Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization

Erythro–Magneto–HA–Virosome: A Bio-Inspired Drug Delivery System for Active Targeting of Drugs in the Lungs

Over the past few decades, finding more efficient and selective administration routes has gained significant attention due to its crucial role in the bioavailability, absorption rate and pharmacokinetics of therapeutic substances. The pulmonary delivery of drugs has become an attractive target of scientific and biomedical interest in the health care research area, as the lung, thanks to its high permeability and large absorptive surface area and good blood supply, is capable of absorbing pharmaceuticals either for local deposition or for systemic delivery. Nevertheless, the pulmonary drug delivery is relatively complex, and strategies to mitigate the effects of mechanical, chemical and immunological barriers are required. Herein, engineered erythrocytes, the Erythro–Magneto–Hemagglutinin (HA)–virosomes (EMHVs), are used as a novel strategy for efficiently delivering drugs to the lungs. EMHV bio-based carriers exploit the physical properties of magnetic nanoparticles to achieve effective targeting after their intravenous injection thanks to an external magnetic field. In addition, the presence of hemagglutinin fusion proteins on EMHVs’ membrane allows the DDS to anchor and fuse with the target tissue and locally release the therapeutic compound. Our results on the biomechanical and biophysical properties of EMHVs, such as the membrane robustness and deformability and the high magnetic susceptibility, as well as their in vivo biodistribution, highlight that this bio-inspired DDS is a promising platform for the controlled and lung-targeting delivery of drugs, and represents a valuable alternative to inhalation therapy to fulfill unmet clinical needs.

Clinical value of MRI T2-mapping quantitative assessment of carotid plaque

BACKGROUND

Stroke is a severe health problem, and magnetic resonance imaging (MRI) plays a significant role in stroke.

PURPOSE

To investigate the clinical value of MRI T2-mapping in carotid artery plaque.

MATERIAL AND METHODS

To locate the plaque in the carotid artery, 25 patients with carotid atherosclerosis were examined by 3.0-T MRI with three-dimensional (3D) time-of-flight and 3D fast spin-echo (FSE) T1-weighted scanning. The original images were obtained after T2-mapping (multi-spin-echo sequence) scanning. The T2 values of the plaque in the narrowest lumen were measured on T2 maps after postprocessing of the original images. Based on the symptoms, the patients were divided into two sub-groups; independent sample t-test was employed to compare the difference between the T2 values of the plaque in the two groups. We evaluated the optimal threshold and diagnostic efficacy of T2 values in predicting cerebrovascular symptoms by the receiver operating characteristic (ROC) curve.

RESULTS

The T2 values of the carotid artery plaque in symptomatic and asymptomatic patients were 111.43 ± 46.54 ms and 59.25 ± 39.77 ms, respectively (t = -3.421, P < 0.01). ROC analysis showed that the T2 value of 65.38 ms was the optimal threshold to predict cerebrovascular symptoms. The specificity, sensitivity, and accuracy attained were 94.1% (16/17), 93.3% (14/15), and 93.8% (30/32), respectively.

CONCLUSION

We quantitatively assessed carotid plaque components by MRI T2-mapping technology. The T2 values of the carotid plaque were associated with cerebrovascular symptoms. The T2 values of the symptomatic plaque group were significantly higher than those of the asymptomatic group.

Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification

Background:

In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Materials and Methods:

The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images.

Results:

The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC.

Conclusion:

The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

A new approach to automatic fetal brain extraction from MRI using a variational level set method

BACKGROUND AND PURPOSE

Appropriate images extracted from the MRI of mothers' wombs can be of great help in the medical diagnosis of fetal abnormalities. As maternal tissue may appear in such images, affecting visualization of myelination of the fetal brain, it is not possible to use methods routinely used for extraction of adult brains for fetal brains. The aim of the present study was to use a variational level set approach to extract fetal brain from T2-weighted MR images of the womb.

METHODS

Coronal T2-weighted images were acquired using fast MRI protocols (to avoid artifacts). The database includes 105 MR images from eight subjects. After correcting the inhomogeneity of the images, the fetal eyes were located, and from that information, the location of the fetus brain was automatically determined. Then, the variational level set was used for fetus brain extraction. The results were analyzed by a clinical specialist (radiologist) and the similarity (Dice and Jaccard coefficients), sensitivity and specificity were calculated.

