Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.     

MR-Less Surface-Based Amyloid Assessment Based on 11C PiB PET

Background

β-amyloid (Aβ) plaques in brain''s grey matter (GM) are one of the pathological hallmarks of Alzheimer''s disease (AD), and can be imaged in vivo using Positron Emission Tomography (PET) with ¹¹C or ¹⁸F radiotracers. Estimating Aβ burden in cortical GM has been shown to improve diagnosis and monitoring of AD. However, lacking structural information in PET images requires such assessments to be performed with anatomical MRI scans, which may not be available at different clinical settings or being contraindicated for particular reasons. This study aimed to develop an MR-less Aβ imaging quantification method that requires only PET images for reliable Aβ burden estimations.

Materials and Methods

The proposed method has been developed using a multi-atlas based approach on ¹¹C-PiB scans from 143 subjects (75 PiB+ and 68 PiB- subjects) in AIBL study. A subset of 20 subjects (PET and MRI) were used as atlases: 1) MRI images were co-registered with tissue segmentation; 2) 3D surface at the GM-WM interfacing was extracted and registered to a canonical space; 3) Mean PiB retention within GM was estimated and mapped to the surface. For other participants, each atlas PET image (and surface) was registered to the subject''s PET image for PiB estimation within GM. The results are combined by subject-specific atlas selection and Bayesian fusion to generate estimated surface values.

Results

All PiB+ subjects (N = 75) were highly correlated between the MR-dependent and the PET-only methods with Intraclass Correlation (ICC) of 0.94, and an average relative difference error of 13% (or 0.23 SUVR) per surface vertex. All PiB- subjects (N = 68) revealed visually akin patterns with a relative difference error of 16% (or 0.19 SUVR) per surface vertex.

Conclusion

The demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.     

An Intra-Individual Comparison of MRI, [18F]-FET and [18F]-FLT PET in Patients with High-Grade Gliomas

Objectives

Intra-individual spatial overlap analysis of tumor volumes assessed by MRI, the amino acid PET tracer [¹⁸F]-FET and the nucleoside PET tracer [¹⁸F]-FLT in high-grade gliomas (HGG).

Methods

MRI, [¹⁸F]-FET and [¹⁸F]-FLT PET data sets were retrospectively analyzed in 23 HGG patients. Morphologic tumor volumes on MRI (post-contrast T1 (cT1) and T2 images) were calculated using a semi-automatic image segmentation method. Metabolic tumor volumes for [¹⁸F]-FET and [¹⁸F]-FLT PETs were determined by image segmentation using a threshold-based volume of interest analysis. After co-registration with MRI the morphologic and metabolic tumor volumes were compared on an intra-individual basis in order to estimate spatial overlaps using the Spearman''s rank correlation coefficient and the Mann-Whitney U test.

Results

[¹⁸F]-FLT uptake was negative in tumors with no or only moderate contrast enhancement on MRI, detecting only 21 of 23 (91%) HGG. In addition, [¹⁸F]-FLT uptake was mainly restricted to cT1 tumor areas on MRI and [¹⁸F]-FLT volumes strongly correlated with cT1 volumes (r = 0.841, p<0.001). In contrast, [¹⁸F]-FET PET detected 22 of 23 (96%) HGG. [¹⁸F]-FET uptake beyond areas of cT1 was found in 61% of cases and [¹⁸F]-FET volumes showed only a moderate correlation with cT1 volumes (r = 0.573, p<0.001). Metabolic tumor volumes beyond cT1 tumor areas were significantly larger for [¹⁸F]-FET compared to [¹⁸F]-FLT tracer uptake (8.3 vs. 2.7 cm³, p<0.001).

Conclusion

In HGG [¹⁸F]-FET but not [¹⁸F]-FLT PET was able to detect metabolic active tumor tissue beyond contrast enhancing tumor on MRI. In contrast to [¹⁸F]-FET, blood-brain barrier breakdown seems to be a prerequisite for [¹⁸F]-FLT tracer uptake.     

