Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas

An update on susceptibility-weighted imaging in brain gliomas

Susceptibility-weighted imaging (SWI) has become a standard component of most brain MRI protocols. While traditionally used for detecting and characterising brain hemorrhages typically associated with stroke or trauma, SWI has also shown promising results in glioma assessment. Numerous studies have highlighted SWI's role in differentiating gliomas from other brain lesions, such as primary central nervous system lymphomas or metastases. Additionally, SWI aids radiologists in non-invasively grading gliomas and predicting their phenotypic profiles. Various researchers have suggested incorporating SWI as an adjunct sequence for predicting treatment response and for post-treatment monitoring. A significant focus of these studies is on the detection of intratumoural susceptibility signals (ITSSs) in gliomas, which are indicative of microhemorrhages and vessels within the tumour. The quantity, distribution, and characteristics of these ITSSs can provide radiologists with more precise information for evaluating and characterising gliomas. Furthermore, the potential benefits and added value of performing SWI after the administration of gadolinium-based contrast agents (GBCAs) have been explored. This review offers a comprehensive, educational, and practical overview of the potential applications and future directions of SWI in the context of glioma assessment. CLINICAL RELEVANCE STATEMENT: SWI has proven effective in evaluating gliomas, especially through assessing intratumoural susceptibility signal changes, and is becoming a promising, easily integrated tool in MRI protocols for both pre- and post-treatment assessments. KEY POINTS: • Susceptibility-weighted imaging is the most sensitive sequence for detecting blood and calcium inside brain lesions. • This sequence, acquired with and without gadolinium, helps with glioma diagnosis, characterisation, and grading through the detection of intratumoural susceptibility signals. • There are ongoing challenges that must be faced to clarify the role of susceptibility-weighted imaging for glioma assessment.

Computational Fractal-Based Analysis of MR Susceptibility-Weighted Imaging (SWI) in Neuro-Oncology and Neurotraumatology

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique able to depict the magnetic susceptibility produced by different substances, such as deoxyhemoglobin, calcium, and iron. The main application of SWI in clinical neuroimaging is detecting microbleedings and venous vasculature. Quantitative analyses of SWI have been developed over the last few years, aimed to offer new parameters, which could be used as neuroimaging biomarkers. Each technique has shown pros and cons, but no gold standard exists yet. The fractal dimension (FD) has been investigated as a novel potential objective parameter for monitoring intratumoral space-filling properties of SWI patterns. We showed that SWI patterns found in different tumors or different glioma grades can be represented by a gradient in the fractal dimension, thereby enabling each tumor to be assigned a specific SWI fingerprint. Such results were especially relevant in the differentiation of low-grade versus high-grade gliomas, as well as from high-grade gliomas versus lymphomas.Therefore, FD has been suggested as a potential image biomarker to analyze intrinsic neoplastic architecture in order to improve the differential diagnosis within clinical neuroimaging, determine appropriate therapy, and improve outcome in patients.These promising preliminary findings could be extended into the field of neurotraumatology, by means of the application of computational fractal-based analysis for the qualitative and quantitative imaging of microbleedings in traumatic brain injury patients. In consideration of some evidences showing that SWI signals are correlated with trauma clinical severity, FD might offer some objective prognostic biomarkers.In conclusion, fractal-based morphometrics of SWI could be further investigated to be used in a complementary way with other techniques, in order to form a holistic understanding of the temporal evolution of brain tumors and follow-up response to treatment, with several further applications in other fields, such as neurotraumatology and cerebrovascular neurosurgery as well.

Multifractal Analysis of Brain Tumor Interface in Glioblastoma

The dynamics of tumor growth is a very complex process, generally accompanied by numerous chromosomal aberrations that determine its genetic and dynamical heterogeneity. Consequently, the tumor interface exhibits a non-regular and heterogeneous behavior often described by a single fractal dimension. A more suitable approach is to consider the tumor interface as a multifractal object that can be described by a set of generalized fractal dimensions. In the present work, detrended fluctuation and multifractal analysis are used to characterize the complexity of glioblastoma.

Fractals in Neuroimaging

Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.

Fractal Dimension Analysis in Neurological Disorders: An Overview

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Fractals, Pattern Recognition, Memetics, and AI: A Personal Journal in the Computational Neurosurgery

In this chapter, the personal journey of the author in many countries, including Italy, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, and Australia, is summarized, aimed to merge different translational fields (such as neurosurgery and the clinical neurosciences in general, biomedical engineering, mathematics, computer science, and cognitive sciences) and lay the foundations of a new field defined computational neurosurgery, with fractals, pattern recognition, memetics, and artificial intelligence as the common key words of the journey.

Fractal-Based Morphometrics of Glioblastoma

Morphometrics have been able to distinguish important features of glioblastoma from magnetic resonance imaging (MRI). Using morphometrics computed on segmentations of various imaging abnormalities, we show that the average and range of lacunarity and fractal dimension values across MRI slices can be prognostic for survival. We look at the repeatability of these metrics to multiple segmentations and how they are impacted by image resolution. We speak to the challenges to overcome before these metrics are included in clinical care, and the insight that they may provide.

