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Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

ABSTRACT: OBJECTIVE:Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features. METHODS:Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels. RESULTS:Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%). CONCLUSIONS:Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.

SUBMITTER: Tan YL

PROVIDER: S-EPMC5748006 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

Tan Yee-Leng YL Kim Hosung H Lee Seunghyun S Tihan Tarik T Ver Hoef Lawrence L Mueller Susanne G SG Barkovich Anthony James AJ Xu Duan D Knowlton Robert R

NeuroImage 20171031

<h4>Objective</h4>Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized co ...[more]

PMID: 29097316

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Project description:One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Project description:Purpose: The superior frontal sulcus (SFS), located in the prefrontal and premotor cortex, is considered as one of the common locations of focal cortical dysplasia (FCD). However, the characteristics of seizures arising from this area are incompletely known. The primary purpose of this study was to investigate the clinical features and the epileptic networks of seizures originating from the SFS. Methods: We included seventeen patients with type II FCD within the SFS. SFS was identified both visually and automatically. Semiological features were evaluated and grouped. Interictal 18FDG-PET imaging in all patients was compared to controls using statistical parametric mapping (SPM-PET). In those subjects with stereoelectroencephalography (SEEG), two different quantitative intracranial electroencephalography analyses were applied. Finally, the locations of the SFS-related hypometabolic regions and epileptogenic zones (EZs) were transformed into standard space for group analysis. Results: We identified two semiological groups. Group 1 (9/17) showed elementary motor signs (head version and tonic posturing), while group 2 (8/17) exhibited complex motor behavior (fear, hypermotor, and ictal pouting). Based on SPM-PET, an SFS-supplementary motor area (SMA) epileptic propagation network was found in group 1, and an SFS-middle cingulate cortex (MCC)-pregenual anterior cingulate cortex (pACC) propagation network was discovered in group 2. Intracranial EEG analysis suggested similar affected structures with high epileptogenicity. The SFS-related hypometabolic regions and EZs in these groups showed a posterior-anterior spatial relationship. Conclusions: Even though originating from the spatially restricted cortex, SFS seizures can be divided into two groups based on semiological features. The SFS-SMA and SFS-MCC-pACC epileptic propagation networks may play pivotal roles in the generation of different semiologies. The posterior-anterior spatial relationship of both hypometabolic regions and EZs provides potentially useful information for distinguishing different types of SFS seizures and surgical evaluation.

Project description:Noninvasive methods are needed to explore the heterogeneous tumor microenvironment and its modulation by therapy. Hybrid PET/MRI systems are being developed for small-animal and clinical use. The advantage of these integrated systems depends on their ability to provide MR images that are spatially coincident with simultaneously acquired PET images, allowing combined functional MRI and PET studies of intratissue heterogeneity. Although much effort has been devoted to developing this new technology, the issue of quantitative and spatial fidelity of PET images from hybrid PET/MRI systems to the tissues imaged has received little attention. Here, we evaluated the ability of a first-generation, small-animal MRI-compatible PET scanner to accurately depict heterogeneous patterns of radiotracer uptake in tumors.Quantitative imaging characteristics of the MRI-compatible PET (PET/MRI) scanner were evaluated with phantoms using calibration coefficients derived from a mouse-sized linearity phantom. PET performance was compared with a commercial small-animal PET system and autoradiography in tumor-bearing mice. Pixel and structure-based similarity metrics were used to evaluate image concordance among modalities. Feasibility of simultaneous PET/MRI functional imaging of tumors was explored by following (64)Cu-labeled antibody uptake in relation to diffusion MRI using cooccurrence matrix analysis.The PET/MRI scanner showed stable and linear response. Activity concentration recovery values (measured and true activity concentration) calculated for 4-mm-diameter rods within linearity and uniform activity rod phantoms were near unity (0.97 ± 0.06 and 1.03 ± 0.03, respectively). Intratumoral uptake patterns for both (18)F-FDG and a (64)Cu-antibody acquired using the PET/MRI scanner and small-animal PET were highly correlated with autoradiography (r > 0.99) and with each other (r = 0.97 ± 0.01). On the basis of these data, we performed a preliminary study comparing diffusion MRI and radiolabeled antibody uptake patterns over time and visualized movement of antibodies from the vascular space into the tumor mass.The MRI-compatible PET scanner provided tumor images that were quantitatively accurate and spatially concordant with autoradiography and the small-animal PET examination. Cooccurrence matrix approaches enabled effective analysis of multimodal image sets. These observations confirm the ability of the current simultaneous PET/MRI system to provide accurate observations of intratumoral function and serve as a benchmark for future evaluations of hybrid instrumentation.

Project description:Background During simultaneous PET/MRI, flexible MRI surface coils that lay on the patient are often omitted from PET attenuation correction processing, leading to quantification bias in PET images. Purpose To identify potential PET image quality improvement by using a recently developed lightweight MRI coil technology for the anterior array (AA) surface coil in both a phantom and in vivo study. Materials and Methods A phantom study and a prospective in vivo study were performed with a PET/CT scanner under three conditions: (a) no MRI surface coil (standard of reference), (b) traditional AA coil, and (c) lightweight AA coil. AA coils were not used in attenuation correction processing to emulate clinical PET/MRI. For the phantom study, PET images were reconstructed with and without time of flight (TOF) to assess quantification accuracy and uniformity. The in vivo study consisted of 10 participants (mean age, 66 years ± 10 [standard deviation]; six men) referred for a PET/CT oncologic examination who had undergone imaging between October 2019 and February 2020. Assessment of image quantification bias (defined as the standard error of the mean values) was conducted by comparing mean liver region of interest standardized uptake values with the no-coil standard of reference. A Wilcoxon signed-rank test was used to establish significance. Results For TOF and non-TOF, respectively, the phantom study revealed a mean PET quantification bias of -9.0% and -8.6% with the traditional AA coil and a mean PET quantification bias of -4.3% and -4.0% with the lightweight AA coil. The coefficients of variation reduced from 4.3% and 6.2% with the traditional AA coil to 2.1% and 2.7% with the lightweight AA coil, which demonstrated a homogeneity benefit from the lightweight coil that was greater with, versus without, TOF reconstruction. For the in vivo study, the mean liver standardized uptake value error was -5.9% with the traditional AA coil (P = .002 vs no coil) and -2.4% with the lightweight AA coil (P = .004 vs no coil). Conclusion The lightweight anterior array coil reduced PET image quantification bias by more than 50% compared with the traditional coil. Using the lightweight coil and performing time of flight-based reconstruction each reduced the variation of error. © RSNA, 2020 Online supplemental material is available for this article.

Dataset Information

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

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Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

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