Automatic Segmentation of Eight Tissue Classes in Neonatal Brain MRI

Purpose

Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs.

Materials and Methods

In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient.

Results

The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard.

Conclusion

The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical trials in neonates.     

Maternal Smoking during Pregnancy and Fetal Organ Growth: A Magnetic Resonance Imaging Study

Objective

To study whether maternal cigarette smoking during pregnancy is associated with alterations in the growth of fetal lungs, kidneys, liver, brain, and placenta.

Design

A case-control study, with operators performing the image analysis blinded.

Setting

Study performed on a research-dedicated magnetic resonance imaging (MRI) scanner (1.5 T) with participants recruited from a large teaching hospital in the United Kingdom.

Participants

A total of 26 pregnant women (13 current smokers, 13 non smokers) were recruited; 18 women (10 current smokers, 8 nonsmokers) returned for the second scan later in their pregnancy.

Methods

Each fetus was scanned with MRI at 22–27 weeks and 33–38 weeks gestational age (GA).

Main outcome measures

Images obtained with MRI were used to measure volumes of the fetal brain, kidneys, lungs, liver and overall fetal size, as well as placental volumes.

Results

Exposed fetuses showed lower brain volumes, kidney volumes, and total fetal volumes, with this effect being greater at visit 2 than at visit 1 for brain and kidney volumes, and greater at visit 1 than at visit 2 for total fetal volume. Exposed fetuses also demonstrated lower lung volume and placental volume, and this effect was similar at both visits. No difference was found between the exposed and nonexposed fetuses with regards to liver volume.

Conclusion

Magnetic resonance imaging has been used to show that maternal smoking is associated with reduced growth of fetal brain, lung and kidney; this effect persists even when the volumes are corrected for maternal education, gestational age, and fetal sex. As expected, the fetuses exposed to maternal smoking are smaller in size. Similarly, placental volumes are smaller in smoking versus nonsmoking pregnant women.     

Fully Automated Segmentation of the Pons and Midbrain Using Human T1 MR Brain Images

Purpose

This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction.

Methods

This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer''s Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics.

Results

The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15).

Conclusions

Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.     

Agreement of Mammographic Measures of Volumetric Breast Density to MRI

Background

Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.

Purpose

To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.

Materials and Methods

Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.

Results

Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R² values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.

Conclusion

Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.     

Infant brain atlases from neonates to 1- and 2-year-olds

Background

Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.

Methodology

To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies.

Conclusions

We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.     

Gated F-18 FDG PET for Assessment of Left Ventricular Volumes and Ejection Fraction Using QGS and 4D-MSPECT in Patients with Heart Failure: A Comparison with Cardiac MRI

Purpose

Ventricular function is a powerful predictor of survival in patients with heart failure (HF). However, studies characterizing gated F-18 FDG PET for the assessment of the cardiac function are rare. The aim of this study was to prospectively compare gated F-18 FDG PET and cardiac MRI for the assessment of ventricular volume and ejection fraction (EF) in patients with HF.

Methods

Eighty-nine patients with diagnosed HF who underwent both gated F-18 FDG PET/CT and cardiac MRI within 3 days were included in the analysis. Left ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), and EF were obtained from gated F-18 FDG PET/CT using the Quantitative Gated SPECT (QGS) and 4D-MSPECT software.

Results

LV EDV and LV ESV measured by QGS were significantly lower than those measured by cardiac MRI (both P<0.0001). In contrast, the corresponding values for LV EDV for 4D-MSPECT were comparable, and LV ESV was underestimated with borderline significance compared with cardiac MRI (P = 0.047). LV EF measured by QGS and cardiac MRI showed no significant differences, whereas the corresponding values for 4D-MSPECT were lower than for cardiac MRI (P<0.0001). The correlations of LV EDV, LV ESV, and LV EF between gated F-18 FDG PET/CT and cardiac MRI were excellent for both QGS (r = 0.92, 0.92, and 0.76, respectively) and 4D-MSPECT (r = 0.93, 0.94, and 0.75, respectively). However, Bland-Altman analysis revealed a significant systemic error, where LV EDV (−27.9±37.0 mL) and ESV (−18.6±33.8 mL) were underestimated by QGS.