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for glioma grading purposes by fusing advanced Magnetic Resonance Imaging (MRI) images. Ninety-six subjects underwent an Apparent diffusion coefficient (ADC) map and Susceptibility-weighted imaging (SWI) MRI scan. After preprocessing, the different medical image fusion methods used to fuse ADC maps and SWI were Principal Component Analysis (PCA), Structure-Aware, Discrete Cosine Harmonic Wavelet Transform (DCHWT), Deep-Convolutional Neural network (DNN), Dual-Discriminator conditional generative adversarial network (DDcGAN), and Laplacian Re-Decomposition (LRD). The Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Relative Signal Contrast (RSC) were calculated for qualitative and quantitative analysis. We found high fused image quality with LRD and DDcGAN methods. Further quantitative analysis showed that RSCs in fused images in Low-Grade glioma (LGG) were significantly higher than RSCs in High-Grade glioma (HGG) with PCA, DCHWT, LRD, and DDcGAN. The Receiver Operating Characteristic (ROC) curve test highlighted that LRD and DDcGAN have the highest performance for glioma grade classification. Our work suggests using the DDcGAN and LRD networks for glioma grade classification by fusing ADC maps and SWI images.

Chitinase-3 like-protein-1, matrix metalloproteinase -9 and positive intracranial arterial remodelling

Introduction

Positive intracranial arterial remodelling is a dilated lesion of the large intracranial vessels; however, its pathogenesis is currently unknown. Some studies have identified chitinase-3 like-protein-1 (YKL-40) and matrix metalloproteinase (MMP)-9 as circulating inflammatory factors involved in positive vascular remodelling. Herein, we aimed to investigate the relationship between changes in serum YKL-40 and MMP-9 levels and positive intracranial arterial remodelling in patients with cerebral small vessel disease (CSVD).

Methods

A total of 110 patients with CSVD were selected. Patients with brain arterial remodelling (BAR) scores >1 times the standard deviation were defined as the positive intracranial artery remodelling group (n = 21 cases), and those with BAR scores ≤1 times the standard deviation were defined as the non-positive intracranial artery remodelling group (n = 89 cases). Serum YKL-40 and MMP-9 levels were measured using an enzyme-linked immunosorbent assay kit. Factors influencing positive intracranial artery remodelling using binary logistic regression analysis and predictive value of YKL-40 and MMP-9 for positive intracranial arterial remodelling in patients with CSVD were assessed by a subject receiver operating characteristic curve.

Results

Statistically significant differences in serum YKL-40 and MMP-9 levels were observed between the positive and non-positive remodelling groups (p < 0.05). The integrated indicator (OR = 9.410, 95% CI: 3.156 ~ 28.054, P<0.01) of YKL-40 and MMP-9 levels were independent risk factors for positive intracranial arterial remodelling. The integrated indicator (OR = 3.763, 95% CI: 1.884 ~ 7.517, p < 0.01) of YKL-40 and MMP-9 were independent risk factors for positive arterial remodelling in posterior circulation, but were not significantly associated with positive arterial remodelling in anterior circulation (p > 0.05). The area under the curve for YKL-40 and MMP-9 diagnostic positive remodelling was 0.778 (95% CI: 0.692–0.865, p < 0.01) and 0.736 (95% CI: 0.636–0.837, p < 0.01), respectively.

Discussion

Elevated serum YKL-40 and MMP-9 levels are independent risk factors for positive intracranial arterial remodelling in patients with CSVD and may predict the presence of positive intracranial arterial remodelling, providing new ideas for the mechanism of its occurrence and development and the direction of treatment.

SWI by 7T MR Imaging for the Microscopic Imaging Diagnosis of Astrocytic and Oligodendroglial Tumors

BACKGROUND AND PURPOSE

Despite advances in molecular imaging, preoperative diagnosis of astrocytomas and oligodendrogliomas can be challenging. In the present study, we assessed whether 7T SWI can be used to distinguish astrocytomas and oligodendrogliomas and whether malignant grading of gliomas is possible.

MATERIALS AND METHODS

7T SWI was performed on 21 patients with gliomas before surgery with optimization for sharp visualization of the corticomedullary junction. Scoring for cortical thickening and displacement of medullary vessels, characteristic of oligodendroglial tumors, and cortical tapering, characteristic of astrocytic tumors, was performed. Additionally, characteristics of malignancy, including thickening of the medullary veins, the presence of microbleeds, and/or necrosis were scored.

RESULTS

Scoring for oligodendroglial (highest possible score, +3) and astrocytic (lowest score possible, -3) characteristics yielded a significant difference between astrocytomas and oligodendrogliomas (mean, -1.93 versus +1.71, P < .01). Scoring for malignancy was significantly different among the World Health Organization grade II (n = 10), grade III (n = 4), and grade IV (n = 7) tumors (mean, 0.20 versus 1.38 versus 2.79). Cortical thickening was observed significantly more frequently in oligodendrogliomas (P < .02), with a sensitivity of 71.4% and specificity of 85.7%; observation of tapering of the cortex was higher in astrocytomas (P < .01) with a sensitivity of 85.7% and specificity of 100%.

CONCLUSIONS

Visualization of the corticomedullary junction by 7T SWI was useful in distinguishing astrocytomas and oligodendrogliomas. Observation of tapering of the cortex was most sensitive and specific for diagnosing astrocytomas. Reliably predicting malignant grade was also possible by 7T SWI.

