-D MRI.

Results

A trend was observed with reduced amygdala size in the ALS group, since mean absolute and brain size-corrected amygdala volumes were 6.9% and 7.6% lower in the patient group compared to those in normal controls (P = 0.086 and P = 0.110), respectively.

Conclusion

Volumetrically identifiable alterations of the amygdala can be mapped in vivo and may be associated with psychopathological findings in later stages of ALS.

Application of three-dimensional magnetic resonance image registration for monitoring hip joint diseases

The purpose of this study was to estimate the accuracy of a method in which three-dimensional (3D) magnetic resonance (MR) volume registration is used for monitoring hip joint disease. Data were analyzed using a normalized cross-correlation (NCC) algorithm involving a user-selected 3D box including the proximal femur. Most of the femoral head was not included in the 3D box because it can become deformed during the course of disease. The accuracy of registration around the femoral head was evaluated using five phantoms and clinical MR data of 17 patients with hip joint disease. In the phantom experiment, registration accuracy was evaluated using four fiducial markers attached to the femoral head. In the experiment using clinical data, registration accuracy was evaluated using a landmark in the femoral head. The registration accuracy in the phantom and clinical experiment was 0.43+/-0.18 mm (S.D.) and 1.12+/-0.46 mm (S.D.), respectively. The former is a value less than half the minimum dimension of a voxel (1.25 x 1.25 x 1.0 mm). Although the latter is slightly larger than the minimum dimension of a voxel, actual errors would be smaller because of the uncertainty in landmark localization. In conclusion, the present method based on an NCC algorithm can be used to accurately register serial MR images of the femoral heads with an error on the order of a voxel. We believe that this method is sufficiently accurate for monitoring hip joint diseases.

Spontaneous regression of steroid-related osteonecrosis of the knee

It is unknown whether lesions of steroid-related osteonecrosis of the knee increase or decrease in size during the course of the disease after diagnosis. We sought to determine whether steroid-related osteonecrosis of the knee would have spontaneous changes in size, and if so, the factors affecting the change. We performed baseline and followup (minimum of 1 year) magnetic resonance imaging scans on 30 knees of 17 patients. We then used image registration techniques to match two sets of images. Lesion size change was evaluated on all contiguous pairs of matched magnetic resonance images. Fourteen Stage 1 (preradiographic stage) knees in seven patients showed spontaneous incomplete regression without subsequent collapse. These patients had early steroid-related lesions detected within 3 years after starting steroid treatment and all showed bilateral and multifocal involvement; lesion regression occurred regardless of location. The initial size and location of the lesions and discontinuing steroid administration did not seem to affect regression. Regression can occur in some patients with early steroid-related osteonecrosis of the knee, and the time between initiation of steroid treatment and its diagnosis might be the most significant predictive factor.

A neuropsychological study of patients with temporal lobe epilepsy and chronic interictal psychosis

PURPOSE

To characterize the pattern of cognitive deficits in patients with temporal lobe epilepsy (TLE) and interictal (schizophrenia-like) psychosis and to examine the relationship between neuropsychological deficits and Magnetization transfer imaging.

METHODS

Twenty patients with TLE and interictal psychosis were compared to 20 non-psychotic TLE patients. Patients were matched with respect to premorbid IQ, age and conventional MRI findings. A battery of neuropsychological tests was administered. The neuropsychological tests which showed significant group differences were used for correlational analysis with magnetization transfer ratio (MTR) which provides a quantitative measure of macromolecular structural integrity.

RESULTS

Patients with interictal psychosis were significantly more impaired on executive and semantic memory tasks than the non-psychotic TLE group. Vocabulary test scores correlated significantly with MTR reduction in the left fusiform gyrus in the psychotic but not the non-psychotic group.

DISCUSSION

In this study, patients with TLE and interictal psychosis were more cognitively impaired than non-psychotic TLE patients. Our findings suggest that the cognitive deterioration in these patients may occur as the illness progresses and the causes for this are probably multifactorial. Our study also provides further evidence that MTR may be useful in investigating structural correlates of cognitive impairment.