RESULTS AND CONCLUSIONS

The means of the statistical analysis for the Dice and Jaccard coefficients, sensitivity and specificity, were 99.56%, 96.89%, 95.71%, and 97.96%, respectively. Thus, extraction of fetal brain from MR images was confirmed, both statistically and visually through cross-validation.

A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease

OBJECTIVES

To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD).

METHODS

TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD.

RESULTS

Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, - 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability - 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65).

CONCLUSIONS

The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice.

KEY POINTS

• This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.

Multifunctional nanoparticles for real-time evaluation of toxicity during fetal development

Increasing production of nanomaterials in industrial quantities has led to public health concerns regarding exposure, particularly among pregnant women and developing fetuses. Information regarding the barrier capacity of the placenta for various nanomaterials is limited due to challenges working with ex vivo human placentas or in vivo animal models. To facilitate real-time in vivo imaging of placental transport, we have developed a novel, multifunctional nanoparticle, based on a core of mesoporous silica nanoparticles (MSN), and functionalized for magnetic resonance imaging (MRI), ultrasound, and fluorescent microscopy. Our MSN particles were tested as a tracking method for harmful and toxic nanomaterials. In gravid mice, intravenous injections of MSN were administered in the maternal circulation in early gestation (day 9) and late gestation (day 14). MRI and ultrasound were used to track the MSN following the injections. Changes in contrast relative to control mice indicated that MSN were observed in the embryos of mice following early gestation injections, while MSN were excluded from the embryo by the placenta following late gestation injections. The timing of transplacental barrier porosity is consistent with the notion that in mice there is a progressive increasing segregation by the placenta in later gestation. In addition, built-in physico-chemical properties of our MSN may present options for the therapeutic treatment of embryonic exposure. For example, if preventive measures such as detoxification of harmful compounds are implemented, the particle size and exposure timing can be tailored to selectively distribute to the maternal side of the trophoblast or delivered to the fetus.

Novel Body Shape Descriptors for Abdominal Adiposity Prediction Using Magnetic Resonance Images and Stereovision Body Images

OBJECTIVE

The purpose of this study was to design novel shape descriptors based on three-dimensional (3D) body images and to use these parameters to establish prediction models for abdominal adiposity.

METHODS

Sixty-six men and fifty-five women were recruited for abdominal magnetic resonance imaging (MRI) and 3D whole-body imaging. Volumes of abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were measured from MRI sequences by using a fully automated algorithm. The shape descriptors were measured on the 3D body images by using the software developed in this study. Multiple regression analysis was employed on the training data set (70% of the total participants) to develop predictive models for VAT and SAT, with potential predictors selected from age, BMI, and the body shape descriptors. The validation data set (30%) was used for the validation of the predictive models.

RESULTS

Thirteen body shape descriptors exhibited high correlations (P < 0.01) with abdominal adiposity. The optimal predictive equations for VAT and SAT were determined separately for men and women.

CONCLUSIONS

Novel body shape descriptors defined on 3D body images can effectively predict abdominal adiposity quantified by MRI.

Automated Liver Elasticity Calculation for 3D MRE

Magnetic Resonance Elastography (MRE) is a phase-contrast MRI technique which calculates quantitative stiffness images, called elastograms, by imaging the propagation of acoustic waves in tissues. It is used clinically to diagnose liver fibrosis. Automated analysis of MRE is difficult as the corresponding MRI magnitude images (which contain anatomical information) are affected by intensity inhomogeneity, motion artifact, and poor tissue- and edge-contrast. Additionally, areas with low wave amplitude must be excluded. An automated algorithm has already been successfully developed and validated for clinical 2D MRE. 3D MRE acquires substantially more data and, due to accelerated acquisition, has exacerbated image artifacts. Also, the current 3D MRE processing does not yield a confidence map to indicate MRE wave quality and guide ROI selection, as is the case in 2D. In this study, extension of the 2D automated method, with a simple wave-amplitude metric, was developed and validated against an expert reader in a set of 57 patient exams with both 2D and 3D MRE. The stiffness discrepancy with the expert for 3D MRE was -0.8% ± 9.45% and was better than discrepancy with the same reader for 2D MRE (-3.2% ± 10.43%), and better than the inter-reader discrepancy observed in previous studies. There were no automated processing failures in this dataset. Thus, the automated liver elasticity calculation (ALEC) algorithm is able to calculate stiffness from 3D MRE data with minimal bias and good precision, while enabling stiffness measurements to be fully reproducible and to be easily performed on the large 3D MRE datasets.