Development of a Cranial Suture Traction Therapy Program for Facial Asymmetry Correction Using the New Delphi Technique

Background and Objectives: We aimed to develop a cranial suture traction therapy program, a non-surgical therapeutic method for facial asymmetry correction. Materials and Methods: Six experts, including rehabilitation medicine specialists, oriental medical doctors, dentistry specialists, five experts, including Master’s or doctoral degree holders in skin care and cosmetology with more than 10 years of experience in the field, 4 experts including educators in the field of skin care, a total of 15 people participated in the validation of the development of the cranial suture traction therapy program in stages 1 to 3. Open questions were used in the primary survey. In the second survey, the results of the first survey were summarized and the degree of agreement regarding the questions in each category was presented. In the third survey, the degree of agreement for each item in the questionnaire was analyzed statistically. Results: Most of the questions attained a certain level of consensus by the experts (average of ≥ 4.0). The difference between the mean values was the highest for the third survey at 0.33 and was the lowest between the second and third surveys at 0.47. The results regarding the perceived degree of importance for each point of the evaluation in both the second and third stages of the cranial suture traction therapy program were verified using the content validity ratio. The ratio for the 13 evaluation points was within the range of 0.40−1.00; thus, the Delphi program for cranial suture traction therapy verified that the content was valid. Conclusions: As most questions attained a certain level of consensus by the experts, it can be concluded that these questions are suitable, relevant, and important. The commercialization of the cranial suture traction treatment program will contribute to the correction and prevention of facial dislocations or asymmetry, and the developed treatment will be referred to as cranial suture traction therapy (CSTT).

Ultra-High-Field MRI in the Diagnosis and Management of Gliomas: A Systematic Review

Importance:

Gliomas, tumors of the central nervous system, are classically diagnosed through invasive surgical biopsy and subsequent histopathological study. Innovations in ultra-high field (UHF) imaging, namely 7-Tesla magnetic resonance imaging (7T MRI) are advancing preoperative tumor grading, visualization of intratumoral structures, and appreciation of small brain structures and lesions.

Objective

Summarize current innovative uses of UHF imaging techniques in glioma diagnostics and treatment.

Methods

A systematic review in accordance with PRISMA guidelines was performed utilizing PubMed. Case reports and series, observational clinical trials, and randomized clinical trials written in English were included. After removing unrelated studies and those with non-human subjects, only those related to 7T MRI were independently reviewed and summarized for data extraction. Some preclinical animal models are briefly described to demonstrate future usages of ultra-high-field imaging.

Results

We reviewed 46 studies (43 human and 3 animal models) which reported clinical usages of UHF MRI in the diagnosis and management of gliomas. Current literature generally supports greater resolution imaging from 7T compared to 1.5T or 3T MRI, improving visualization of cerebral microbleeds and white and gray matter, and providing more precise localization for radiotherapy targeting. Additionally, studies found that diffusion or susceptibility-weighted imaging techniques applied to 7T MRI, may be used to predict tumor grade, reveal intratumoral structures such as neovasculature and microstructures like axons, and indicate isocitrate dehydrogenase 1 mutation status in preoperative imaging. Similarly, newer imaging techniques such as magnetic resonance spectroscopy and chemical exchange saturation transfer imaging can be performed on 7T MRI to predict tumor grading and treatment efficacy. Geometrical distortion, a known challenge of 7T MRI, was at a tolerable level in all included studies.

Conclusion

UHF imaging has the potential to preoperatively and non-invasively grade gliomas, provide precise therapy target areas, and visualize lesions not seen on conventional MRI.

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.

Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results in brain tumor segmentation tasks; however, DCNN models fail the task when applied to volumes that are different from the training dataset. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on Tumor Core and Enhancing Tumor classes, achieving a further improvement in accuracy by merging the two models together. The proposed model is not resolution-dependent, thus improving segmentation accuracy and theoretically solving some transfer learning problems related to the domain shifting, at least in terms of image resolution in the datasets.

7-T Magnetic Resonance Imaging in the Management of Brain Tumors

This article provides an overview of the current status of ultrahigh-field 7-T magnetic resonance (MR) imaging in neuro-oncology, specifically for the management of patients with brain tumors. It includes a discussion of areas across the pretherapeutic, peritherapeutic, and posttherapeutic stages of patient care where 7-T MR imaging is currently being exploited and holds promise. This discussion includes existing technical challenges, barriers to clinical integration, as well as our impression of the future role of 7-T MR imaging as a clinical tool in neuro-oncology.

High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI

Purpose

Susceptibility Weighted Imaging (SWI) has become established in the clinical investigation of stroke, microbleeds, tumor vascularization, calcification and iron deposition, but suffers from a number of shortcomings and artefacts. The goal of this study was to reduce the sensitivity of SWI to strong B₁ and B₀ inhomogeneities at ultra-high field to generate homogeneous images with increased contrast and free of common artefacts. All steps in SWI processing have been addressed −coil combination, phase unwrapping, image combination over echoes, phase filtering and homogeneity correction −and applied to an efficient bipolar multi-echo acquisition to substantially improve the quality of SWI.

Principal results

Our findings regarding the optimal individual processing steps lead us to propose a Contrast-weighted, Laplace-unwrapped, bipolar multi-Echo, ASPIRE-combined, homogeneous, improved Resolution SWI, or CLEAR-SWI. CLEAR-SWI was compared to two other multi-echo SWI methods and standard, single-echo SWI with the same acquisition time at 7 T in 10 healthy volunteers and with single-echo SWI in 13 patients with brain tumors. CLEAR-SWI had improved contrast-to-noise and homogeneity, reduced signal dropout and was not compromised by the artefacts which affected standard SWI in 10 out of 13 cases close to tumors (as assessed by expert raters), as well as generating T2* maps and phase images which can be used for Quantitative Susceptibility Mapping. In a comparison with other multi-echo SWI methods, CLEAR-SWI had the fewest artefacts, highest SNR and generally higher contrast-to-noise.

Major conclusions

CLEAR-SWI eliminates the artefacts common in standard, single-echo SWI, reduces signal dropouts and improves image homogeneity and contrast-to-noise. Applied clinically, in a study of brain tumor patients, CLEAR-SWI was free of the artefacts which affected standard, single-echo SWI.

Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection

BACKGROUND AND PURPOSE

Glioblastoma (GBM) is an aggressive primary CNS neoplasm with poor overall survival (OS) despite standard of care. On MRI, GBM is usually characterized by an enhancing portion (CET) (surgery target) and a nonenhancing surrounding (NET). Extent of resection is a long debated issue in GBM, with recent evidence suggesting that both CET and NET should be resected in <65 years old patients, regardless of other risk factors (i.e., molecular biomarkers). Our aim was to test a radiomic model for patient survival stratification in <65 years old patients, by analyzing MRI features of NET, to aid tumor resection.

METHODS

Sixty-eight <65 years old GBM patients, with extensive CET resection, were selected. Resection was evaluated by manually segmenting CET on volumetric T1-weighted MRI pre and postsurgery (within 72 h). All patients underwent the same treatment protocol including chemoradiation. NET radiomic features were extracted with a custom version of Pyradiomics. Feature selection was performed with principal component analysis (PCA) and its effect on survival tested with Cox regression model. Twelve months OS discrimination was tested by t-test followed by logistic regression. Statistical significance was set at p<0.05. The most relevant features were identified from the component matrix.

RESULTS

Five PCA components (PC1-5) explained 90% of the variance. PC5 resulted significant in the Cox model (p = 0.002; exp(B) = 0.686), at t-test (p = 0.002) and logistic regression analysis (p = 0.006). Apparent diffusion coefficient (ADC)-based features were the most significant for patient survival stratification.

CONCLUSIONS

ADC radiomic features on NET predict survival after standard therapy and could be used to improve patient selection for more extensive surgery.

Recognition of Brain Metastases Using Gadolinium-Enhanced SWI MRI: Proof-of-Concept Study

Background and purpose: SWI MRI, a T2^*-dominant MRI sequence with T1 shine-through effect, uses intrinsic structural susceptibility to create enhancement among brain structures. We evaluated whether gadolinium-enhanced SWI (SWI-Gd) improves brain metastasis detection in combination with other MRI sequences.

Materials and methods: MRI images of 24 patients (46 studies) were prospectively acquired using a 1.5-T scanner. T1-weighted, unenhanced SWI (SWI-U) and SWI-Gd were evaluated blindly to clinical features by two board-certified radiologists.

Results: SWI-Gd revealed more significant metastatic lesions than either T1-Gd or SWI-U (p = 0.0004 for either comparator sequence). Moreover, SWI-Gd revealed more lesions only for those patients with ≤5 lesions on T1-Gd (n = 30 studies from 16 patients; p = 0.046). Performing SWI-Gd added <5 min of scanning time with no further additional risk.

Conclusions: Our findings suggest that, when added to T1-Gd and other common sequences, SWI-Gd may improve the diagnostic yield of brain metastases with only a few extra minutes of scanning time and no further risk than that of a regular gadolinium-enhanced MRI.

Clinical Value of Susceptibility Weighted Imaging of Brain Metastases

MRI is used for screening, initial diagnosis and follow-up of brain metastases. Multiparametric MRI protocols encompass an array of image sequences to depict key aspects of metastases morphology and biology. Given the recent safety concerns of Gd-administration and the retention of linear Gd-agents in the brain, non-contrast sequences are currently evaluated regarding their diagnostic value for brain imaging studies. Susceptibility weighted imaging has been established as a valuable clinical and research tool that is heavily used in clinical practice and utilized in diverse pathologies ranging from neuroinflammation, neurovascular disease to neurooncology. We review the value of SWI in the field of brain metastases with an emphasis on its role in early diagnosis, determination of the primary tumor entity, treatment monitoring and discuss therapy-associated changes that can affect SWI. We also review recent insights on the role of “isolated SWI signals” and the controversy on the specificity of SWI for the early detection of brain metastases.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Multi-parametric effect in predicting tumor histological grade by using susceptibility weighted magnetic resonance imaging in tongue squamous cell carcinoma

BACKGROUND

Susceptibility weighted imaging (SWI) is helpful for depicting hemorrhage, calcification, and increased vascularity in some neoplasms, which may reflect tumor grade. In this study, we aimed to apply SWI in patients with oral tongue squamous cell carcinomas (OTSCCs) and relate multi-parametric effect to tumor histological grade prediction.

METHODS

Preoperative MR examinations were performed on a 1 .5T MRI scanner with T1-, T2- and contrast-enhanced (CE) T1-weighted imaging. In addition to routine head and neck MRI sequences, SWI was performed. Tumor thickness and volume were measured. Intratumoral susceptibility signal intensities (ITSSs), ITSS score and ITSS ratio on SWI were evaluated and recorded. Subjects were sub-grouped into low- and high-grade according to the histological findings post operation. Parameters such as tumor thickness, tumor volume and three ITSS related parameters were compared between low- and high-grade groups. ROC analysis was performed on above parameters to access the capability in predicting tumor histological grade. Different multi-parametric models were run to access multi-parametric combination effect.

RESULTS

Thirty patients with OTSCC were finally included in the study. Twenty of them were categorized as low-grade SCC and the other ten subjects were high-grade SCC according to the pathologic findings. No significant difference was seen for tumor thickness or tumor volume between two sub-groups. ITSSs were seen in 23/30 patients. Significant difference of ITSS scores between low- and high-grade OTSCCs was observed, with mean value of 0.95 ± 0.83 and 1.70 ± 0.95, respectively. Univariate ROC analysis demonstrated ITSSs, ITSS score and ITSS ratio were valuable parameters for predicting tumor histological grade and ITSSs was superior to the other two parameters, with an area under ROC curve of 0.790. Multi-parametric model using combination of ITSSs and tumor thickness would greatly improve the predictive capability in comparison with a univariate approach, yielding the area under ROC curve of 0.84(0.69,0.99). On contrast-enhanced SWI (CE-SWI), ITSSs were shown more clearly delineated in comparison with non-contrast enhanced SWI.

