Kevin T. Chen

An SPM8-Based Approach for Attenuation Correction Combining Segmentation and Nonrigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Methods: Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest–based analyses were performed. Results: The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest–based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. Conclusion: We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks.

Identification of Genes with Consistent Methylation Levels across Different Human Tissues

DNA methylation plays an important role in regulating cell growth and disease development. Methylation profiles are examined by bisulfite conversion; however, the lack of markers for bisulfite conversion efficiency and appropriate internal control genes remains a major challenge. To address these issues, we utilized two bioinformatics approaches, coefficients of variances and resampling tests, to identify probes showing stable methylation levels from several independent microarray datasets. Mass spectrometry validated the consistently high methylation levels of the five probes (N4BP2, EGFL8, CTRB1, TSPAN3, and ZNF690) in 13 human tissue types from 24 cell lines. Linear associations between detected methylation levels and methyl concentrations of DNA samples were further demonstrated in three genes (N4BP2, EGFL8, and CTRB1). To summarize, we identified five genes which may serve as internal controls for methylation studies by analyzing large-scale microarray data, and three of them can be used as markers for evaluating the efficiency of bisulfite conversion.

Anatomically-aided PET reconstruction using the kernel method

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

Masamune: a tool for automatic dynamic PET data processing, image reconstruction and integrated PET/MRI data analysis

We describe a novel semi-automated pipeline which integrates advanced data analysis tools for MR and PET with advanced PET reconstruction correction methods (partial volume effect correction [PVC], motion correction [MC], attenuation correction [AC]) in a user-friendly Matlab graphical user interface (GUI). The reconstruction and analysis GUI is written in Matlab. Computationally intensive tasks in the pipeline are automatically transferred to a high-performance computing cluster and retrieved. Descriptions of the commercial packages used can be found in their corresponding references. SPM8 [1] is used in MC and AC processing. Comkat [2] and PMOD [3] are used for kinetic modeling. FSL [4] and SPM8 are used for group analysis. Freesurfer [5] is used for regions-of-interest (ROI) definition and smoothing. Data preprocessing: Head-motion is derived from a number of sources: echo-planar MR images, MR-based motion navigators, and directly from the PET data when MR data is unavailable (e.g. during shimming). Subsequently, the ME-MPRAGE is reoriented to the reference position. Cortical and subcortical ROIs are labeled using FreeSurfer; similarly, the MPRAGE is registered to MNI-space for generating subject-specific atlases. Image reconstruction: An OP-OSEM algorithm is used for PET reconstruction [6]. MC [7] and PVC [8] can be performed using the results from data preprocessing. AC can be imported directly from CT, using MR-images [9], or through atlas-based methods. Automated Bolus Arrival Time (BAT) & Image-Derived Input Function: The singles count rate is recorded during PET acquisition. The BAT is determined by fitting a trilinear piecewise function and used as the reference time. Time-of-Flight MR can then be used to segment the arteries of the head and an image-derived input function can be determined using short frames. We presented a novel pipeline which interfaces with a number of different commercial software to provide improved PET data quantification.

MR-assisted PET motion correction in simultaneous PET/MRI studies of dementia subjects: MR-Assisted PET Motion correction

Subject motion in positron emission tomography (PET) studies leads to image blurring and artifacts; simultaneously acquired magnetic resonance imaging (MRI) data provides a means for motion correction (MC) in integrated PET/MRI scanners.

Nonlinear PET parametric image reconstruction with MRI information using kernel method

Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

New SPM8-based MRAC method for simultaneous PET/MR brain images: comparison with state-of-the-art non-rigid registration methods.