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter- and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, bias-corrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.

Automated quantification of abdominal adiposity by magnetic resonance imaging

OBJECTIVES

To develop a fully-automated algorithm to process axial magnetic resonance imaging (MRI) slices for quantifying abdominal visceral, subcutaneous and total adipose tissues, i.e., VAT, SAT, and TAT, without human intervention or prior knowledge.

MATERIALS AND METHODS

Fat regions in single MRI slice or sequence (20 slices) were identified with image processing techniques including region-growing, inhomogeneity correction, fuzzy c-means clustering, and active contours segmentation. The MR images of 85 subjects (60 males and 25 females), whose body mass index (BMI) values ranged from 19.96 to 40.35 kg/m2 , were analyzed using the fully-automated algorithm-the automatic method developed in the research and the widely used semi-automated software (sliceOmatic® Tomovision, Inc.)-the reference method.

RESULTS

The proposed automated method showed good performance against the reference method to quantify adipose tissues in both single umbilical slice and MRI sequence. The square of the Pearson correlation coefficients (R2 ) based on the results generated from the two methods for VAT/SAT/TAT were 0.977/0.998/0.997 for single slice data and 0.995/0.999/0.999 for volumetric data. The intra-class correlation of visceral adipose tissue (VAT) between the three operators was 0.939 in the reference method, which was improved to 0.999 in the automatic method. The adipose tissue measurements in the slice at Lumbar 3 vertebra have the highest correlation with the total fat volumes across the entire abdomen.

CONCLUSION

The fully-automated algorithm presented in the paper provides an accurate and reliable assessment of abdominal fat without human intervention. Am. J. Hum. Biol. 28:757-766, 2016. © 2016Wiley Periodicals, Inc.

3-dimensional throat region segmentation from MRI data based on Fourier interpolation and 3-dimensional level set methods

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and to reduce artifacts. Isotropic 3D volume is reconstructed using the Fourier interpolation. Furthermore, a cube encompassing the throat region is evolved using level set method to form a smooth 3D boundary of the throat region. The results of the proposed algorithm on real and synthetic MRI data are used to validate the robustness and accuracy of the algorithm.

Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

PURPOSE

The aim of this work was to quantify the extent of lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques.

METHODS

Patients scheduled for carotid endarterectomy underwent four-point Dixon and T1-weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three-dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH.

RESULTS

Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s^-1 versus 56.94 ± 0.9095 s^-1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R²  = 0.92) as well as between cumulative R2* and IPH (R²  = 0.94).

CONCLUSION

Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH. Magn Reson Med 78:285-296, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Nanotechnology and cancer: improving real-time monitoring and staging of bladder cancer with multimodal mesoporous silica nanoparticles

Background

Despite being one of the most common cancers, bladder cancer is largely inefficiently and inaccurately staged and monitored. Current imaging methods detect cancer only when it has reached “visible” size and has significantly disrupted the structure of the organ. By that time, thousands of cells will have proliferated and perhaps metastasized. Repeated biopsies and scans are necessary to determine the effect of therapy on cancer growth. In this report, we describe a novel approach based on multimodal nanoparticle contrast agent technology and its application to a preclinical animal model of bladder cancer. The innovation relies on the engineering core of mesoporous silica with specific scanning contrast properties and surface modification that include fluorescence and magnetic resonance imaging (MRI) contrast. The overall dimensions of the nano-device are preset at 80–180 nm, depending on composition with a pore size of 2 nm.

Methods

To facilitate and expedite discoveries, we combined a well-known model of bladder cancer and our novel technology. We exposed nanoparticles to MB49 murine bladder cancer cells in vitro and found that 70 % of the cells were labeled by nanoparticles as measured by flow cytometry. The in vivo mouse model for bladder cancer is particularly well suited for T1- and T2-weighted MRI.