CONCLUSIONS

In conclusion, SWI was superior in depiction of internal characteristics of OTSCCs, which would potentially provide more diagnostic information. Multi-parametric model using combination of ITSSs and tumor thickness would be valuable in predicting tumor histological grade.

Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high field

In recent years, ultra-high field MRI (7 T and above) has received more interest for clinical imaging. Indeed, a number of studies have shown the benefits from the application of this powerful tool not only for research purposes, but also in realms of improved diagnostics and patient management. The increased signal-to-noise ratio and higher spatial resolution compared with conventional and high-field clinical scanners allow imaging of small anatomical detail and subtle pathological findings. Furthermore, greater spectral resolution achieved at ultra-high field allows the resolution of metabolites for MR spectroscopic imaging. All these advantages have a significant impact on many neurological diseases, including multiple sclerosis, cerebrovascular disease, brain tumors, epilepsy and neurodegenerative diseases, in part because the pathology can be subtle and lesions small in these diseases, therefore having higher signal and resolution will help lesion detection. In this review, we discuss the main clinical neurological applications and some technical challenges which remain with ultra-high field MRI.

Susceptibility-Weighted Imaging of Deep Venous Congestion in Petroclival Meningioma

BACKGROUND

Protecting the venous drainage route during surgery in cases of petroclival meningioma (PCM) is important. Identifying venous congestion preoperatively can be valuable in reducing the risks associated with venous congestion during surgery. In this study, we examined the utility of susceptibility-weighted imaging (SWI) in identifying the presence of venous congestion in PCM cases preoperatively and identified the factors associated with it.

METHODS

We retrospectively examined 24 patients who had undergone surgery for primary PCM. The areas of the basal and internal cerebral veins on the affected and unaffected sides, obtained using SWI, were compared to identify venous congestion. We further examined the association between multiple candidate factors that are thought to be related to venous congestion and venous congestion using statistical analyses.

RESULTS

SWI could successfully identify venous congestion in 11 of 24 PCM cases. Among the 12 factors examined, those associated with venous congestion were an extension of the tumor, over the midline or upward, which is known to disturb the venous flow at the brainstem surface; anastomosis of the superficial cerebral vein (i.e., bypass route for venous congestion); and a high ABC Surgical Risk Scale score, an indicator of postoperative neurologic deterioration.

CONCLUSIONS

We showed that SWI is useful for evaluating venous congestion in PCM cases preoperatively and for identifying factors reflecting the risk of venous congestion. Taken together, our findings provide a multimodal strategy for the preoperative prediction of venous congestion, which could facilitate the treatment of PCM.

3D microsurgical anatomy of the cortico-spinal tract and lemniscal pathway based on fiber microdissection and demonstration with tractography

OBJECTIVE

To demonstrate tridimensionally the anatomy of the cortico-spinal tract and the medial lemniscus, based on fiber microdissection and diffusion tensor tractography (DTT).

MATERIAL AND METHODS

Ten brain hemispheres and brain-stem human specimens were dissected and studied under the operating microscope with microsurgical instruments by applying the fiber microdissection technique. Brain magnetic resonance imaging was obtained from 15 healthy subjects using diffusion-weighted images, in order to reproduce the cortico-spinal tract and the lemniscal pathway on DTT images.

RESULTS

The main bundles of the cortico-spinal tract and medial lemniscus were demonstrated and delineated throughout most of their trajectories, noticing their gross anatomical relation to one another and with other white matter tracts and gray matter nuclei the surround them, specially in the brain-stem; together with their corresponding representation on DTT images.

CONCLUSIONS

Using the fiber microdissection technique we were able to distinguish the disposition, architecture and general topography of the cortico-spinal tract and medial lemniscus. This knowledge has provided a unique and profound anatomical perspective, supporting the correct representation and interpretation of DTT images. This information should be incorporated in the clinical scenario in order to assist surgeons in the detailed and critic analysis of lesions located inside the brain-stem, and therefore, improve the surgical indications and planning, including the preoperative selection of optimal surgical strategies and possible corridors to enter the brainstem, to achieve safer and more precise microsurgical technique.

Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas

The aim of the present study was to assess the value of susceptibility-weighted imaging (SWI) and diffusion-weighted imaging (DWI) in the grading of gliomas and to evaluate the correlation between these quantitative parameters derived from SWI and DWI. A total of 49 patients with glioma were assessed by DWI and SWI. The evaluation included the ratio of apparent diffuse coefficient values between the solid portion of tumors and contralateral normal white matter (rADC) and the degree of intratumoral susceptibility signal intensity (ITSS) within tumors. Receiver operating characteristic curve (ROC) analyses were performed and the area under the ROC curve was calculated to compare the diagnostic performance, determine optimum thresholds for tumor grading, and calculate the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for identifying high-grade gliomas. The correlation between DWI- and SWI-derived parameters was also evaluated. The rADC and the degrees of ITSS within tumors were significantly higher in high-grade gliomas than those in low-grade gliomas. ROC curve analysis indicated that the rADC was a better index for grading gliomas than the ITSS degree. Statistical analysis demonstrated a threshold value of 1.497 for rADC to provide a sensitivity, specificity, PPV and NPV of 86.2, 85.0, 89.3 and 81.0%, respectively, for determining high-grade gliomas. A degree of ITSS of 1.5 was defined as the threshold to identify high-grade gliomas and sensitivity, specificity, PPV and NPV of 82.8, 75.0, 82.8 and 75.0% were obtained, respectively. Furthermore, a moderate inverse correlation between rADC and the ITSS degree was revealed. Combination of SWI with DWI may provide valuable information for glioma grading.