Differentiation of sCJD and vCJD forms by automated analysis of basal ganglia intensity distribution in multisequence MRI of the brain--definition and evaluation of new MRI-based ratios

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence magnetic resonance imaging (MRI) of the brain. One common feature of most forms of prion protein diseases is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted magnetic resonance (MR) images. We employ T1, T2, and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data are registered to a probabilistic atlas and normalized in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance with the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (sCJD), in agreement with the histological data. The algorithm permitted the classification of the intensities of abnormal signals in sCJD patient FLAIR images with a higher hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalized MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, in an attempt to create an automatic classification tool of human spongiform encephalopathies.

A protocol for evaluation of similarity measures for rigid registration

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.

Medical image registration with partial data

We have developed a general-purpose registration algorithm for medical images and volumes. The transformation between images is modeled as locally affine but globally smooth, and explicitly accounts for local and global variations in image intensities. An explicit model of missing data is also incorporated, allowing us to simultaneously segment and register images with partial or missing data. The algorithm is built upon a differential multiscale framework and incorporates the expectation maximization algorithm. We show that this approach is highly effective in registering a range of synthetic and clinical medical images.

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

In vivo three-dimensional motion analysis of the forearm with radioulnar synostosis treated by the Kanaya procedure

Attempts to separate congenital radioulnar synostosis and restore forearm rotation had been disappointing until a new mobilization technique was developed by Kanaya and colleagues using free vascularized fascio-fat graft interposition. This technique provides a functional rotation arc, but postoperative forearm motion is difficult to evaluate given the inaccuracies in determining the range of motion and rotational axis using conventional x-rays or computed tomography. This study represents an attempt to analyze in vivo three-dimensional motion of the forearm with radioulnar synostosis treated by Kanaya's operation using a markerless bone registration technique. Six patients with seven postoperative forearms (six with congenital and one with posttraumatic radioulnar synostosis) underwent 3D computed tomography with the forearm in neutral, fully pronated, and fully supinated positions. Range of motion according to this method was 30 +/- 18 degrees, significantly smaller than the 82 +/- 29 degrees from manual examination. Improvements in range of motion were significantly greater in cases without dislocation of the radial head (46 +/- 13 degrees) than in cases with dislocation (19 +/- 10 degrees). Dislocation of the radial head was also significantly correlated with an abnormal axis of rotation of the forearm.

Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change

This paper is motivated by the analysis of serial structural magnetic resonance imaging (MRI) data of the brain to map patterns of local tissue volume loss or gain over time, using registration-based deformation tensor morphometry. Specifically, we address the important confound of local tissue contrast changes which can be induced by neurodegenerative or neurodevelopmental processes. These not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. In order to address this confound we derive an approach to the voxel-wise optimization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images in a symmetric registration process. A quantitative evaluation of the method when compared to earlier approaches is included using both synthetic data and clinical imaging data. Results show a significant reduction in errors when tissue contrast changes locally between acquisitions. Finally, examples of applying the technique to map different patterns of atrophy rate in different neurodegenerative conditions is included.

A magnetization transfer imaging study in patients with temporal lobe epilepsy and interictal psychosis

BACKGROUND

Findings from previous neuropathological and neuroimaging studies in patients with epilepsy and interictal psychosis have been inconclusive, and both focal and widespread brain abnormalities have been reported. Thus, further investigation with advanced in vivo magnetic resonance imaging (MRI) techniques, such as magnetization transfer imaging, capable of detecting more subtle brain abnormalities, is warranted.

METHODS

Twenty patients with temporal lobe epilepsy and interictal psychosis were compared with 20 nonpsychotic patients. Patients were matched with respect to conventional MRI findings. Each group comprised of 10 patients with hippocampal sclerosis (6 left, 4 right) and 10 patients without focal lesions on MRI. A voxel-based analysis was used for the group comparisons.