Results

Under our experimental conditions, we demonstrate that the nanoparticles considerably improve tumor definition in terms of volumetric, intensity and structural characteristics. Important bladder tumor parameters can be ascertained, non-invasively, repetitively, and with great accuracy. Furthermore, since the particles are not biodegradable, repetitive injection is not required. This feature allows follow-up diagnostic evaluations during cancer treatment. Changes in MRI signals show that in situ uptake of free particles has predilection to tumor cells relative to normal bladder epithelium. The particle distribution within the tumors was corroborated by fluorescent microscopy of sections of excised bladders. In addition, MRI imaging revealed fibrous finger-like projections into the tumors where particles insinuated themselves deeply. This morphological characteristic was confirmed by fluorescence microscopy.

Conclusions

These findings may present new options for therapeutic intervention. Ultimately, the combination of real-time and repeated MRI evaluation of the tumors enhanced by nanoparticle contrast may have the potential for translation into human clinical studies for tumor staging, therapeutic monitoring, and drug delivery.

Multimodal molecular imaging system for pathway-specific reporter gene expression

Preclinical imaging modalities represent an essential tool to develop a modern and translational biomedical research. To date, Optical Imaging (OI) and Magnetic Resonance Imaging (MRI) are used principally in separate studies for molecular imaging studies. We decided to combine OI and MRI together through the development of a lentiviral vector to monitor the Wnt pathway response to Lithium Chloride (LiCl) treatment. The construct was stably infected in glioblastoma cells and, after intracranial transplantation in mice, serial MRI and OI imaging sessions were performed to detect human ferritin heavy chain protein (hFTH) and firefly luciferase enzyme (FLuc) respectively. The system allowed also ex vivo analysis using a constitutive fluorescence protein expression. In mice, LiCl administration has shown significantly increment of luminescence signal and a lower signal of T2 values (P<0.05), recorded noninvasively with OI and a 7 Tesla MRI scanner. This study indicates that OI and MRI can be performed in a single in vivo experiment, providing an in vivo proof-of-concept for drug discovery projects in preclinical phase.

Automated liver elasticity calculation for MR elastography

PURPOSE

MR elastography (MRE) is a phase-contrast MRI technique that is used to quantitatively assess liver stiffness for staging hepatic fibrosis. The current approach requires manual selection of a region of interest (ROI) with good wave quality from which to measure stiffness. The purpose of this work was to develop and evaluate a fully automated approach for measuring hepatic stiffness from MRE images to further reduce measurement variability.

MATERIALS AND METHODS

An automated liver elasticity calculation (ALEC) algorithm was developed to address reader stiffness measurement variability. ALEC has three stages: initial tissue estimation, segmentation, and ROI cleanup. Stiffnesses measured by the algorithm were compared with technicians and an expert radiologist in a set of 121 clinical cases acquired at 1.5 Tesla. Intra-class correlation (ICC), Bland-Altman analysis, and a noninferiority test were performed to evaluate whether the algorithm can be used in place of manual analysis by technicians.

RESULTS

The stiffness measurement difference with the expert was 1.42% ± 11.17% (mean ± standard deviation) for the algorithm and 1.82% ± 13.65% for the technicians. The ICCs were 0.981 and 0.984, respectively. Both the algorithm and technicians were equivalent to the expert within a 5% significance margin (P < 0.01). The algorithm had no failures in the 119 cases that were considered analyzable by the human readers.

CONCLUSION

The results of this study show that the newly developed automated algorithm is able to measure stiffness in clinical liver MRE exams with an accuracy that is equivalent to that of an expert radiologist. ALEC may be useful for analysis of archived data and suitable for performing multi-center studies.

The influence of pericardial fat upon left ventricular function in obese females: evidence of a site-specific effect

BACKGROUND

Although increased volume of pericardial fat has been associated with decreased cardiac function, it is unclear whether this association is mediated by systemic overall obesity or direct regional fat interactions. We hypothesized that if local effects dominate, left ventricular (LV) function would be most strongly associated with pericardial fat that surrounds the left rather than the right ventricle (RV).