Key clinical benefits of neuroimaging at 7T

The growing interest in ultra-high field MRI, with more than 35.000 MR examinations already performed at 7T, is related to improved clinical results with regard to morphological as well as functional and metabolic capabilities. Since the signal-to-noise ratio increases with the field strength of the MR scanner, the most evident application at 7T is to gain higher spatial resolution in the brain compared to 3T. Of specific clinical interest for neuro applications is the cerebral cortex at 7T, for the detection of changes in cortical structure, like the visualization of cortical microinfarcts and cortical plaques in Multiple Sclerosis. In imaging of the hippocampus, even subfields of the internal hippocampal anatomy and pathology may be visualized with excellent spatial resolution. Using Susceptibility Weighted Imaging, the plaque-vessel relationship and iron accumulations in Multiple Sclerosis can be visualized, which may provide a prognostic factor of disease. Vascular imaging is a highly promising field for 7T which is dealt with in a separate dedicated article in this special issue. The static and dynamic blood oxygenation level-dependent contrast also increases with the field strength, which significantly improves the accuracy of pre-surgical evaluation of vital brain areas before tumor removal. Improvement in acquisition and hardware technology have also resulted in an increasing number of MR spectroscopic imaging studies in patients at 7T. More recent parallel imaging and short-TR acquisition approaches have overcome the limitations of scan time and spatial resolution, thereby allowing imaging matrix sizes of up to 128×128. The benefits of these acquisition approaches for investigation of brain tumors and Multiple Sclerosis have been shown recently. Together, these possibilities demonstrate the feasibility and advantages of conducting routine diagnostic imaging and clinical research at 7T.

Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging

BACKGROUND

Susceptibility-weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro- and macrobleedings. Low- and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer-based morphological assessment of SWI patterns.

OBJECTIVE

To show the feasibility of SWI analysis on 3-T magnetic resonance imaging to distinguish different kinds of brain tumors.

METHODS

Seventy-eight patients affected by brain tumors of different histopathology (low- and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor.

RESULTS

Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P < .05). FD was statistically different between lymphomas and high-grade gliomas (P < .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low- from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%).

CONCLUSION

FD of SWI on 3-T magnetic resonance imaging is a novel image biomarker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors.

ABBREVIATIONS

FD, fractal dimensionSR, signal ratioSWI, susceptibility-weighted imaging.

Comparison of Routine Brain Imaging at 3 T and 7 T

OBJECTIVE

The aim of this study was to compare quantitative and semiquantitative parameters (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], image quality, diagnostic confidence) from a standard brain magnetic resonance imaging examination encompassing common neurological disorders such as demyelinating disease, gliomas, cerebrovascular disease, and epilepsy, with comparable sequence protocols and acquisition times at 3 T and at 7 T.

MATERIALS AND METHODS

Ten healthy volunteers and 4 subgroups of 40 patients in total underwent comparable magnetic resonance protocols with standard diffusion-weighted imaging, 2D and 3D turbo spin echo, 2D and 3D gradient echo and susceptibility-weighted imaging of the brain (10 sequences) at 3 T and 7 T. The subgroups comprised patients with either lesional (n = 5) or nonlesional (n = 4) epilepsy, intracerebral tumors (n = 11), demyelinating disease (n = 11) (relapsing-remitting multiple sclerosis [MS, n = 9], secondary progressive MS [n = 1], demyelinating disease not further specified [n = 1]), or chronic cerebrovascular disorders [n = 9]). For quantitative analysis, SNR and CNR were determined. For a semiquantitative assessment of the diagnostic confidence, a 10-point scale diagnostic confidence score (DCS) was applied. Two experienced radiologists with additional qualification in neuroradiology independently assessed, blinded to the field strength, 3 pathology-specific imaging criteria in each of the 4 disease groups and rated their diagnostic confidence. The overall image quality was semiquantitatively assessed using a 4-point scale taking into account whether diagnostic decision making was hampered by artifacts or not.

RESULTS

Without correction for spatial resolution, SNR was higher at 3 T except in the T2 SPACE 3D, DWI single shot, and DIR SPACE 3D sequences. The SNR corrected by the ratio of 3 T/7 T voxel sizes was higher at 7 T than at 3 T in 10 of 11 sequences (all except for T1 MP2RAGE 3D).In CNR, there was a wide variation between sequences and patient cohorts, but average CNR values were broadly similar at 3 T and 7 T.DCS values for all 4 pathologic entities were higher at 7 T than at 3 T. The DCS was significantly higher at 7 T for diagnosis and exclusion of cortical lesions in vascular disease. A tendency to higher DCS at 7 T for cortical lesions in MS was observed, and for the depiction of a central vein and iron deposits within MS lesions. Despite motion artifacts, DCS values were higher at 7 T for the diagnosis and exclusion of hippocampal sclerosis in mesial temporal lobe epilepsy (improved detection of the hippocampal subunits). Interrater agreement was 69.7% at 3 T and 93.3% at 7 T. There was no significant difference in the overall image quality score between 3 T and 7 T taking into account whether diagnostic decision making was hampered by artifacts or not.

CONCLUSIONS

Ultra-high-field magnetic resonance imaging at 7 T compared with 3 T yielded an improved diagnostic confidence in the most frequently encountered neurologic disorders. Higher spatial resolution and contrast were identified as the main contributory factors.

Brain Tumor Imaging

Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

The Medial and Lateral Lemnisci: Anatomically Adjoined But Functionally Distinct Fiber Tracts

OBJECTIVE

The dense and complex distribution of neural structures in the brainstem makes it challenging to understand their real configuration. We used the fiber microdissection technique to show the course of the medial and lateral lemnisci within the brainstem. Although these structures seem anatomically alike, they are functionally distinct.

METHODS

Fifteen human brainstems and 8 brain hemispheres (formalin-fixed and previously frozen) were dissected and studied under the operating microscope by applying the fiber microdissection technique.

RESULTS

We delineated and described the medial and lateral lemnisci, noting their gross elaborate arrangement. These structures are intimately compact and closely related to one another in their common trajectory through the tegmenta of the pons and midbrain. However, we were not able to identify the exact origin and termination of their fibers or the accurate delimitation between the medial lemniscus, spinothalamic tract, and lateral lemniscus along their course in the brainstem.

CONCLUSIONS

Using the fiber microdissection technique, we were able to define a general perspective of the topography and architecture of the medial and lateral lemnisci in the brainstem. This perspective should be incorporated into interpretations of magnetic resonance imaging techniques, recognizing both their benefits and limitations. It should also be applied to surgical planning and strategies to achieve a safer and more precise microsurgical procedure.

The utility of fractal analysis in clinical neuroscience

Physicians and scientists can use fractal analysis as a tool to objectively quantify complex patterns found in neuroscience and neurology. Fractal analysis has the potential to allow physicians to make predictions about clinical outcomes, categorize pathological states, and eventually generate diagnoses. In this review, we categorize and analyze the applications of fractal theory in neuroscience found in the literature. We discuss how fractals are applied and what evidence exists for fractal analysis in neurodegeneration, neoplasm, neurodevelopment, neurophysiology, epilepsy, neuropharmacology, and cell morphology. The goal of this review is to introduce the medical community to the utility of applying fractal theory in clinical neuroscience.

Clinical applications at ultrahigh field (7 T). Where does it make the difference?

Presently, three major MR vendors provide commercial 7-T units for clinical research under ethical permission, with the number of operating 7-T systems having increased to over 50. This rapid increase indicates the growing interest in ultrahigh-field MRI because of improved clinical results with regard to morphological as well as functional and metabolic capabilities. As the signal-to-noise ratio scales linearly with the field strength (B0 ) of the scanner, the most obvious application at 7 T is to obtain higher spatial resolution in the brain, musculoskeletal system and breast. Of specific clinical interest for neuro-applications is the cerebral cortex at 7 T, for the detection of changes in cortical structure as a sign of early dementia, as well as for the visualization of cortical microinfarcts and cortical plaques in multiple sclerosis. In the imaging of the hippocampus, even subfields of the internal hippocampal anatomy and pathology can be visualized with excellent resolution. The dynamic and static blood oxygenation level-dependent contrast increases linearly with the field strength, which significantly improves the pre-surgical evaluation of eloquent areas before tumor removal. Using susceptibility-weighted imaging, the plaque-vessel relationship and iron accumulation in multiple sclerosis can be visualized for the first time. Multi-nuclear clinical applications, such as sodium imaging for the evaluation of repair tissue quality after cartilage transplantation and (31) P spectroscopy for the differentiation between non-alcoholic benign liver disease and potentially progressive steatohepatitis, are only possible at ultrahigh fields. Although neuro- and musculoskeletal imaging have already demonstrated the clinical superiority of ultrahigh fields, whole-body clinical applications at 7 T are still limited, mainly because of the lack of suitable coils. The purpose of this article was therefore to review the clinical studies that have been performed thus far at 7 T, compared with 3 T, as well as those studies performed at 7 T that cannot be routinely performed at 3 T. Copyright © 2015 John Wiley & Sons, Ltd.

Susceptibility-Weighted Imaging of Glioma: Update on Current Imaging Status and Future Directions

Susceptibility-weighted imaging (SWI) provides invaluable insight into glioma pathophysiology and internal tumoral architecture. The physical contribution of intratumoral susceptibility signal (ITSS) may correspond to intralesional hemorrhage, calcification, or tumoral neovascularity. In this review, we present emerging evidence of ITSS for assessment of intratumoral calcification, grading of glioma, and factors influencing the pattern of ITSS in glioblastoma. SWI phase imaging assists in identification of intratumoral calcification that aids in narrowing the differential diagnosis. Development of intratumoral calcification posttreatment of glioma serves as an imaging marker of positive therapy response. Grading of tumors with ITSS using information attributed to microhemorrhage and neovascularity in SWI correlates with MR perfusion parameters and histologic grading of glioma and enriches preoperative prognosis. Quantitative susceptibility mapping may provide a means to discriminate subtle calcifications and hemorrhage in tumor imaging. Recent data suggest ITSS patterns in glioblastoma vary depending on tumoral volume and sublocation and correlate with degree of intratumoral necrosis and neovascularity. Increasingly, there is a recognized role of obtaining contrast-enhanced SWI (CE-SWI) for assessment of tumoral margin in high-grade glioma. Significant higher concentration of gadolinium accumulates at the border of the tumoral invasion zone as seen on the SWI sequence; this results from contrast-induced phase shift that clearly delineates the tumor margin. Lastly, absence of ITSS may aid in differentiation between high-grade glioma and primary CNS lymphoma, which typically shows absence of ITSS. We conclude that SWI and CE-SWI are indispensable tools for diagnosis, preoperative grading, posttherapy surveillance, and assessment of glioma.

