SU‐E‐I‐178: Spatial Analysis of PET Images Using 3D Moment Invariants (3DMI)

In normal free-breathing PET/CT imaging protocol, a single CT image of the patient is obtained before the PET scan, which is inadequate for attenuation correction of the free-breathing PET data. The aim of this study is to design an automatic workflow to correct mismatch and reduce motion blurring of free-breathing PET and single-scan CT data. First, the PET data was divided into four equal-count respiratory frames using data-driven respiratory gating method. Modified attenuation map was used in during reconstruction to obtain mismatch-free gated PET images. Second, the respiratory phase of the original CT image was identified automatically using mutual information (MI) as the similarity metric. Two regions of interests (ROIs) near the lungs were segmented from CT image. The mutual information (MI) inside the ROIs of each gated PET reconstruction and the CT image was measured for all gates and the phase with highest MI value is identified as the correct respiratory phase of CT image. Third, using the respiratory phase of the CT image as the reference gate, MVFs from every other gate to the reference gate was estimated using a B-spline based multi-resolution image registration algorithm. Fourth, the original CT image with identified respiratory phase was transformed to every other frame using estimated MVFs to obtain attenuation maps for all respiratory frames. Finally, image reconstruction of gated PET data was repeated using phase-matched gated PET-CT data pairs. MVFs estimated in step 3 were applied to this set of gated PET images to obtain motion compensated PET image. This method was applied to clinical datasets using both F18-FDG and Ga68 -DOTA-NOC tracer. The proposed method successfully identified the respiratory phase of the CT image and generated phase-matched CT images for all respiratory phases of PET data. Final reconstructed PET data using the proposed method showed much-reduced mismatch artifacts and motion blurring, as well as improved statistics.

Segmentation of a region containing the brain tumor from 3D magnetic resonance imaging (MRI) data can help physicians to diagnose accurately the size and malignancy of the tumor. However, manual segmentation is time consuming and involves risk of having inaccurate result. In this paper, an automatic method of segmenting the region of interest (ROI), a region encompassing the brain tumor and its neighborhood, is proposed based on voxel statistics. In the proposed method, first possible candidate selection is performed utilizing intensity characteristics of tumor region in the FLAIR and T1 images of MRI data. Next, a cubic shaped 3D mean filtering operation is applied on the whole volumetric data to obtain filtered volume where some random intensity behavior is expected to be eliminated. Finally, from the resulting 3D FLAIR data, ROI is extracted based on cumulative distribution function of intensity. It is found that the extracted ROI offers significant reduction of the overall MRI volume without losing tumor data. The proposed ROI extraction scheme is tested on 20 real life high grade tumor cases obtained from a widely used database and a very satisfactory performance is obtained in terms of segmentation accuracy, overall volume reduction and computational time.

Neocortical atrophy reduces PET signal intensity, potentially affecting the diagnostic efficacy of β-amyloid (Aβ) brain PET imaging. This study investigated whether partial-volume effect correction (PVEC), adjusting for this atrophy bias, improves the accuracy of (18)F-florbetaben Aβ PET. We analyzed (18)F-florbetaben PET and MRI data obtained from 3 cohorts. The first was 10 patients with probable Alzheimer disease (AD) and 10 age-matched healthy controls (HCs), the second was 31 subjects who underwent in vivo imaging and postmortem histopathology for Aβ plaques, and the third was 5 subjects who underwent PET and MRI at baseline and 1 y later. The imaging data were coregistered and segmented. PVEC was performed using the voxel-based modified Müller-Gärtner method (PVELab, SPM8). From the PET data, regional and composite SUV ratios (SUVRs) with and without PVEC were obtained. In the MRI data, mesial temporal lobe atrophy was determined by the Scheltens mesial temporal atrophy scale and gray matter volumes by voxel-based morphometry. In cohort 1, PVEC increased the effect on AD-versus-HC discrimination from a Cohen d value of 1.68 to 2.0 for composite SUVRs and from 0.04 to 1.04 for mesial temporal cortex SUVRs. The PVEC-related increase in mesial temporal cortex SUVR correlated with the Scheltens score (r = 0.84, P < 0.001), and that of composite SUVR correlated with the composite gray matter volume (r = -0.75, P < 0.001). In cohort 2, PVEC increased the correlation coefficient between mesial temporal cortex SUVR and histopathology score for Aβ plaque load from 0.28 (P = 0.09) to 0.37 (P = 0.03). In cohort 3, PVEC did not affect the composite SUVR dynamics over time for the Aβ-negative subject. This finding was in contrast to the 4 Aβ-positive subjects, in 2 of whom PVEC changed the composite SUVR dynamics. The influence of PVEC on (18)F-florbetaben PET data is associated with the degree of brain atrophy. Thus, PVEC increases the ability of (18)F-florbetaben PET to discriminate between AD patients and HCs, to detect Aβ plaques in the atrophic mesial temporal cortex, and potentially to evaluate changes in brain Aβ load over time. As such, the use of PVEC should be considered for quantitative (18)F-florbetaben PET scans, especially in assessing patients with brain atrophy.

In this article we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance imaging (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs’ activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task, we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia dataset, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.

Cognitive theories of obsessive-compulsive disorder (OCD) stress the importance of dysfunctional beliefs in the development and maintenance of the disorder. However, a neurobiological understanding of these cognitive models, including thought-action fusion (TAF), is surprisingly lacking. Thus, this functional magnetic resonance imaging study aimed to investigate whether altered functional connectivity (FC) is associated with the TAF paradigm in OCD patients. Forty-one OCD patients and 47 healthy controls (HCs) participated in a functional magnetic resonance imaging study using a TAF task, in which they were asked to read the name of a close or a neutral person in association with positive and negative statements. The conventional TAF condition (negative statements/close person) induced significant FC between the regions of interest (ROIs) identified using multivoxel pattern analysis and the visual association areas, default mode network subregions, affective processing, and several subcortical regions in both groups. Notably, sparser FC was observed in OCD patients. Further analysis confined to the cortico-striato-thalamo-cortical (CSTC) and affective networks demonstrated that OCD patients exhibited reduced ROI FC with affective regions and greater ROI FC with CSTC components in the TAF condition compared to HCs. Within the OCD patients, middle cingulate cortex-insula FC was correlated with TAF and responsibility scores. Our TAF paradigm revealed altered context-dependent engagement of the CSTC and affective networks in OCD patients. These findings suggest that the neurobiology of cognitive models corresponds to current neuroanatomical models of OCD. Further, they elucidate the underlying neurobiological mechanisms of OCD at the circuit-based level.

The PET/MRI hybrid system became eligible for staging the diagnosis of malignant tumors, metastasis, and recurrence using 18F-Fluorodeoxyglucose (FDG). Inserting PET in an MRI is technically challenging and requires PET detectors to be compatible with the magnetic field. The key technology that enables this is the semiconductor-based photomultiplier tube. The advantage of the PET/MRI hybrid system is not simply that MRI has replaced computed tomography (CT), but rather that PET and MRI data can be acquired simultaneously. The high-precision fusion images show improved diagnostic accuracy. Positional misalignment due to movements, such as respiratory motion, as seen on PET/CT, can be minimized through simultaneous acquisition, and higher-precision fusion images can be obtained by performing PET and MRI acquisitions under respiratory gating. One of the disadvantages of PET/MRI over PET/CT is that CT cannot be used for gamma-ray attenuation correction, which is necessary for PET image reconstruction. Although a method has been established to generate a pseudo-CT image from MRI for attenuation correction, generating a pseudo-CT image that includes bone components is particularly difficult. In PET/MRI systems approved for clinical use, bone components are not considered for attenuation correction outside the head region. Recently, active research has been conducted utilizing deep learning to generate pseudo-CT images with bone components from MRI, regardless of the anatomical region, or to perform attenuation correction using PET data alone. Recently, new PET tracers have been approved in Japan. As PET/MRI can produce highly accurate fusion images with MRI, these tracers and target lesions are likely to benefit more from PET/MRI than from PET/CT. As such, the scope of PET/MRI applications is expected to continue to expand, and efforts should be made to promote its widespread adoption so that more facilities can utilize this technology.

Respiratory motion is a known image degradation factor in PET imaging. A lot of research has been done in the field of motion correction of PET data. With the recent development of full-body PET/MRI, the morphological data provided by MRI is a promising source of motion information. MRI-based motion correction of PET data is a topic of active research. These approaches, however, usually do not take into account the available information provided by PET data. In this work we show results of a phantom study using a joint motion estimation approach that incorporates motion information from both PET and MRI data.

ObjectiveBulimia nervosa (BN) and binge eating disorder (BED) are eating disorders (EDs) characterized by recurrent binge eating (BE) episodes. Overlap exists between ED diagnostic groups, with BE episodes presenting one clinical feature that occurs transdiagnostically. Neuroimaging of the responses of those with BN and BED to disorder-specific stimuli, such as food, is not extensively investigated. Furthermore, to our knowledge, there have been no previous published studies examining the neural response of individuals currently experiencing binge eating, to low energy foods. Our objective was to examine the neural responses to both low energy and high energy food images in three emotive categories (disgust; fear; and happy) in BN and BED participants.MethodsNineteen females with BN (n = 14) or BED (n = 5), comprising the binge eating group (BEG; N = 19), and 19 age-matched healthy control (HC)’s completed thorough clinical assessment prior to functional MRI (fMRI). Neural response to low energy and high energy foods and non-food images was compared between groups using whole-brain exploratory analyses, from which six regions of interest (ROI) were then selected: frontal, occipital, temporal, and parietal lobes; insula and cingulate.ResultsIn response to low energy food images, the BEG demonstrated differential neural responses to all three low energy foods categories (disgust; fear; and happy) compared to HCs. Correlational analyses found a significant association between frequency of binge episodes and diminished temporal lobe and greater occipital lobe response. In response to high energy food images, compared to HC’s, the BEG demonstrated significantly decreased neural activity in response to all high energy food images. The HC’s had significantly greater neural activity in the limbic system, occipital lobe, temporal lobe, frontal lobe, and limbic system in response to high energy food images.ConclusionResults in the low energy food condition indicate that binge frequency may be related to increased aberrant neural responding. Furthermore, differences were found between groups in all ROI’s except the insula. The neural response seen in the BEG to disgust food images may indicate disengagement with this particular stimuli. In the high energy food condition, results demonstrate that neural activity in BN and BED patients may decrease in response to high energy foods, suggesting disengagement with foods that may be more consistent with those consumed during a binge eating episode.

PurposeWhether the intrinsic functional connectivity pattern of the default mode network (DMN) is involved in the progression of cognitive decline in Parkinson’s disease (PD) remains unclear. This study aimed to investigate the intrinsic functional connectivity (FC) pattern of the DMN anchored on the posterior cingulate cortex (PCC) in patients with PD by resting-state functional magnetic resonance imaging (fMRI).MethodsFifty patients with PD and 50 healthy controls (HCs) were included for resting-state fMRI scanning. A seed-based FC method was used to reveal FC patterns in the DMN with region of interest (ROI) in the PCC. Relationships between FC patterns and disease severity (UPDRS-III) were detected.ResultsCompared with the HCs, the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus. In the PD group, the increased FC values in the right precuneus were significantly and positively correlated with motor severity as assessed with UPDRS-III scores (rho = 0.337, p = 0.02).ConclusionOur result highlights that the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus in the DMN. The altered connectivity pattern in the DMN may play a crucial role in the neurophysiological mechanism of cognitive decline in patients with PD. These findings might provide new insights into neural mechanisms of cognitive decline in PD.

Magnetic resonance (MR) relaxometry is of increasing scientific relevance in neurodegenerative disorders but is still not established in clinical routine. Several studies have investigated relaxation time alterations in disease-specific areas in Parkinson's disease (PD), all using manually drawn regions of interest (ROI). Implementing MR relaxometry into the clinical setting involves the reduction of time needed for postprocessing using an investigator-independent and reliable approach. The aim of this study was to evaluate an automated, atlas-based ROI method for evaluating T2* relaxation times in patients with PD. Automated atlas-based ROI analysis of quantitative T2* maps were generated from 20PD patients and 20controls. To test for the accuracy of the atlas-based ROI segmentation, we evaluated the spatial overlap in comparison with manually segmented ROIs using the Dice similarity coefficient (DSC). Additionally, we tested for group differences using our automated atlas-based ROIs of the putamen, globus pallidus, and substantia nigra. Agood spatial overlap accuracy was shown for the automated segmented putamen (mean DSC, 0.64± 0.04) and was inferior but still acceptable for the substantia nigra (mean DSC, 0.50± 0.17). Based on our automated defined ROI selection, a significant decrease of T2* relaxation time was found in the putamen as well as in the internal and external globus pallidus in PD patients compared with healthy controls. Automated digital brain atlas-based approaches are reliable, more objective and time-efficient, and therefore have the potential to replace the time-consuming manual drawing of ROIs.

Abnormal caudate nucleus activity in patients with depressive disorder: Meta-analysis of task-based functional magnetic resonance imaging studies with behavioral domain

To explore the functions of temporal parietal junction (TPJ) as parts of attention networks in the pathogenesis of online game addiction using resting-state functional magnetic resonance imaging (fMRI). A total of 17 online game addicts (OGA) were recruited as OGA group and 17 healthy controls during the same period were recruited as CON group. The neuropsychological tests were performed for all of them to compare the inter-group differences in the results of Internet Addiction Test (IAT) and attention functions. All fMRI data were preprocessed after resting-state fMRI scanning. Then left and right TPJ were selected as regions of interest (ROIs) to calculate the linear correlation between TPJ and entire brain to compare the inter-group differences. Obvious differences existed between OGA group (71 ± 5 scores) and CON group (19 ± 7 scores) in the IAT results and attention function (P < 0.05). Compared with the controls, right TPJ in online game addicts showed decreased functional connectivity with bilateral ventromedial prefrontal cortex (VMPFC), bilateral hippocampal gyrus and bilateral amygdaloid nucleus, but increased functional connectivity with right cuneus.However, left TPJ demonstrated decreased functional connectivity with bilateral superior frontal gyrus and bilateral middle frontal gyrus, but increased functional connectivity with bilateral cuneus (P < 0.05). Altered functional connectivity of TPJ reflected its dysfunction in online game addicts.It suggests that TPJ is an important component of attention networks participating in the generation of online game addiction.

Differing from common 2D images, a texture map, since it is used to project onto a 3D model in 3D space, not only contains 2D texture information, but also implicitly associates certain 3D geometric information. Related to this, an effective 3D geometry-dependent texture map compression method with hybrid region of interest (ROI) coding is proposed in this paper. We regard the visually important area of the texture map as the ROI. To acquire the visually important areas of the texture map, we take into account information from both the 3D geometry and 2D texture maps, depicting the saliency of the textured model, distortion of the texture mapping, and boundary of the texture atlas. These visually important areas are expressed as a visual importance map. According to the particularity of the texture map, a hybrid ROI coding method that utilizes Max-Shift and an improved post compression rate distortion (PCRD) technique is presented, guided by this visual importance map. To find the exact wavelet coefficients pertaining to these ROIs before carrying out the hybrid ROI coding, this paper proposes a stochastic coefficient priority mask map computational method. Experimental results show that the visually important areas of the texture image have a better visual effect and that a good rendering result can be obtained from the texture mapping.

Kinetic modelling in PET requires the arterial input function (AIF), defined as the time-activity curve (TAC) in plasma. This measure is challenging to obtain in mice due to low blood volumes, resulting in a reliance on image-based methods for AIF derivation. We present a comparison of PET- and MR-based region-of-interest (ROI) analysis to obtain image-derived AIFs from the left ventricle (LV) of a mouse model. ROI-based partial volume correction (PVC) was performed to improve quantification. MRI and dynamic PET images were obtained from a recent study investigating treatment effects in 12 mice following myocardial infarction [1], where half the mice received a new treatment and half did not. Prospectively gated MRI (4.7T Bruker BioSpec, FLASH TR/TE 400/3ms, spatial resolution 140μm in 1mm slices) were acquired prior to PET acquisition (approx. 25MBq 18F-FDG bolus, 45 minute emission listmode acquisition reconstructed with 3DRP in four cardiac frames) on a split-magnet PET camera [2]. Images were co-registered using SPMMouse [3] (see Figure ​Figure11). Figure 1 Mouse heart MR (left) and fused with 18F-FDG static PET (right). Arrow indicates infarcted region. AIF extraction AIFs were obtained by taking mean time courses from LV Lumen ROIs, shown in Figure ​Figure2.2. The regional geometric transfer matrix (GTM) method was applied for PVC [4], using ROIs drawn on either the co-registered MR images or directly onto the last dynamic frame PET images. ROIs covered LV lumen, myocardium, lungs/body and background. Patlak [5] analysis was performed to evaluate glucose metabolism. Figure 2 AIFs and TACs derived for single subject using (A) MR ROIs, (B) PET ROIs with PVC and (C) MR ROIs with PVC. Insets detail first 500s Uncorrected AIFs and myocardial TACs produced by manual ROI delineation displayed contamination with myocardial signal. AIFs and myocardial curves became distinguishable if GTM PVC was applied. Only MR-based PVC produced significant differences (p<0.05) in Ki values between the treated and untreated groups (see Table ​Table11). Table 1 GTM-based PVC gives best results in mice when ROIs are based on MRI data, due to its high-resolution and excellent soft-tissue contrast.

FRI0637 MUSCLES IN MYOSITIS PATIENTS WITH NORMAL MRI APPEARANCE HAVE HIGHER QUANTITATIVE T2 COMPARED TO THOSE IN HEALTHY CONTROLS