METHODS

Female obese subjects (n = 60) had cardiovascular magnetic resonance (CMR) scans to obtain measures of LV function and pericardial fat volumes. LV function was obtained using the cine steady state free precession imaging in short axis orientation. The amount of pericardial fat was determined volumetrically by the cardiac gated T1 black blood imaging and normalized to body surface area.

RESULTS

In this study cohort, LV fat correlated with several LV hemodynamic measurements including cardiac output (r = -0.41, p = 0.001) and stroke volume (r = -0.26, p = 0.05), as well as diastolic functional parameters including peak-early-filling rate (r = -0.38, p = 0.01), early late filling ratio (r = -0.34, p = 0.03), and time to peak-early-filling (r = 0.34, p = 0.03). These correlations remained significant even after adjusting for the body mass index and the blood pressure. However, similar correlations became weakened or even disappeared between RV fat and LV function. LV function was not correlated with systemic plasma factors, such as C-reactive protein (CRP), B-type natriuretic peptide (BNP), Interleukin-6 (IL-6), resistin and adiponectin (all p > 0.05).

CONCLUSIONS

LV hemodynamic and diastolic function was associated more with LV fat as compared to RV or total pericardial fat, but not with systemic inflammatory markers or adipokines. The correlations between LV function and pericardial fat remained significant even after adjusting for systemic factors. These findings suggest a site-specific influence of pericardial fat on LV function, which could imply local secretion of molecules into the underlying tissue or an anatomic effect, both mechanisms meriting future evaluation.

Monte Carlo bias field correction in endorectal diffusion imaging

Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multiparametric magnetic resonance imaging (MRI) has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci, which is required to plan the treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multiparametric datasets. An endorectal receiver coil is often used to improve signal-to-noise ratio and aid in detection of smaller cancer foci. Moreover, computed high b-value diffusion-weighted imaging show improved delineation of tumors but is subject to strong bias fields near the coil. Here, a nonparametric approach to bias field correction for endorectal diffusion imaging via Monte Carlo sampling is introduced. It will be shown that the delineation between the prostate gland and the background and intensity inhomogeneity may be improved using the proposed approach. High b-value generated results also show improved visualization of tumor regions. The results suggest that Monte Carlo bias correction may have potential as a preprocessing tool for endorectal diffusion images for the prostate cancer detection and localization or segmentation.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing.

In-vivo quantitative T2 mapping of carotid arteries in atherosclerotic patients: segmentation and T2 measurement of plaque components

BACKGROUND

Atherosclerotic plaques in carotid arteries can be characterized in-vivo by multicontrast cardiovascular magnetic resonance (CMR), which has been thoroughly validated with histology. However, the non-quantitative nature of multicontrast CMR and the need for extensive post-acquisition interpretation limit the widespread clinical application of in-vivo CMR plaque characterization. Quantitative T2 mapping is a promising alternative since it can provide absolute physical measurements of plaque components that can be standardized among different CMR systems and widely adopted in multi-centre studies. The purpose of this study was to investigate the use of in-vivo T2 mapping for atherosclerotic plaque characterization by performing American Heart Association (AHA) plaque type classification, segmenting carotid T2 maps and measuring in-vivo T2 values of plaque components.

METHODS

The carotid arteries of 15 atherosclerotic patients (11 males, 71 ± 10 years) were imaged at 3 T using the conventional multicontrast protocol and Multiple-Spin-Echo (Multi-SE). T2 maps of carotid arteries were generated by mono-exponential fitting to the series of images acquired by Multi-SE using nonlinear least-squares regression. Two reviewers independently classified carotid plaque types following the CMR-modified AHA scheme, one using multicontrast CMR and the other using T2 maps and time-of-flight (TOF) angiography. A semi-automated method based on Bayes classifiers segmented the T2 maps of carotid arteries into 4 classes: calcification, lipid-rich necrotic core (LRNC), fibrous tissue and recent IPH. Mean ± SD of the T2 values of voxels classified as LRNC, fibrous tissue and recent IPH were calculated.