RESULTS

Voxel-based analysis revealed significant reductions of magnetization transfer ratio (an index of signal loss derived from magnetization transfer imaging) in the left superior and middle temporal gyri in the psychotic patients for the subgroup of patients with no focal lesions on MRI. There were no significant volumetric differences between the psychotic and nonpsychotic patients.

CONCLUSIONS

Focal cortical magnetization transfer ratio abnormalities in the left temporal lobe unrelated to volume changes can be demonstrated in some temporal lobe epilepsy patients with interictal psychosis. Our findings might reflect subtle neuropathological abnormalities that are undetected by conventional MRI.

MRI volumetric and intensity analysis of the cerebellum in Parkinson's disease patients infused with glial-derived neurotrophic factor (GDNF)

BACKGROUND

A recent human therapeutic trial using intraputaminal infusion of glial cell-derived neurotrophic factor (GDNF) in Parkinson's disease (PD) was abruptly terminated, partly due to safety concerns raised by the finding of cerebellar lesions in monkeys given high-dose GDNF.

METHODS

Magnetic resonance images from nine PD patients participating in this trial were analyzed to determine whether subtle volumetric or intensity changes could be detected in the cerebellum or elsewhere following GDNF treatment for over 1 year. Subtraction images were compared to a reference standard deviation map constructed by using identically-processed paired scans from 25 normal adults. In a separate voxel-based group morphometric (VBM) analysis of the same patient images, grey matter intensity was compared between pre and post-GDNF infusion scans using a repeated measures ANOVA with family-wise error threshold of P = 0.10. Two expert readers independently reviewed serial FLAIR images from all patients.

RESULTS

(1) There were no significant cerebellar differences in any of the nine individual PD patients (difference image analysis), (2) there were no significant morphometric differences between pre- and post-GDNF scans (VBM), and (3) there were no signal abnormalities in the cerebellum detected on the FLAIR images in PD patients (clinical scan review).

CONCLUSIONS

In concert with lack of evidence of cerebellar dysfunction on clinical examination, we find no imaging evidence of cerebellar injury in human subjects undergoing chronic intracerebral GDNF infusion.

Application of 3D-MR image registration to monitor diseases around the knee joint

PURPOSE

To estimate the accuracy and consistency of a method using a voxel-based MR image registration algorithm for precise monitoring of knee joint diseases.

MATERIALS AND METHODS

Rigid body transformation was calculated using a normalized cross-correlation (NCC) algorithm involving simple manual segmentation of the bone region based on its anatomical features. The accuracy of registration was evaluated using four phantoms, followed by a consistency test using MR data from the 11 patients with knee joint disease.

RESULTS

The registration accuracy in the phantom experiment was 0.49+/-0.19 mm (SD) for the femur and 0.56+/-0.21 mm (SD) for the tibia. The consistency value in the experiment using clinical data was 0.69+/-0.25 mm (SD) for the femur and 0.77+/-0.37 mm (SD) for the tibia. These values were all smaller than a voxel (1.25 x 1.25 x 1.5 mm).

CONCLUSION

The present method based on an NCC algorithm can be used to register serial MR images of the knee joint with error on the order of a sub-voxel. This method would be useful for precisely assessing therapeutic response and monitoring knee joint diseases; normalized cross-correlation; accuracy.

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

Image change detection algorithms: a systematic survey

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

Serial imaging of the brain

Epilepsy is a heterogeneous condition and it is likely that susceptibility factors and genetic predisposition interact with acquired influences such as seizures, AEDs, cerebral insults, subclinical seizures,and ongoing neurodegenerative processes to render certain individuals selectively vulnerable to cerebral damage. There is currently in substantial evidence to suggest that neuroprotective treatments that rely entirely on their anticonvulsant properties are sufficient to prevent the development of cerebral atrophy. The development of postprocessing techniques in serial imaging studies have allowed the detection of subtle changes, and it is likely that development of more sensitive imaging techniques with higher-strength MR magnets and novel MR contrasts will expand our understanding of the factors that render an individual susceptible to hippocampal and extratemporal atrophy. This will allow a more informed assessment of the role required of neuroprotective agents in arresting the progression of cerebral damage and dysfunction.

A review of the automated detection of change in serial imaging studies of the brain

Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.

Temporal dynamics of brain anatomy

The brain changes profoundly in structure and function during development and as a result of diseases such as the dementias, schizophrenia, multiple sclerosis, and tumor growth. Strategies to measure, map, and visualize these brain changes are of immense value in basic and clinical neuroscience. Algorithms that map brain change with sufficient spatial and temporal sensitivity can also assess drugs that aim to decelerate or arrest these changes. In neuroscience studies, these tools can reveal subtle brain changes in adolescence and old age and link these changes with measurable differences in brain function and cognition. Early detection of brain change in patients at risk for dementia; tumor recurrence; or relapsing-remitting conditions, such as multiple sclerosis, is also vital for optimizing therapy. We review a variety of mathematical and computational approaches to detect structural brain change with unprecedented sensitivity, both spatially and temporally. The resulting four-dimensional (4-D) maps of brain anatomy are warehoused in population-based brain atlases. Here, statistical tools compare brain changes across subjects and across populations, adjusting for complex differences in brain structure. Brain changes in an individual can be compared with a normative database comprised of subjects matched for age, gender, and other demographic factors. These dynamic brain maps offer key biological markers for understanding disease progression and testing therapeutic response. The early detection of disease-related brain changes is also critical for possible pre-emptive intervention before the ravages of disease have set in.

A similarity measure for nonrigid volume registration using known joint distribution of targeted tissue: Application to dynamic CT data of the liver

A similarity measure for nonrigid volume registration with known joint distribution of a targeted tissue is developed to process tissue slide at the boundaries between the targeted and non-targeted tissues. Pre-segmentation of the targeted tissue is unnecessary. This measure is applied to registering volumes acquired at different time-phases in dynamic CT scans of the liver using contrast materials and can be derived for the case where only the joint distribution of the targeted tissue is known. The similarity measure is formulated as a likelihood by introducing a concept termed 'exclusivity condition' and embedded into a cost function for nonrigid registration to be combined with the smoothness term. In addition, a practical method for estimating the joint distribution of the liver from unregistered clinical CT data is described. We demonstrate experimentally that tissue slide is effectively processed by this proposed measure using simulated dynamic CT data generated from a software phantom and clinical CT data of eight patients.

A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis

Serial quantitative magnetic resonance imaging (MRI) allows the detection of subtle volumetric changes in brain volume. We used serial volumetry and voxel-based difference image analysis to quantify and characterize longitudinal changes in the hippocampus, cerebellum, and neocortex in younger and middle-age individuals. Paired volumetric MRI brain scans 3.5 years apart were performed on 90 healthy subjects 14 to 77 years old. Quantitative assessment of registered images included hippocampal volumetry, cerebellar volumetry, and automatically determined regional brain volumes. Longitudinal volume changes in three age epochs (<35, 35-54, >54 years) were compared and neocortical changes beyond regions of interest were visualized using filtered difference images. Cross-sectional analysis revealed a significant association between age and reduction in all brain volumes except hippocampal volume. Changes in normalized hippocampal and white matter volume were significantly different among the three groups. Individual analysis revealed 5 subjects with significant longitudinal volume changes lying outside the normative range. Difference image analysis showed global involutional changes in the >54 age group. Our findings suggest that cross-sectional observations in intracranial volume, cerebellar volume, and gray matter volume are likely to reflect uniform rates of volume loss or secular changes. Accelerated brain atrophy was seen from the age of 35-54 and increased rates of hippocampal atrophy from the age of 54. Our findings emphasize the importance of controlling for age effects when studying pathological brain changes over a wide age range.