Conclusion

Despite the observation that gated F-18 FDG PET/CT were well correlated with cardiac MRI for assessing LV function, variation was observed between the two imaging modalities, and so these imaging techniques should not be used interchangeably.     

Comparison of positron emission tomography/computed tomography imaging and ultrasound in surveillance of head and neck cancer – The 3-year experience of the ENT Department in Poznan

Background

Posttreatment surveillance for the local and regional recurrence of the head and neck squamous cell carcinoma often requires a multimodality techniques that include PET combined with CT, MRI, US.

Aim

The purpose of this study is to compare the diagnostic performance of two imaging techniques (PET/CT and US), and their combined use for the detection of a subclinical regional recurrence in patients after HNSCC treatment.

Materials and methods

83 patients after completion of the HNSCC treatment underwent both US and PET/CT on the mean follow-up of 14 months after initial treatment.

Results

The sensitivity and specificity of PET/CT were 86% and 82%, respectively; US values reached 81% and 87%, respectively. PPV was 79% for PET/CT, and 83% for US. NPV was 89% for PET/CT, and 85% for US. The overall accuracy for PET/CT and US was 84% for both methods.

Conclusion

US could be regarded as complementary to PET/CT as the procedures with highest sensitivity, specificity and NPV for detecting subclinical regional recurrences after HNSCC treatment.     

Corticospinal tract-sparing intensity-modulated radiotherapy treatment planning

Aim

To establish intensity-modulated radiotherapy (IMRT) planning procedures that spare the corticospinal tract by integrating diffusion tensor tractography into the treatment planning software.

Background

Organs at risk are generally contoured according to the outline of the organ as demonstrated by CT or MRI. But a part of the organ with specific function is difficult to protect, because such functional part of the organ cannot be delineated on CT or conventional sequence of MRI.

Methods

Diagnostic and treatment planning images of glioblastoma patients who had been treated by conventional 3-dimensional conformal radiotherapy were used for re-planning of IMRT. Three-dimensional fiber maps of the corticospinal tracts were created from the diffusion tensors obtained from the patients before the surgery, and were blended with the anatomical MR images (i.e. gadolinium-enhanced T1-weighted images or T2-weighted images). DICOM-formatted blended images were transferred and fused to the planning CT images. Then, IMRT plans were attempted.

Results

The corticospinal tracts could be contoured as organs at risk (OARs), because the blended images contained both anatomical information and fiber-tract maps. Other OARs were contoured in a way similar to that of ordinary IMRT planning. Gross tumor volumes, clinical target volumes, planning target volumes, and other OARs were contoured on the treatment planning software, and IMRT plans were made.

Conclusions

IMRT plans with diminished doses to the corticospinal tract were attained. This technique enabled us to spare specific neuron fibers as OARs which were formerly “invisible” and to reduce the probability of late morbidities.     

Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

The impact of PET/CT scanning on the size of target volumes,radiation exposure of organs at risk,TCP and NTCP,in the radiotherapy planning of non-small cell lung cancer

Aim

To compare radiotherapy plans made according to CT and PET/CT and to investigate the impact of changes in target volumes on tumour control probability (TCP), normal tissue complication probability (NTCP) and the impact of PET/CT on the staging and treatment strategy.

Background

Contemporary studies have proven that PET/CT attains higher sensitivity and specificity in the diagnosis of lung cancer and also leads to higher accuracy than CT alone in the process of target volume delineation in NSCLC.

Materials and methods

Between October 2009 and March 2012, 31 patients with locally advanced NSCLC, who had been referred to radical radiotherapy were involved in our study. They all underwent planning PET/CT examination. Then we carried out two separate delineations of target volumes and two radiotherapy plans and we compared the following parameters of those plans: staging, treatment purpose, the size of GTV and PTV and the exposure of organs at risk (OAR). TCP and NTCP were also compared.