RESULTS

In 37 images of carotid arteries from 15 patients, AHA plaque type classified by multicontrast CMR and by T2 maps (+ TOF) showed good agreement (76% of matching classifications and Cohen's κ = 0.68). The T2 maps of 14 normal arteries were used to measure T2 of tunica intima and media (T2 = 54 ± 13 ms). From 11865 voxels in the T2 maps of 15 arteries with advanced atherosclerosis, 2394 voxels were classified by the segmentation algorithm as LRNC (T2 = 37 ± 5 ms) and 7511 voxels as fibrous tissue (T2 = 56 ± 9 ms); 192 voxels were identified as calcification and one recent IPH (236 voxels, T2 = 107 ± 25 ms) was detected on T2 maps and confirmed by multicontrast CMR.

CONCLUSIONS

This carotid CMR study shows the potential of in-vivo T2 mapping for atherosclerotic plaque characterization. Agreement between AHA plaque types classified by T2 maps (+TOF) and by conventional multicontrast CMR was good, and T2 measured in-vivo in LRNC, fibrous tissue and recent IPH demonstrated the ability to discriminate plaque components on T2 maps.

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

PURPOSE

Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis.

METHODS

Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B(1) inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions.

RESULTS

The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%).

CONCLUSIONS

The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.

An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy

PURPOSE

Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation.

METHODS AND MATERIALS

Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans.

RESULTS

The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean ± standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 ± 0.12, 0.70 ± 0.14 for the prostate, 0.64 ± 0.16 for the bladder, and 0.63 ± 0.16 for the rectum.

CONCLUSIONS

The electron-density atlas method provides the ability to automatically define organs and map realistic electron densities to MRI scans for radiotherapy dose planning and DRR generation. This method provides the necessary tools for MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy.

Segmentation of the quadratus lumborum muscle using statistical shape modeling

PURPOSE

To compare automated segmentation of the quadratus lumborum (QL) based on statistical shape modeling (SSM) with conventional manual processing of magnetic resonance (MR) images for segmentation of this paraspinal muscle.

MATERIALS AND METHODS

The automated SSM scheme for QL segmentation was developed using an MR database of 7 mm axial images of the lumbar region from 20 subjects (cricket fast bowlers and athletic controls). Specifically, a hierarchical 3D-SSM scheme for segmentation of the QL, and surrounding psoas major (PS) and erector spinae+multifidus (ES+MT) musculature, was implemented after image preprocessing (bias field correction, partial volume interpolation) followed by image registration procedures to develop average and probabilistic MR atlases for initializing and constraining the SSM segmentation of the QL. The automated and manual QL segmentations were compared using spatial overlap and average surface distance metrics.

RESULTS

The spatial overlap between the automated SSM and manual segmentations had a median Dice similarity metric of 0.87 (mean = 0.86, SD = 0.08) and mean average surface distance of 1.26 mm (SD = 0.61) and 1.32 mm (SD = 0.60) for the right and left QL muscles, respectively.

CONCLUSION

The current SSM scheme represents a promising approach for future automated morphometric analyses of the QL and other paraspinal muscles from MR images.

Compensation of RF field and receiver coil induced inhomogeneity effects in abdominal MR images by a priori knowledge on the human adipose tissue distribution

PURPOSE

To reliably compensate bias field effects in abdominal areas to accurately quantify visceral adipose tissue using standard T1-weighted sequences on MR scanners with up to 3 Tesla (T) field strength.

MATERIALS AND METHODS

Compensation is achieved in two steps: The bias field is first estimated by picking and fitting sampling points from the subcutaneous adipose tissue, using active contours and a thin plate fitting spline. Then, additional sampling points from visceral adipose tissue compartments are detected by thresholding and the bias field estimation is refined. It was compared with an established method using a simulated abdominal image and real 3T data.

RESULTS

At low bias field amplitudes (40-50%), the simulation study showed a good reduction of the mean coefficients of variance (CV) for both approaches (>80%). At higher amplitudes, the CV reduction was significantly higher for our approach (83.6%), compared with LEMS (54.3%). In the real data study, our approach showed reliable reduction of the inhomogeneities, while the LEMS algorithm sometimes even amplified the inhomogeneities.

CONCLUSION

The proposed method enables accurate and reliable segmentation of abdominal adipose tissue using simple thresholding techniques, even in severely corrupted images slices, obtained when using high field strengths and/or phased-array coils.

Current techniques for MR imaging of atherosclerosis

Vessel wall imaging of large vessels has the potential to identify culprit atherosclerotic plaques that lead to cardiovascular events. Comprehensive assessment of atherosclerotic plaque size, composition, and biological activity is possible with magnetic resonance imaging (MRI). Magnetic resonance imaging of the atherosclerotic plaque has demonstrated high accuracy and measurement reproducibility for plaque size. The accuracy of in vivo multicontrast MRI for identification of plaque composition has been validated against histological findings. Magnetic resonance imaging markers of plaque biological activity such as neovasculature and inflammation have been demonstrated. In contrast to other plaque imaging modalities, MRI can be used to study multiple vascular beds noninvasively over time. In this review, we compare the status of in vivo plaque imaging by MRI to competing imaging modalities. Recent MR technological improvements allow fast, accurate, and reproducible plaque imaging. An overview of current MRI techniques required for carotid plaque imaging including hardware, specialized pulse sequences, and processing algorithms are presented. In addition, the application of these techniques to coronary, aortic, and peripheral vascular beds is reviewed.

The relationship of ectopic lipid accumulation to cardiac and vascular function in obesity and metabolic syndrome

Storage of lipid in ectopic depots outside of abdominal visceral and subcutaneous stores, including within the pericardium and liver, has been associated with obesity, insulin resistance, and cardiovascular risk. We sought to determine whether anatomically distinct ectopic depots were physiologically correlated and site-specific effects upon cardiovascular function could be identified. Obese subjects (n = 28) with metabolic syndrome but without known atherosclerotic disease and healthy controls (n = 18) underwent magnetic resonance imaging (MRI) and proton MR spectroscopy (MRS) to quantify pericardial and periaortic lipid volumes, cardiac function, aortic compliance, and intrahepatic lipid content. Fasting plasma lipoproteins, glucose, insulin, and free-fatty acids were measured. Pericardial and intrahepatic (P < 0.01) and periaortic (P < 0.05) lipid volumes were increased in obese subjects vs. controls and were strongly and positively correlated (P <or= 0.01) but independent of BMI (P = NS) among obese subjects. Intrahepatic lipid was associated with insulin resistance (P < 0.01) and triglycerides (P < 0.05), whereas pericardial and periaortic lipid were not (P = NS). Periaortic and pericardial lipid positively correlated to free-fatty acids (P <or= 0.01) and negatively correlated to high-density lipoprotein (HDL) cholesterol (P < 0.05). Pericardial lipid negatively correlated to cardiac output (P = 0.03) and stroke volume (P = 0.01) but not to left ventricular ejection fraction (P = 0.46). None of the ectopic depots correlated to aortic compliance. In conclusion, ectopic storage of lipid in anatomically distinct depots appeared tightly correlated but independent of body size. Site-specific functional abnormalities were observed for pericardial but not periaortic lipid. These findings underscore the utility of MRI to assess individual differences in ectopic lipid that are not predictable from BMI.

Reproducible MRI measurement of adipose tissue volumes in genetic and dietary rodent obesity models

PURPOSE

To develop ratio MRI [lipid/(lipid+water)] methods for assessing lipid depots and compare measurement variability with biological differences among lean controls (spontaneously hypertensive rats [SHRs]), dietary obese rats (SHR-DOs), and genetic/dietary obese rats (SHROBs).

MATERIALS AND METHODS

Images with and without chemical shift-selective (CHESS) water suppression were processed using a semiautomatic method that accounts for relaxometry, chemical shift, receive coil sensitivity, and partial volume.

RESULTS

Partial volume correction improved results by 10% to 15%. Over six operators, volume variation was reduced to 1.9 mL from 30.6 mL for single-image-analysis with intensity inhomogeneity. For three acquisitions on the same animal, volume reproducibility was <1%. SHROBs had six times more visceral and eight times more subcutaneous adipose tissue than SHRs. SHR-DOs had enlarged visceral depots (three times larger than those in SHRs). SHROBs had significantly more subcutaneous adipose tissue, indicating a strong genetic component to this fat depot. Liver ratios in SHR-DO and SHROB were higher than in SHR, indicating elevated fat content. Among SHROBs, evidence suggested a phenotype SHROB* having elevated liver ratios and visceral adipose tissue volumes.

CONCLUSION

Effects of diet and genetics on obesity were significantly larger than variations due to image acquisition and analysis, indicating that these methods can be used to assess accumulation/depletion of lipid depots in animal models of obesity.

Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging

A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.

Bias artifact suppression on MR volumes

RF-inhomogeneity correction is a relevant research topic in the field of magnetic resonance imaging (MRI). A volume corrupted by this artifact exhibits nonuniform illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR volumes scanned from different body parts without any a priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.

A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme

A fully automatic, multiscale fuzzy C-means (MsFCM) classification method for MR images is presented in this paper. We use a diffusion filter to process MR images and to construct a multiscale image series. A multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels. The objective function of the conventional fuzzy C-means (FCM) method is modified to allow multiscale classification processing where the result from a coarse scale supervises the classification in the next fine scale. The method is robust for noise and low-contrast MR images because of its multiscale diffusion filtering scheme. The new method was compared with the conventional FCM method and a modified FCM (MFCM) method. Validation studies were performed on synthesized images with various contrasts and on the McGill brain MR image database. Our MsFCM method consistently performed better than the conventional FCM and MFCM methods. The MsFCM method achieved an overlap ratio of greater than 90% as validated by the ground truth. Experiments results on real MR images were given to demonstrate the effectiveness of the proposed method. Our multiscale fuzzy C-means classification method is accurate and robust for various MR images. It can provide a quantitative tool for neuroimaging and other applications.

Automated segmentation of the quadratus lumborum muscle from magnetic resonance images using a hybrid atlas based - geodesic active contour scheme

This study presents a novel method for the automatic segmentation of the quadratus lumborum (QL) muscle from axial magnetic resonance (MR) images using a hybrid scheme incorporating the use of non-rigid registration with probabilistic atlases (PAs) and geodesic active contours (GACs). The scheme was evaluated on an MR database of 7mm axial images of the lumbar spine from 20 subjects (fast bowlers and athletic controls). This scheme involved several steps, including (i) image pre-processing, (ii) generation of PAs for the QL, psoas (PS) and erector spinae+multifidus (ES+MT) muscles and (iii) segmentation, using 3D GACs initialized and constrained by the propagation of the PAs using non-rigid registration. Pre-processing of the images involved bias field correction based on local entropy minimization with a bicubic spline model and a reverse diffusion interpolation algorithm to increase the slice resolution to 0.98 x 0.98 x 1.75mm. The processed images were then registered (affine and non-rigid) and used to generate an average atlas. The PAs for the QL, PS and ES+MT were then generated by propagation of manual segmentations. These atlases were further analysed with specialised filtering to constrain the QL segmentation from adjacent non-muscle tissues (kidney, fat). This information was then used in 3D GACs to obtain the final segmentation of the QL. The automatic segmentation results were compared with the manual segmentations using the Dice similarity metric (DSC), with a median DSC for the right and left QL muscles of 0.78 (mean = 0.77, sd=0.07) and 0.75 (mean =0.74, sd=0.07), respectively.

Removal of local and biased global maxima in intensity-based registration

Intensity based registration (e.g., mutual information) suffers from a scalloping artifact giving rise to local maxima and sometimes a biased global maximum in a similarity objective function. Here, we demonstrate that scalloping is principally due to the noise reduction filtering that occurs when image samples are interpolated. Typically at a much smaller scale (100 times less in our test cases), there are also fluctuations in the similarity objective function due to interpolation of the signal and to sampling of a continuous, band-limited image signal. Focusing on the larger problem from noise, we show that this phenomenon can even bias global maxima, giving inaccurate registrations. This phenomenon is readily seen when one registers an image onto itself with different noise realizations but is absent when the same noise realization is present in both images. For linear interpolation, local maxima and global bias are removed if one filters the interpolated image using a new constant variance filter for linear interpolation (cv-lin filter), which equalizes the variance across the interpolated image. We use 2D synthetic and MR images and characterize the effect of cv-lin on similarity objective functions. With a reduction of local and biased maxima, image registration becomes more robust and accurate. An efficient implementation adds insignificant computation time per iteration, and because optimization proceeds more smoothly, sometimes fewer iterations are needed.