Correction of partial volume effect in 18F-FDG PET brain studies using coregistered MR volumes: Voxel based analysis of tracer uptake in the white matter

Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis

Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis.

Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study).

Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data.

Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.

Spatial adaptive and transformer fusion network (STFNet) for low-count PET blind denoising with MRI

PURPOSE

Positron emission tomography (PET) has been widely used in various clinical applications. PET is a type of emission computed tomography and operates by positron annihilation radiation. With magnetic resonance imaging (MRI) providing anatomical information, joint PET/MRI reduces the radiation exposure risk of patients. Improved hardware and imaging algorithms have been proposed to further decrease the dose from radioactive tracers or the bed duration, but few methods focus on denoising low-count PET with MRI input. The existing methods are based on fixed conventional convolution and local attention, which do not sufficiently extract and fuse contextual and complementary information from multimodal input. There is still much room for improvement. Therefore, we propose a novel deep learning method for low-count PET/MRI denoising called the spatial-adaptive and transformer fusion network (STFNet), which consists of a Siamese encoder with a spatial-adaptive block (SA-block) and the transformer fusion encoder (TFE).

METHODS

Our proposed STFNet consists of a Siamese encoder with an SA-block, TFE, and two branches of the decoder. First, in the encoder, we adapt the SA-block in the Siamese encoder. The SA-block comprises deformable convolution with fusion modulation (DCFM) and two convolutional operations, which can promote network extraction of more relative and long-range contextual features. Second, the pixel-to-pixel TFE helps the network establish a local and global relationship between high-level feature maps of PET and MRI. In the decoder part, we design two branches for PET denoising and MRI translation, and predictions are obtained by trainable weighted summation. This proposed algorithm is implemented to predict synthetic standard-dose neck PET images from low-count neck PET images and MRI. Additionally, this method is compared with the existing U-Net and residual U-Net methods with and without MRI input.

RESULTS

To demonstrate the advantages of our method, we introduce configuration studies about TFE, ablation studies, and empirical comparative studies. Quantitative analyses are based on root mean square error (RSME), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and Pearson correlation coefficient (PCC). Additionally, qualitative results show the comparisons between our proposed method and other existing methods. All experimental results and visualizations show that our method achieves state-of-the-art performance in quantification and qualification.

CONCLUSIONS

Based on our experiments, STFNet performs better than existing methods in measurement and visualization. However, our proposed method may still be suboptimal because we apply only the L1 loss to train our data set, and the data set includes corrupted PET with different low counts. In the future, we may exploit a generative adversarial network (GAN)-based paradigm in our STFNet to further improve the visual quality.

Fascicle- and Glucose-Specific Deterioration in White Matter Energy Supply in Alzheimer's Disease

BACKGROUND

White matter energy supply to oligodendrocytes and the axonal compartment is crucial for normal axonal function. Although gray matter glucose hypometabolism is extensively reported in Alzheimer's disease (AD), glucose and ketones, the brain's two main fuels, are rarely quantified in white matter in AD.

OBJECTIVE

Using a dual-tracer PET method combined with a fascicle-specific diffusion MRI approach, robust to white matter hyper intensities and crossing fibers, we aimed to quantify both glucose and ketone metabolism in specific white matter fascicles associated with mild cognitive impairment (MCI; n = 51) and AD (n = 13) compared to cognitively healthy age-matched controls (Controls; n = 14).

METHODS

Eight white matter fascicles of the limbic lobe and corpus callosum were extracted and analyzed into fascicle profiles of five sections. Glucose (18F-fluorodeoxyglucose) and ketone (11C-acetoacetate) uptake rates, corrected for partial volume effect, were calculated along each fascicle.

RESULTS

The only fascicle with significantly lower glucose uptake in AD compared to Controls was the left posterior cingulate segment of the cingulum (-22%; p = 0.016). Non-significantly lower glucose uptake in this fascicle was also observed in MCI. In contrast to glucose, ketone uptake was either unchanged or higher in sections of the fornix and parahippocampal segment of the cingulum in AD.

CONCLUSION

To our knowledge, this is the first report of brain fuel uptake calculated along white matter fascicles in humans. Energetic deterioration in white matter in AD appears to be specific to glucose and occurs first in the posterior cingulum.

MRI-Driven PET Image Optimization for Neurological Applications

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

[18F]-Flutemetamol Uptake in Cortex and White Matter: Comparison with Cerebrospinal Fluid Biomarkers and [18F]-Fludeoxyglucose

Flutemetamol (¹⁸F-Flut) is an [¹⁸F]-labelled amyloid PET tracer with increasing availability. The main objectives of this study were to investigate 1) cerebrospinal fluid (CSF) Aβ 1-42 (Aβ₄₂) concentrations associated with regional ¹⁸F-Flut uptake, 2) associations between cortical ¹⁸F-Flut and [¹⁸F]-fludeoxyglucose (¹⁸F-FDG)-PET, and 3) the potential use of ¹⁸F-Flut in WM pathology. Cognitively impaired, nondemented subjects were recruited (n = 44). CSF was drawn, and ¹⁸F-Flut-PET, ¹⁸F-FDG-PET, and MRI performed. Our main findings were: 1) Different Alzheimer’s disease predilection areas showed increased ¹⁸F-Flut retention at different CSF Aβ₄₂ concentrations (posterior regions were involved at higher concentrations). 2) There were strong negative correlations between regional cortical ¹⁸F-Flut and ¹⁸F-FDG uptake. 3) Increased ¹⁸F-Flut uptake were observed in multiple subcortical regions in amyloid positive subjects, including investigated reference regions. However, WM hyperintensity ¹⁸F-Flut standardized uptake value ratios (SUVr) were not significantly different, thus we cannot definitely conclude that the higher uptake in ¹⁸F-Flut(+) is due to amyloid deposition. In conclusion, our findings support clinical use of CSF Aβ₄₂, putatively relate decreasing CSF Aβ₄₂ concentrations to a sequence of regional amyloid deposition, and associate amyloid pathology to cortical hypometabolism. However, we cannot conclude that ¹⁸F-Flut-PET is a suitable marker for WM pathology due to high aberrant WM uptake.

Magnetic Resonance-Derived Improvements in PET Imaging

Simultaneous PET-MR imaging improves deficiencies in PET images. The primary areas in which magnetic resonance (MR) has been applied to guide PET results are in correction for patient motion and in improving the effects of PET resolution and partial volume averaging. MR-guided motion correction of PET has been applied to respiratory, cardiac, and gross body movements and shown to improve lesion detectability and contrast. Partial volume correction or resolution improvement of PET governed by MR imaging anatomic information improves visualization of structures and quantitative accuracy. Evaluation in clinical applications is needed to determine their true impacts.

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

PURPOSE

The purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images.

METHODS

We exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient (DSC), sensitivity (SE), positive predictive value (PPV) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET/CT datasets of non-small cell lung cancer (NSCLC) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET/CT images. The proposed framework was applied to PET-only, CT-only and PET/CT datasets, and were compared to standard and improved fuzzy c-means (FCM) multimodal implementations.

RESULTS

A high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet-based HMT led to the best results with a DSC of 0.92 ± 0.11 compared to 0.89 ± 0.13 and 0.90 ± 0.12 for Intensity-based HMT and Wavelet-based HMT, respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT, even when considering multimodal implementations.

CONCLUSIONS

We evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain.

Magnetic resonance imaging perfusion is associated with disease severity and activity in multiple sclerosis

PURPOSE

The utility of perfusion-weighted imaging in multiple sclerosis (MS) is not well investigated. The purpose of this study was to compare baseline normalized perfusion measures in subgroups of newly diagnosed MS patients. We wanted to test the hypothesis that this method can differentiate between groups defined according to disease severity and disease activity at 1 year follow-up.

METHODS

Baseline magnetic resonance imaging (MRI) including a dynamic susceptibility contrast perfusion sequence was performed on a 1.5-T scanner in 66 patients newly diagnosed with relapsing-remitting MS. From the baseline MRI, cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) maps were generated. Normalized (n) perfusion values were calculated by dividing each perfusion parameter obtained in white matter lesions by the same parameter obtained in normal-appearing white matter. Neurological examination was performed at baseline and at follow-up approximately 1 year later to establish the multiple sclerosis severity score (MSSS) and evidence of disease activity (EDA).

RESULTS

Baseline normalized mean transit time (nMTT) was lower in patients with MSSS >3.79 (p = 0.016), in patients with EDA (p = 0.041), and in patients with both MSSS >3.79 and EDA (p = 0.032) at 1-year follow-up. Baseline normalized cerebral blood flow and normalized cerebral blood volume did not differ between these groups.

CONCLUSION

Lower baseline nMTT was associated with higher disease severity and with presence of disease activity 1 year later in newly diagnosed MS patients. Further longitudinal studies are needed to confirm whether baseline-normalized perfusion measures can differentiate between disease severity and disease activity subgroups over time.

Cerebrospinal Fluid Levels of Amyloid Beta 1-43 Mirror 1-42 in Relation to Imaging Biomarkers of Alzheimer's Disease

Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed.

Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent ¹⁸F-Flutemetamol PET. Seventy-eight subjects were assessed with ¹⁸F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging.

Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with ¹⁸F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing.

Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.

Decreased hippocampal translocator protein (18 kDa) expression in alcohol dependence: a [11C]PBR28 PET study

Repeated withdrawal from alcohol is clinically associated with progressive cognitive impairment. Microglial activation occurring during pre-clinical models of alcohol withdrawal is associated with learning deficits. We investigated whether there was microglial activation in recently detoxified alcohol-dependent patients (ADP), using [¹¹C]PBR28 positron emission tomography (PET), selective for the 18kDa translocator protein (TSPO) highly expressed in activated microglia and astrocytes. We investigated the relationship between microglial activation and cognitive performance. Twenty healthy control (HC) subjects (45±13; M:F 14:6) and nine ADP (45±6, M:F 9:0) were evaluated. Dynamic PET data were acquired for 90 min following an injection of 331±15 MBq [¹¹C]PBR28. Regional volumes of distribution (V_T) for regions of interest (ROIs) identified a priori were estimated using a two-tissue compartmental model with metabolite-corrected arterial plasma input function. ADP had an ~20% lower [¹¹C]PBR28 V_T, in the hippocampus (F(1,24) 5.694; P=0.025), but no difference in V_T in other ROIs. Hippocampal [¹¹C]PBR28 V_T was positively correlated with verbal memory performance in a combined group of HC and ADP (r=0.720, P<0.001), an effect seen in HC alone (r=0.738; P=0.001) but not in ADP. We did not find evidence for increased microglial activation in ADP, as seen pre-clinically. Instead, our findings suggest lower glial density or an altered activation state with lower TSPO expression. The correlation between verbal memory and [¹¹C]PBR28 V_T, raises the possibility that abnormalities of glial function may contribute to cognitive impairment in ADP.

Amyloid Dysmetabolism Relates to Reduced Glucose Uptake in White Matter Hyperintensities

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and cause of dementia and is characterized by amyloid plaques and neurofibrillary tangles. AD has traditionally been considered to primarily affect gray matter, but multiple lines of evidence also indicate white matter (WM) pathology and associated small-vessel cerebrovascular disease. WM glucose delivery and metabolism may have implications for local tissue integrity, and [¹⁸F]-fluorodeoxyglucose positron emission tomography (FDG-PET) may be helpful to assess neuroglial and axonal function in WM. Hypothesizing that affection of oligodendroglia will be associated with loss of glucose uptake, we aimed to investigate glucose metabolism in magnetic resonance imaging (MRI) white matter hyperintensities (WMHs) and normal-appearing WM in patients with and without evidence of amyloid plaques. Subjects with mild cognitive impairment or subjective cognitive decline were included and dichotomized according to pathological (Aβ+) or normal (Aβ−) concentrations of cerebrospinal fluid amyloid-β 1–42. A total of 50 subjects were included, of whom 30 subjects were classified as Aβ(+) and 20 subjects as Aβ(−). All subjects were assessed with MRI and FDG-PET. FDG-PET images were corrected for effects of partial voluming and normalized to cerebellar WM, before determining WMH FDG-uptake. Although there were no significant differences between the groups in terms of age, WMH volume, number of individual WMHs, or WMH distribution, we found significantly lower (p = 0.021) FDG-uptake in WMHs in Aβ(+) subjects (mean = 0.662, SD = 0.113) compared to Aβ(−) subjects (mean = 0.596, SD = 0.073). There were no significant group differences in the FDG-uptake in normal-appearing WM. Similar results were obtained without correction for effects of partial voluming. Our findings add to the evidence for a link between Aβ dysmetabolism and WM pathology in AD.

The relationship between cerebrospinal fluid markers of Alzheimer pathology and positron emission tomography tau imaging

The two primary molecular pathologies in Alzheimer's disease are amyloid-β plaques and tau-immunoreactive neurofibrillary tangles. Investigations into these pathologies have been restricted to cerebrospinal fluid assays, and positron emission tomography tracers that can image amyloid-β plaques. Tau tracers have recently been introduced into the field, although the utility of the tracer and its relationship to other Alzheimer biomarkers are still unknown. Here we examined tau deposition in 41 cognitively normal and 11 cognitively impaired older adults using the radioactive tau ligand (18)F-AV-1451 (previously known as T807) who also underwent a lumbar puncture to assess cerebrospinal fluid levels of total tau (t-tau), phosphorylated tau181 (p-tau181) and amyloid-β42 Voxel-wise statistical analyses examined spatial patterns of tau deposition associated with cognitive impairment. We then related the amount of tau tracer uptake to levels of cerebrospinal fluid biomarkers. All analyses controlled for age and gender and, when appropriate, the time between imaging and lumbar puncture assessments. Symptomatic individuals (Clinical Dementia Rating > 0) demonstrated markedly increased levels of tau tracer uptake. This elevation was most prominent in the temporal lobe and temporoparietal junction, but extended more broadly into parietal and frontal cortices. In the entire cohort, there were significant relationships among all cerebrospinal fluid biomarkers and tracer uptake, notably for tau-related cerebrospinal fluid markers. After controlling for levels of amyloid-β42, the correlations with tau uptake were r = 0.490 (P < 0.001) for t-tau and r = 0.492 (P < 0.001) for p-tau181 Within the cognitively normal cohort, levels of amyloid-β42, but not t-tau or p-tau181, were associated with elevated tracer binding that was confined primarily to the medial temporal lobe and adjacent neocortical regions. AV-1451 tau binding in the medial temporal, parietal, and frontal cortices is correlated with tau-related cerebrospinal fluid measures. In preclinical Alzheimer's disease, there is focal tauopathy in the medial temporal lobes and adjacent cortices.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PURPOSE

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image.

METHODS

The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy.

RESULTS

The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image.

CONCLUSIONS

In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Quantitative sodium MRI of the human brain at 9.4 T provides assessment of tissue sodium concentration and cell volume fraction during normal aging

Sodium ion homeostasis is a fundamental property of viable tissue, allowing the tissue sodium concentration to be modeled as the tissue cell volume fraction. The modern neuropathology literature using ex vivo tissue from selected brain regions indicates that human brain cell density remains constant during normal aging and attributes the volume loss that occurs with advancing age to changes in neuronal size and dendritic arborization. Quantitative sodium MRI performed with the enhanced sensitivity of ultrahigh-field 9.4 T has been used to investigate tissue cell volume fraction during normal aging. This cross-sectional study (n = 49; 21-80 years) finds that the in vivo tissue cell volume fraction remains constant in all regions of the brain with advancing age in individuals who remain cognitively normal, extending the ex vivo literature reporting constant neuronal cell density across the normal adult age range. Cell volume fraction, as measured by quantitative sodium MRI, is decreased in diseases of cell loss, such as stroke, on a time scale of minutes to hours, and in response to treatment of brain tumors on a time scale of days to weeks. Neurodegenerative diseases often have prodromal periods of decades in which regional neuronal cell loss occurs prior to clinical presentation. If tissue cell volume fraction can detect such early pathology, this quantitative parameter may permit the objective measurement of preclinical disease progression. This current study in cognitively normal aging individuals provides the basis for the pursuance of investigations directed towards such neurodegenerative diseases.

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures.

Combined PET/MR: Where are we now? Summary report of the second international workshop on PET/MR imaging April 8-12, 2013, Tubingen, Germany

This workshop was held a year after the initial positron emission tomography/magnetic resonance (PET/MR) workshop in Tübingen, which was recently reported in this journal. The discussions at the 2013 workshop, however, differed substantially from those of the initial workshop, attesting to the progress of combined PET/MR as an innovative imaging modality. Discussions were focused on the search for truly novel, unique clinical and research applications as well as technical issues such as reliable and accurate approaches for attenuation and scatter correction of PET emission data. The workshop provided hands-on experience with PET and MR imaging. In addition, structured and moderated open discussion sessions, including six dialogue boards and two roundtable discussions, provided input from current and future PET/MR imaging users. This summary provides a snapshot of the current achievements and challenges for PET/MR.