Results

PET/CT information led to a significant decrease in the sizes of target volumes, which had the impact on the radiation exposure of OARs. The reduction of target volume sizes was not reflected in the significant increase of the TCP value. We found that there is a very strong direct linear relationship between all evaluated dosimetric parameters and NTCP values of all evaluated OARs.

Conclusions

Our study found that the use of planning PET/CT in the radiotherapy planning of NSCLC has a crucial impact on the precise determination of target volumes, more precise staging of the disease and thus also on possible changes of treatment strategy.     

Computer-Assisted Interpretation of the EEG Background Pattern: A Clinical Evaluation

Objective

Interpretation of the EEG background pattern in routine recordings is an important part of clinical reviews. We evaluated the feasibility of an automated analysis system to assist reviewers with evaluation of the general properties in the EEG background pattern.

Methods

Quantitative EEG methods were used to describe the following five background properties: posterior dominant rhythm frequency and reactivity, anterior-posterior gradients, presence of diffuse slow-wave activity and asymmetry. Software running the quantitative methods were given to ten experienced electroencephalographers together with 45 routine EEG recordings and computer-generated reports. Participants were asked to review the EEGs by visual analysis first, and afterwards to compare their findings with the generated reports and correct mistakes made by the system. Corrected reports were returned for comparison.

Results

Using a gold-standard derived from the consensus of reviewers, inter-rater agreement was calculated for all reviewers and for automated interpretation. Automated interpretation together with most participants showed high (kappa > 0.6) agreement with the gold standard. In some cases, automated analysis showed higher agreement with the gold standard than participants. When asked in a questionnaire after the study, all participants considered computer-assisted interpretation to be useful for every day use in routine reviews.

Conclusions

Automated interpretation methods proved to be accurate and were considered to be useful by all participants.

Significance

Computer-assisted interpretation of the EEG background pattern can bring consistency to reviewing and improve efficiency and inter-rater agreement.     

A New Atlas Localization Approach for Subthalamic Nucleus Utilizing Chinese Visible Human Head Datasets

Objective

To study the possibility of Chinese visible human (CVH) head datasets as brain atlas for locating the subthalamic nucleus (STN) before deep brain stimulation (DBS) surgery.

Methods

Optimal head axial and coronal 3.0T, T2-weighted magnetic resonance images (MRI) of 30 patients were obtained. Cross-sectional head images of four CVH head datasets were chosen to establish an average CVH model. All MRI sequences were registered to the CVH model via fiducials in X-, Y-, and Z-direction, respectively, within the same stereotactic space. The correlations between red nucleus (RN) and fiducials, the accuracy of MRI-to-CVH registration, and the coordinate value differences of RN gravity center between MRI and CVH were tested.

Results

The mean gravity center coordinate values and ranges of STN in CVH were measured; The X coordinate value of RN positively correlates with the most anterior point of putamen(PU-A), the lateral edges of putamens (PU-L), and width of the third ventricle, negatively correlated with the patients’ age; The minimal mean errors were when no fiducials were used when locating RN between the MRI and the CVH average model; There were no significant differences of RN in X- and Y-direction between MRI sequences and CVH.

Conclusion

CVH head datasets can be used as brain atlas for Chinese STN localization. Moreover, average coordinate values of the gravity center and the ranges of STN in CVH can be considered as references for locating STN.     

Identification of Neonatal White Matter on DTI: Influence of More Inclusive Thresholds for Atlas Segmentation

Purpose

Semi-automated diffusion tensor imaging (DTI) analysis of white matter (WM) microstructure offers a clinically feasible technique to assess neonatal brain development and provide early prognosis, but is limited by variable methods and insufficient evidence regarding optimal parameters. The purpose of this research was to investigate the influence of threshold values on semi-automated, atlas-based brain segmentation in very-low-birth-weight (VLBW) preterm infants at near-term age.