We describe a new MR-based attenuation correction (MRAC) method for neurological studies performed using integrated PET/MR scanners. The method, combining the advantages of image segmentation and atlas-based approaches to generate a high-resolution template, is based on the widely available SPM8 software and provides robust and accurate linear attenuation coefficients (LACs) for head while requiring minimal user interaction. Atlas generation: 3T MR and CT images from 15 glioblastoma subjects were used to generate the high-resolution atlas. MR images were segmented into 6 tissue classes: GM, WM, CSF, soft tissue, bone and air)[1]. Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm [2] to form the template. Atlas validation: The template was validated on 16 subjects. SyN [3] and IRTK [4], considered state-of-the-art for non-rigid image registration[5], were used for comparison. Final attenuation maps were created from the warped CT atlas following [6]. PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence) [7] and compared to the gold standard CT-based attenuation correction (CTAC). The qualitative and quantitative analysis of the attenuation maps revealed that the SPM8-based method produces very robust results (Figure ​(Figure1).1). In terms of the PET data quantification, we observed improvements of > 70% compared to the VIBE-based method (Table ​(Table11 and Figure ​Figure2).2). When compared to SyN-based image registration, the SPM8 approach showed improved global results on the brain area (Figures ​(Figures11 and ​and22). Figure 1 Comparison of LACs from a validation subject for our proposed method (A), the SyN method (B) and the manufacturer’s built-in Dixon method (C) to the gold standard CTAC (D). Image differences with respect to the gold standard CTAC of our method ... Table 1 Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon) Figure 2 PET images from a validation subject reconstructed with our proposed method (A), with the SyN method (B) and with the manufacturer’s built-in Dixon method (C), compared with the gold standard CTAC (D). Relative changes (in % with respect to gold ... We presented a new MRAC technique for brain images acquired on simultaneous PET/MR scanners. The new approach relies on segmentation- and atlas-based features to provide robust and more accurate LACs than using state-of-art non-rigid image registration while avoiding sophisticated user input or interaction.

Combined MR-assisted motion and partial volume effects corrections - impact on PET data quantification.

Our goal in this study was to characterize the combined effect of MR-assisted motion correction (MC) and partial volume effects correction (PVEC) on the estimation of [11C]NNC112 binding potential (BP) in healthy volunteers. 29 subjects were scanned on the Siemens 3T MR-BrainPET scanner prototype. Emission data were acquired in list mode format for 90-minutes following the i.v. administration of ~8 mCi of [11C]NNC112. The head attenuation map was obtained from the MPRAGE data using an atlas-based method. Head motion estimates were derived from the MR data and used to correct the PET data in LOR space before image reconstruction [1]. PVEC was applied to the motion corrected data using the region-based voxel-wise (RBV) method [2] and regions of interest (ROIs) defined from the MPRAGE images using FreeSurfer and the measured point-spread function [3]. BPnd for each of the ROIs was estimated in PMOD using the simplified reference tissue kinetic (SRTM) model and the cerebellum as a reference tissue. Maximum translations of up to 9 mm and rotations of up to 12 degrees have been observed in this group of subjects (Figure ​(Figure1).1). Less variability in the tissue time activity curves (TACs) was noted after MC (the curves before and after MC for a representative subject are shown in Figure ​Figure2).2). The percentage changes in BPnd after MC and PVEC revealed both under- and overestimation in the ROIs analyzed (Figure ​(Figure3).3). The cumulative effect exceeded 100% for some of the structures analyzed. Figure 1 Maximum translations (left) and rotations (right) measured in 29 healthy volunteers scanned for 90 minutes Figure 2 TACs in the left and right caudate and cerebellar cortices before (left) and after (right) MR-assisted motion correction Figure 3 Percent change in NNC112 BPnd after motion and partial volume effects corrections Significant motion and PVE were observed in all the subjects, biasing the PET estimates. The combined effect is difficult to predict, depending on the size and location of the structure of interest and patient compliance. Without addressing these issues, the value of the BPnd’s derived from these data is questionable.

An Efficient Approach to Perform MR-assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies

A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by factors including attenuation, subject motion, and limited spatial resolution. Using the information from one simultaneously acquired morphologic MR sequence with embedded navigators for MR motion correction (MC), we propose an efficient method, MR-assisted PET data optimization (MaPET), for attenuation correction (AC), PET MC, and anatomy-aided reconstruction. Methods: For AC, voxelwise linear attenuation coefficient maps were generated using an SPM8-based method on the MR volume. The embedded navigators were used to derive head motion estimates for event-based PET MC. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. Region-based analyses were performed to assess the quality of images generated through various stages of PET data optimization. Results: The optimized PET images reconstructed with MaPET were superior in image quality to images reconstructed using only AC, with high signal-to-noise ratio and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared with 3.12 and 0.570, P < 10−4 for both comparisons). The optimized images were also shown using the Cohen’s d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism. Conclusion: We have shown that the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion, and partial-volume effects corrections and that the MaPET method may enable more accurate assessment of pathologic changes in dementia and other brain disorders.

Compressive Sensing and Sparse Coding

Compressive sensing is a technique to acquire signals at rates proportional to the amount of information in the signal, and it does so by exploiting the sparsity of signals. This section discusses the fundamentals of compressive sensing, and how it is related to sparse coding.