Magnetic resonance susceptibility weighted imaging in neurosurgery: current applications and future perspectives

Susceptibility weighted imaging (SWI) is a relatively new imaging technique. Its high sensitivity to hemorrhagic components and ability to depict microvasculature by means of susceptibility effects within the veins allow for the accurate detection, grading, and monitoring of brain tumors. This imaging modality can also detect changes in blood flow to monitor stroke recovery and reveal specific subtypes of vascular malformations. In addition, small punctate lesions can be demonstrated with SWI, suggesting diffuse axonal injury, and the location of these lesions can help predict neurological outcome in patients. This imaging technique is also beneficial for applications in functional neurosurgery given its ability to clearly depict and differentiate deep midbrain nuclei and close submillimeter veins, both of which are necessary for presurgical planning of deep brain stimulation. By exploiting the magnetic susceptibilities of substances within the body, such as deoxyhemoglobin, calcium, and iron, SWI can clearly visualize the vasculature and hemorrhagic components even without the use of contrast agents. The high sensitivity of SWI relative to other imaging techniques in showing tumor vasculature and microhemorrhages suggests that it is an effective imaging modality that provides additional information not shown using conventional MRI. Despite SWI's clinical advantages, its implementation in MRI protocols is still far from consistent in clinical usage. To develop a deeper appreciation for SWI, the authors here review the clinical applications in 4 major fields of neurosurgery: neurooncology, vascular neurosurgery, neurotraumatology, and functional neurosurgery. Finally, they address the limitations of and future perspectives on SWI in neurosurgery.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

Fractals in the Neurosciences, Part II

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

Computational Fractal-Based Analysis of Brain Arteriovenous Malformation Angioarchitecture

BACKGROUND:

Neuroimaging is the gold standard for diagnosis and follow-up of brain arteriovenous malformations (bAVMs), but no objective parameter has been validated for the assessment of the nidus angioarchitecture and for prognostication following treatment. The fractal dimension (FD), which is a mathematical parameter able to quantify the space-filling properties and roughness of natural objects, may be useful in quantifying the geometrical complexity of bAVMs nidus.

OBJECTIVE:

To propose FD as a neuroimaging biomarker of the nidus angioarchitecture, which might be related to radiosurgical outcome.

METHODS:

We retrospectively analyzed 54 patients who had undergone stereotactic radiosurgery for the treatment of bAVMs. The quantification of the geometric complexity of the vessels forming the nidus, imaged in magnetic resonance imaging, was assessed by means of the box-counting method to obtain the fractal dimension.

RESULTS:

FD was found to be significantly associated with the size (P = .03) and volume (P &lt; .001) of the nidus, in addition to several angioarchitectural parameters. A nonsignificant association between clinical outcome and FD was observed (area under the curve, 0.637 [95% confidence interval, 0.49-0.79]), indicative of a potential inverse relationship between FD and bAVM obliteration.

CONCLUSION:

In our exploratory methodological research, we showed that the FD is an objective computer-aided parameter for quantifying the geometrical complexity and roughness of the bAVM nidus. The results suggest that more complex bAVM angioarchitecture, having higher FD values, might be related to decreased response to radiosurgery and that the FD of the bAVM nidus could be used as a morphometric neuroimaging biomarker.

Microvascular morphometrics of the hypophysis and pituitary tumors: from bench to operating theatre

The idea that microvasculature might be a histopathological biomarker in the prognosis and treatment of tumors is garnering even more attention in the scientific community. The roles of neovascularity in tumor progression and metastasis, have become a hot-topic of investigation in cancer research. A number of methods of quantitatively analyzing pituitary adenoma microvasculature have been applied, and fractal analysis is emerging as a potential effective model for this aim. Additionally, new and more specific immunological techniques have been developed for the detection of microvessels. CD105 (Endoglin) has been proposed as a valuable antigen that marks only newly formed vessels, rather than the entire tumor microvascular system. The combination of different types of immunostaining techniques for the detection of microvessels in pituitary adenomas with fractal analysis as an objective and computer-aided technique to quantify and describe morphological aspects of microvessels has potential implications in future clinical and surgical applications. Tumor treatments, such as anti-angiogenic therapy, as well as intraoperative tools, stand to be enhanced by increasing advances in microvascular research. We here review the methods used for the quantitative analysis of microvessels of the pituitary in its physiopathological states, with the aim to show the pituitary adenoma as a model for the study of neoplastic angioarchitecture and the importance of the introduction of new techniques for the study of angiogenesis, with the relative scientific, medical and surgical implications.

Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review

Susceptibility-weighted imaging (SWI) is a technique that exploits the susceptibility difference between tissues to provide contrast for different regions of the brain. In essence, it uses the deoxygenated hemoglobin of veins, hemosiderin of hemorrhage, etc. as intrinsic contrast agents, allowing for much better visualization of blood and microvessels even without administration of an external contrast agent. It is a fast-evolving field that is being constantly improved and increasingly implemented with updates in relevant technology. Multiple studies have been done on the role of SWI in the management of various neurologic disorders and it is also being seen as a further step in the neuroradiologist’s goal of being able to noninvasively grade tumors in order to influence therapy. This article briefly reviews the evolution of SWI since its conception and provides the reader with a comprehensive summary of various studies that have been done on its application for detecting and grading intraaxial brain tumors, specifically gliomas. Other useful magnetic resonance techniques that have shown promise in grading gliomas are also discussed.