Materials and Methods

DTI scans were analyzed from 45 VLBW preterm neonates at near-term-age with no brain abnormalities evident on MRI. Brain regions were selected with a neonatal brain atlas and threshold values: trace <0.006 mm²/s, fractional anisotropy (FA)>0.15, FA>0.20, and FA>0.25. Relative regional volumes, FA, axial diffusivity (AD), and radial diffusivity (RD) were compared for twelve WM regions.

Results

Near-term brain regions demonstrated differential effects from segmentation with the three FA thresholds. Regional DTI values and volumes selected in the PLIC, CereP, and RLC varied the least with the application of different FA thresholds. Overall, application of higher FA thresholds significantly reduced brain region volume selected, increased variability, and resulted in higher FA and lower RD values. The lower threshold FA>0.15 selected 78±21% of original volumes segmented by the atlas, compared to 38±12% using threshold FA>0.25.

Conclusion

Results indicate substantial and differential effects of atlas-based DTI threshold parameters on regional volume and diffusion scalars. A lower, more inclusive FA threshold than typically applied for adults is suggested for consistent analysis of WM regions in neonates.     

Cerebral Blood Volume Analysis in Glioblastomas Using Dynamic Susceptibility Contrast-Enhanced Perfusion MRI: A Comparison of Manual and Semiautomatic Segmentation Methods

Purpose

To compare the reproducibilities of manual and semiautomatic segmentation method for the measurement of normalized cerebral blood volume (nCBV) using dynamic susceptibility contrast-enhanced (DSC) perfusion MR imaging in glioblastomas.

Materials and Methods

Twenty-two patients (11 male, 11 female; 27 tumors) with histologically confirmed glioblastoma (WHO grade IV) were examined with conventional MR imaging and DSC imaging at 3T before surgery or biopsy. Then nCBV (means and standard deviations) in each mass was measured using two DSC MR perfusion analysis methods including manual and semiautomatic segmentation method, in which contrast-enhanced (CE)-T1WI and T2WI were used as structural imaging. Intraobserver and interobserver reproducibility were assessed according to each perfusion analysis method or each structural imaging. Interclass correlation coefficient (ICC), Bland-Altman plot, and coefficient of variation (CV) were used to evaluate reproducibility.

Results

Intraobserver reproducibilities on CE-T1WI and T2WI were ICC of 0.74–0.89 and CV of 20.39–36.83% in manual segmentation method, and ICC of 0.95–0.99 and CV of 8.53–16.19% in semiautomatic segmentation method, repectively. Interobserver reproducibilites on CE-T1WI and T2WI were ICC of 0.86–0.94 and CV of 19.67–35.15% in manual segmentation method, and ICC of 0.74–1.0 and CV of 5.48–49.38% in semiautomatic segmentation method, respectively. Bland-Altman plots showed a good correlation with ICC or CV in each method. The semiautomatic segmentation method showed higher intraobserver and interobserver reproducibilities at CE-T1WI-based study than other methods.

Conclusion

The best reproducibility was found using the semiautomatic segmentation method based on CE-T1WI for structural imaging in the measurement of the nCBV of glioblastomas.     

11C-Acetate PET Imaging in Patients with Multiple Sclerosis

Background

Activation of glial cells is a cardinal feature in multiple sclerosis (MS) pathology, and acetate has been reported to be selectively uptaken by astrocytes in the CNS. The aim of this study was to investigate the efficacy of PET with ¹¹C-acetate for MS diagnosis.

Materials and Methods

Six patients with relapsing-remitting MS and 6 healthy volunteers (HV) were enrolled. The ¹¹C-acetate brain uptake on PET was measured in patients with MS and HV. Volume-of-interest analysis of cerebral gray and white matter based on the segmentation technique for co-registered MRI and voxel-based statistical parametric analysis were performed. Correlation between ¹¹C-acetate uptake and the lesion number in T1- and T2- weighted MR images were also assessed.

