Dataset Information

Quantitative MR Image Analysis for Brian Tumor.

ABSTRACT: This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.

SUBMITTER: Shboul ZA

PROVIDER: S-EPMC5719874 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Quantitative MR Image Analysis for Brian Tumor.

Shboul Zeina A ZA Reza Sayed M S SMS Iftekharuddin Khan M KM

VipIMAGE 2017 : proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017. VipIMAGE (Conference) (2017 : Porto, Portugal) 20171013

This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) ...[more]

PMID: 29239394

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Project description:Background and Aims: To investigate the impact of MR bias field correction on response determination and survival prediction using volumetric tumor enhancement analysis in patients with infiltrative hepatocellular carcinoma, after transcatheter arterial chemoembolization (TACE). Methods: This study included 101 patients treated with conventional or drug-eluting beads TACE between the years of 2001 and 2013. Semi-automated 3D quantification software was used to segment and calculate the enhancing tumor volume (ETV) of the liver with and without bias-field correction on multi-phasic contrast-enhanced MRI before and 1-month after initial TACE. ETV (expressed as cm3) at baseline imaging and the relative change in ETV (as % change, ETV%) before and after TACE were used to predict response and survival, respectively. Statistical survival analyses included Kaplan-Meier curve generation and Cox proportional hazards modeling. Q statistics were calculated and used to identify the best cut-off value for ETV to separate responders and non-responders (ETV cm3). The difference in survival was evaluated between responders and non-responders using Kaplan-Meier and Cox models. Results: MR bias field correction correlated with improved response calculation from baseline MR as well as survival after TACE; using a 415 cm3 cut-off for ETV at baseline (hazard ratio: 2.00, 95% confidence interval: 1.23-3.26, p=0.01) resulted in significantly improved response prediction (median survival in patients with baseline ETV <415 cm3: 19.66 months vs. ≥415 cm3: 9.21 months, p<0.001, log-rank test). A ≥41% relative decrease in ETV (hazard ratio: 0.58, 95%confidence interval: 0.37-0.93, p=0.02) was significant in predicting survival (ETV ≥41%: 19.20 months vs. ETV <41%: 8.71 months, p=0.008, log-rank test). Without MR bias field correction, response from baseline ETV could be predicted but survival after TACE could not. Conclusions: MR bias field correction improves both response assessment and accuracy of survival prediction using whole liver tumor enhancement analysis from baseline MR after initial TACE in patients with infiltrative hepatocellular carcinoma.

Project description:BackgroundOptical coherence tomography (OCT) is an invaluable diagnostic tool for the detection and follow-up of retinal pathology in patients and experimental disease models. However, as morphological structures and layering in health as well as their alterations in disease are complex, segmentation procedures have not yet reached a satisfactory level of performance. Therefore, raw images and qualitative data are commonly used in clinical and scientific reports. Here, we assess the value of OCT reflectivity profiles as a basis for a quantitative characterization of the retinal status in a cross-species comparative study.MethodsSpectral-Domain Optical Coherence Tomography (OCT), confocal Scanning-Laser Ophthalmoscopy (SLO), and Fluorescein Angiography (FA) were performed in mice (Mus musculus), gerbils (Gerbillus perpadillus), and cynomolgus monkeys (Macaca fascicularis) using the Heidelberg Engineering Spectralis system, and additional SLOs and FAs were obtained with the HRA I (same manufacturer). Reflectivity profiles were extracted from 8-bit greyscale OCT images using the ImageJ software package (http://rsb.info.nih.gov/ij/).ResultsReflectivity profiles obtained from OCT scans of all three animal species correlated well with ex vivo histomorphometric data. Each of the retinal layers showed a typical pattern that varied in relative size and degree of reflectivity across species. In general, plexiform layers showed a higher level of reflectivity than nuclear layers. A comparison of reflectivity profiles from specialized retinal regions (e.g. visual streak in gerbils, fovea in non-human primates) with respective regions of human retina revealed multiple similarities. In a model of Retinitis Pigmentosa (RP), the value of reflectivity profiles for the follow-up of therapeutic interventions was demonstrated.ConclusionsOCT reflectivity profiles provide a detailed, quantitative description of retinal layers and structures including specialized retinal regions. Our results highlight the potential of this approach in the long-term follow-up of therapeutic strategies.

Project description:PurposeTo evaluate the diagnostic performance of three-dimensional ( 3D three-dimensional ) quantitative enhancement-based and diffusion-weighted volumetric magnetic resonance (MR) imaging assessment of hepatocellular carcinoma ( HCC hepatocellular carcinoma ) lesions in determining the extent of pathologic tumor necrosis after transarterial chemoembolization ( TACE transarterial chemoembolization ).Materials and methodsThis institutional review board-approved retrospective study included 17 patients with HCC hepatocellular carcinoma who underwent TACE transarterial chemoembolization before surgery. Semiautomatic 3D three-dimensional volumetric segmentation of target lesions was performed at the last MR examination before orthotopic liver transplantation or surgical resection. The amount of necrotic tumor tissue on contrast material-enhanced arterial phase MR images and the amount of diffusion-restricted tumor tissue on apparent diffusion coefficient ( ADC apparent diffusion coefficient ) maps were expressed as a percentage of the total tumor volume. Visual assessment of the extent of tumor necrosis and tumor response according to European Association for the Study of the Liver ( EASL European Association for the Study of the Liver ) criteria was performed. Pathologic tumor necrosis was quantified by using slide-by-slide segmentation. Correlation analysis was performed to evaluate the predictive values of the radiologic techniques.ResultsAt histopathologic examination, the mean percentage of tumor necrosis was 70% (range, 10%-100%). Both 3D three-dimensional quantitative techniques demonstrated a strong correlation with tumor necrosis at pathologic examination (R(2) = 0.9657 and R(2) = 0.9662 for quantitative EASL European Association for the Study of the Liver and quantitative ADC apparent diffusion coefficient , respectively) and a strong intermethod agreement (R(2) = 0.9585). Both methods showed a significantly lower discrepancy with pathologically measured necrosis (residual standard error [ RSE residual standard error ] = 6.38 and 6.33 for quantitative EASL European Association for the Study of the Liver and quantitative ADC apparent diffusion coefficient , respectively), when compared with non- 3D three-dimensional techniques ( RSE residual standard error = 12.18 for visual assessment).ConclusionThis radiologic-pathologic correlation study demonstrates the diagnostic accuracy of 3D three-dimensional quantitative MR imaging techniques in identifying pathologically measured tumor necrosis in HCC hepatocellular carcinoma lesions treated with TACE transarterial chemoembolization .

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Project description:Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

Dataset Information

Quantitative MR Image Analysis for Brian Tumor.

Publications

Quantitative MR Image Analysis for Brian Tumor.

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