Results

The standardized uptake value (SUV) of ¹¹C-acetate was increased in both white and gray matter in MS patients compared to HV. Voxel-based statistical analysis revealed a significantly increased SUV relative to that in the bilateral thalami (SUVt) in a broad area of white matter, particularly in the subcortical white matter of MS patients. The numbers of T2 lesions and T1 black holes were significantly correlated with SUV of ¹¹C-acetate in white and gray matter.

Conclusions

The ¹¹C-acetate uptake significantly increased in MS patients and correlated to the number of MRI lesions. These preliminary data suggest that ¹¹C-acetate PET can be a useful clinical examination for MS patients.     

MRI Findings in 77 Children with Non-Syndromic Autistic Disorder

Background

The clinical relevance of MR scanning in children with autism is still an open question and must be considered in light of the evolution of this technology. MRI was judged to be of insufficient value to be included in the standard clinical evaluation of autism according to the guidelines of the American Academy of Neurology and Child Neurology Society in 2000 [1]. However, this statement was based on results obtained from small samples of patients and, more importantly, included mostly insufficient MRI sequences. Our main objective was to evaluate the prevalence of brain abnormalities in a large group of children with a non-syndromic autistic disorder (AD) using T1, T2 and FLAIR MRI sequences.

Methodology

MRI inspection of 77 children and adolescents with non-syndromic AD (mean age 7.4±3.6) was performed. All met the DSM-IV and ADI –R criteria for autism. Based on recommended clinical and biological screenings, we excluded patients with infectious, metabolic or genetic diseases, seizures or any other neurological symptoms. Identical MRI inspections of 77 children (mean age 7.0±4.2) without AD, developmental or neurological disorders were also performed. All MRIs were acquired with a 1.5-T Signa GE (3-D T1-FSPGR, T2, FLAIR coronal and axial sequences). Two neuroradiologists independently inspected cortical and sub-cortical regions. MRIs were reported to be normal, abnormal or uninterpretable.

Principal Findings

MRIs were judged as uninterpretable in 10% (8/77) of the cases. In 48% of the children (33/69 patients), abnormalities were reported. Three predominant abnormalities were observed, including white matter signal abnormalities (19/69), major dilated Virchow–Robin spaces (12/69) and temporal lobe abnormalities (20/69). In all, 52% of the MRIs were interpreted as normal (36/69 patients).

Conclusions

An unexpectedly high rate of MRI abnormalities was found in the first large series of clinical MRI investigations in non-syndromic autism. These results could contribute to further etiopathogenetic research into autism.     

A Statistical Parametric Mapping Toolbox Used for Voxel-Wise Analysis of FDG-PET Images of Rat Brain

Purpose

PET (positron emission tomography) imaging researches of functional metabolism using fluorodeoxyglucose (¹⁸F-FDG) of animal brain are important in neuroscience studies. FDG-PET imaging studies are often performed on groups of rats, so it is desirable to establish an objective voxel-based statistical methodology for group data analysis.

Material and Methods

This study establishes a statistical parametric mapping (SPM) toolbox (plug-ins) named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain, in which an FDG-PET template and an intracranial mask image of rat brain in Paxinos & Watson space were constructed, and the default settings were modified according to features of rat brain. Compared to previous studies, our constructed rat brain template comprises not only the cerebrum and cerebellum, but also the whole olfactory bulb which made the later cognitive studies much more exhaustive. And with an intracranial mask image in the template space, the brain tissues of individuals could be extracted automatically. Moreover, an atlas space is used for anatomically labeling the functional findings in the Paxinos & Watson space. In order to standardize the template image with the atlas accurately, a synthetic FDG-PET image with six main anatomy structures is constructed from the atlas, which performs as a target image in the co-registration.

Results

The spatial normalization procedure is evaluated, by which the individual rat brain images could be standardized into the Paxinos & Watson space successfully and the intracranial tissues could also be extracted accurately. The practical usability of this toolbox is evaluated using FDG-PET functional images from rats with left side middle cerebral artery occlusion (MCAO) in comparison to normal control rats. And the two-sample t-test statistical result is almost related to the left side MCA.

Conclusion

We established a toolbox of SPM8 named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain.