Martin Noergaard is a biomedical engineer (BSc and MSc) by training from the Technical University of Denmark and the University of Copenhagen, specializing in medical imaging, biomedical signal processing and machine learning. Next, he did his PhD in neuroscience with the title "optimizing preprocessing pipelines in PET/MR neuroimaging" at the University of Copenhagen, in collaboration with University of Toronto, and the Martinos Center (MGH/Harvard-MIT). Martin finished his PhD in only 2.5 years. Martin has a strong expertise in medical image analysis and statistics, and is heavily involved in data sharing initiatives (USA, UK & EU), evaluation/optimization of data analysis pipelines for PET/MRI/fMRI brain imaging (multiverse analyses), and development of open source tools and software for analyzing neuroimaging data.
Thesis link: https://nru.dk/index.php/misc?task=download.send&id=644&catid=4&m=0
Honors & Awards
DFF International Postdoc Fellowship (grant of 211,000 USD), Stanford University (01/07/2020-31/12/2022)
Carlsberg Foundation Internationalization Fellowship 2019 (grant of 105,000 USD) - declined for DFF, Stanford University (01/07/2020-31/12/2022)
Lundbeckfonden travel stipend 2017 (grant of 10,000 USD), MGH/Harvard-MIT Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts, USA (01/08/2017-01/01/2018)
University of Copenhagen travel stipend 2017 (grant of 5,500 USD), MGH/Harvard-MIT Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts, USA (01/08/2017-01/01/2018)
Young Researcher of the Year at Rigshospitalet 2017 (FYF) - 3rd place (grant of 1,000 USD), Rigshospitalet, Copenhagen University Hospital (2017)
PhD, University of Copenhagen, Neuroscience (2019)
MSc, Technical University of Denmark, Biomedical Engineering (2015)
BSc, Technical University of Denmark, Biomedical Engineering (2013)
- PET-BIDS, an extension to the brain imaging data structure for positron emission tomography. Scientific data 2022; 9 (1): 65
A High-Resolution In Vivo Atlas of the Human Brain's Benzodiazepine Binding Site of GABAA Receptors.
Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. For decades, several in vivo and ex vivo techniques have been used to highlight the mechanisms of the GABA system, however, no studies have currently combined the techniques to create a high-resolution multimodal view of the GABA system. Here, we present a quantitative high-resolution in vivo atlas of the human brain benzodiazepine receptor sites (BZR) located on postsynaptic ionotropic GABAA receptors (GABAARs), generated on the basis of in vivo [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on ex vivo autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association between BZR protein density and ex vivo mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein. This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.
View details for DOI 10.1016/j.neuroimage.2021.117878
View details for PubMedID 33610745
Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data.
View details for DOI 10.1177/0271678X211015101
View details for PubMedID 33993794
False positive rates in positron emission tomography (PET) voxelwise analyses.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Issues with inflated false positive rates (FPRs) in brain imaging have recently received significant attention. However, to what extent FPRs present a problem for voxelwise analyses of Positron Emission Tomography (PET) data remains unknown. In this work, we evaluate the FPR using real PET data under group assignments that should yield no significant results after correcting for multiple comparisons. We used data from 159 healthy participants, imaged with the serotonin transporter ([11C]DASB; N = 100) or the 5-HT4 receptor ([11C]SB207145; N = 59). Using this null data, we estimated the FPR by performing 1,000 group analyses with randomly assigned groups of either 10 or 20, for each tracer, and corrected for multiple comparisons using parametric Monte Carlo simulations (MCZ) or non-parametric permutation testing. Our analyses show that for group sizes of 10 or 20, the FPR for both tracers was 5-99% using MCZ, much higher than the expected 5%. This was caused by a heavier-than-Gaussian spatial autocorrelation, violating the parametric assumptions. Permutation correctly controlled the FPR in all cases. In conclusion, either a conservative cluster forming threshold and high smoothing levels, or a non-parametric correction for multiple comparisons should be performed in voxelwise analyses of brain PET data.
View details for DOI 10.1177/0271678X20974961
View details for PubMedID 33241770
Guidelines for the content and format of PET brain data in publications and archives: A consensus paper
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM
It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.
View details for DOI 10.1177/0271678X20905433
View details for Web of Science ID 000514043700001
View details for PubMedID 32065076
Different preprocessing strategies lead to different conclusions: A [C-11]DASB-PET reproducibility study
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM
Positron emission tomography (PET) neuroimaging provides unique possibilities to study biological processes in vivo under basal and interventional conditions. For quantification of PET data, researchers commonly apply different arrays of sequential data analytic methods ("preprocessing pipeline"), but it is often unknown how the choice of preprocessing affects the final outcome. Here, we use an available data set from a double-blind, randomized, placebo-controlled [11C]DASB-PET study as a case to evaluate how the choice of preprocessing affects the outcome of the study. We tested the impact of 384 commonly used preprocessing strategies on a previously reported positive association between the change from baseline in neocortical serotonin transporter binding determined with [11C]DASB-PET, and change in depressive symptoms, following a pharmacological sex hormone manipulation intervention in 30 women. The two preprocessing steps that were most critical for the outcome were motion correction and kinetic modeling of the dynamic PET data. We found that 36% of the applied preprocessing strategies replicated the originally reported finding (p < 0.05). For preprocessing strategies with motion correction, the replication percentage was 72%, whereas it was 0% for strategies without motion correction. In conclusion, the choice of preprocessing strategy can have a major impact on a study outcome.
View details for DOI 10.1177/0271678X19880450
View details for Web of Science ID 000491454400001
View details for PubMedID 31575336
Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [C-11]DASB PET study
2019; 199: 466–79
Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [11C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.
View details for DOI 10.1016/j.neuroimage.2019.05.055
View details for Web of Science ID 000478780200043
View details for PubMedID 31158479
View details for PubMedCentralID PMC6688914
Cerebral serotonin transporter measurements with [C-11]DASB: A review on acquisition and preprocessing across 21 PET centres
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM
2019; 39 (2): 210–22
Positron Emission Tomography (PET) imaging has become a prominent tool to capture the spatiotemporal distribution of neurotransmitters and receptors in the brain. The outcome of a PET study can, however, potentially be obscured by suboptimal and/or inconsistent choices made in complex processing pipelines required to reach a quantitative estimate of radioligand binding. Variations in subject selection, experimental design, data acquisition, preprocessing, and statistical analysis may lead to different outcomes and neurobiological interpretations. We here review the approaches used in 105 original research articles published by 21 different PET centres, using the tracer [11C]DASB for quantification of cerebral serotonin transporter binding, as an exemplary case. We highlight and quantify the impact of the remarkable variety of ways in which researchers are currently conducting their studies, while implicitly expecting generalizable results across research groups. Our review provides evidence that the foundation for a given choice of a preprocessing pipeline seems to be an overlooked aspect in modern PET neuroscience. Furthermore, we believe that a thorough testing of pipeline performance is necessary to produce reproducible research outcomes, avoiding biased results and allowing for better understanding of human brain function.
View details for DOI 10.1177/0271678X18770107
View details for Web of Science ID 000457647200002
View details for PubMedID 29651896
View details for PubMedCentralID PMC6365604
Preprocessing, Prediction and Significance : Framework and Application to Brain Imaging
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 (Lecture Notes in Computer Science, volume 11767)
Springer Nature Switzerland AG 2019. 2019: 196–204
View details for DOI 10.1007/978-3-030-32251-9_22
The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data
2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
View details for DOI 10.1109/PRNI.2018.8423962
Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.
2021; 10 (8)
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
View details for DOI 10.1093/gigascience/giab051
View details for PubMedID 34414422
Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.
Human brain mapping
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state-of-the-art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi-atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi-atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images.
View details for DOI 10.1002/hbm.25604
View details for PubMedID 34322940
Characterisation of Children's Head Motion for Magnetic Resonance Imaging With and Without General Anaesthesia
Frontiers in Radiology
2021; 1: 1-10
View details for DOI 10.3389/fradi.2021.789632
Seasonality-resilient individuals downregulate their cerebral 5-HT transporter binding in winter - A longitudinal combined C-11-DASB and C-11-SB207145 PET study
2018; 28 (10): 1151–60
We have recently shown that the emergence and severity of seasonal affective disorder (SAD) symptoms in the winter is associated with an increase in cerebral serotonin (5-HT) transporter (SERT) binding. Intriguingly, we also found that individuals resilient to SAD downregulate their cerebral SERT binding in the winter. In the present paper, we provide an analysis of the SERT- and 5-HT dynamics as indexed by 5-HT4 receptor (5-HT4R) binding related to successful stress coping. We included 46 11C-DASB positron emission tomography (PET) scans (N = 23, 13 women, age: 26 ± 6 years) and 14 11C-SB207145 PET scans (7 participants, 3 women, age: 25 ± 3 years) from 23 SAD-resilient Danes. Data was collected longitudinally in summer and winter. We found that compared to the summer, raphe nuclei and global brain SERT binding decreased significantly in the winter (praphe = 0.003 and pglobal = 0.003) and the two measures were positively correlated across seasons (summer: R2 = 0.33, p = .004, winter: R2 = 0.24, p = .018). A voxel-based analysis revealed prominent changes in SERT in clusters covering both angular gyri (0.0005 < pcorrected < 0.0016), prefrontal cortices (0.00087 < pcorrected < 0.0039) and the posterior temporal and adjacent occipital cortices (0.0001 < pcorrected < 0.0066). We did not observe changes in 5-HT4R binding, suggesting that 5-HT levels remained stable across seasons. We conclude that resilience to SAD is associated with a global downregulation of SERT levels in winter which serves to keep 5-HT levels across seasons.
View details for DOI 10.1016/j.euroneuro.2018.06.004
View details for Web of Science ID 000445764300007
View details for PubMedID 30077433
Low 5-HT1B receptor binding in the migraine brain: A PET study
2018; 38 (3): 519–27
Background The pathophysiology of migraine may involve dysfunction of serotonergic signaling. In particular, the 5-HT1B receptor is considered a key player due to the efficacy of 5-HT1B receptor agonists for treatment of migraine attacks. Aim To examine the cerebral 5-HT1B receptor binding in interictal migraine patients without aura compared to controls. Methods Eighteen migraine patients, who had been migraine free for >48 hours, and 16 controls were scanned after injection of the 5-HT1B receptor specific radioligand [11C]AZ10419369 for quantification of cerebral 5-HT1B receptor binding. Patients who reported migraine <48 hours after the PET examination were excluded from the final analysis. We defined seven brain regions involved in pain modulation as regions of interest and applied a latent variable model (LVM) to assess the group effect on binding across these regions. Results Our data support a model wherein group status predicts the latent variable ( p = 0.038), with migraine patients having lower 5-HT1B receptor binding across regions compared to controls. Further, in a whole-brain voxel-based analysis, time since last migraine attack correlated positively with 5-HT1B receptor binding in the dorsal raphe and in the midbrain. Conclusion We report here for the first time that migraine patients have low 5-HT1B receptor binding in pain modulating regions, reflecting decreased receptor density. This is either a primary constitutive trait of the migraine brain or secondary to repeated exposure to migraine attacks. We also provide indirect support for the dorsal raphe 5-HT1B receptors being temporarily downregulated during the migraine attack, presumably in response to higher cerebral serotonin levels in the ictal phase.
View details for DOI 10.1177/0333102417698708
View details for Web of Science ID 000429427000012
View details for PubMedID 28730894
High brain serotonin levels in migraine between attacks: A 5-HT4 receptor binding PET study
2018; 18: 97–102
Migraine has been hypothesized to be a syndrome of chronic low serotonin (5-HT) levels, but investigations of brain 5-HT levels have given equivocal results. Here, we used positron emission tomography (PET) imaging of the 5-HT4 receptor as a proxy for brain 5-HT levels. Given that the 5-HT4 receptor is inversely related to brain 5-HT levels, we hypothesized that between attacks migraine patients would have higher 5-HT4 receptor binding compared to controls. Eighteen migraine patients without aura (migraine free >48 h), and 16 age- and sex-matched controls underwent PET scans after injection of [11C]SB207145, a specific 5-HT4 receptor radioligand. An investigator blinded to group calculated a neocortical mean [11C]SB207145 binding potential (BPND). Three migraine patients reported a migraine attack within 48 h after the scan and were excluded from the primary analysis. Comparing 15 migraine patients and 16 controls, we found that migraine patients have significantly lower neocortical 5-HT4 receptor binding than controls (0.60 ± 0.09 vs. 0.67 ± 0.05, p = .024), corrected for 5-HTTLPR genotype, sex and age. We found no association between 5-HT4 receptor binding and attack frequency, years with migraine or time since last migraine attack. Our finding of lower 5-HT4 receptor binding in migraine patients is suggestive of higher brain 5-HT levels. This is in contrast with the current belief that migraine is associated with low brain 5-HT levels. High brain 5-HT levels may represent a trait of the migraine brain or it could be a consequence of migraine attacks.
View details for DOI 10.1016/j.nicl.2018.01.016
View details for Web of Science ID 000433169000010
View details for PubMedID 29387527
View details for PubMedCentralID PMC5790018
Brain Networks Implicated in Seasonal Affective Disorder: A Neuroimaging PET Study of the Serotonin Transporter
FRONTIERS IN NEUROSCIENCE
2017; 11: 614
Background: Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption to the environmental stressor of winter in females with the short 5-HTTLPR genotype, a specific subgroup previously reported to be at increased risk for developing SAD. Methods: Nineteen females, either S' carriers (LG- and S-carriers) without SAD (N = 13, mean age 23.6 ± 3.2 year, range 19-28) or S' carriers with SAD (N = 6, mean age 23.7 ± 2.4, range 21-26) were PET-scanned with [11C]DASB during both summer and winter seasons (asymptomatic and symptomatic phase, 38 scans in total) in randomized order, defined as a 12-week interval centered on summer or winter solstice. We used a multivariate Partial Least Squares (PLS) approach with NPAIRS split-half cross-validation, to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD. Results: We identified a pattern of 5-HTT levels, distinguishing females with SAD from those without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only significant in the winter. Female S' carriers with SAD, in turn, displayed robustly increased 5-HTT levels in the ventral striatum (bilaterally), right orbitofrontal cortex, middle frontal gyrus (bilaterally), extending to the left supramarginal gyrus, left precentral gyrus and left postcentral gyrus during winter compared to female S' carriers without SAD. Limitations: The study is preliminary and limited by small sample size in the SAD group (N = 6). Conclusions: These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females with the short 5-HTTLPR genotype more vulnerable to persistent stressors like winter. The affected brain regions comprise a distributed set of areas responsive to emotion, voluntary, and planned movement, executive function, and memory. The preliminary findings provide additional insight into the neurobiological components through which the anatomical distribution of serotonergic discrepancies between individuals genetically predisposed to SAD, but with different phenotypic presentations during the environmental stressor of winter, may constitute a potential biomarker for resilience against developing SAD.
View details for DOI 10.3389/fnins.2017.00614
View details for Web of Science ID 000414319500001
View details for PubMedID 29163018
View details for PubMedCentralID PMC5682039
Feasibility of Multiparametric Imaging with PET/MR in Head and Neck Squamous Cell Carcinoma
JOURNAL OF NUCLEAR MEDICINE
2017; 58 (1): 69–74
The purpose of this study was to investigate and assess the correlation and reproducibility of multiparametric imaging in head and neck cancer patients.Twenty-one patients were included in this prospective scan-rescan study. All patients were scanned twice on an integrated PET and MRI scanner. Gross tumor volumes were defined on T2-weighted MR images, and volumes of interest were defined on diffusion-weighted MRI and 18F-FDG PET (VOIDWI, VOIPET). Overlap between volumes was assessed as a percentwise overlap. 18F-FDG uptake and diffusion were measured using SUV and apparent diffusion coefficient, and correlation was tested across and within patients and as a voxel-by-voxel analysis.Seventeen patients were available for correlation analysis, and 12 patients were available for assessment of tumor overlap. The median tumor overlap between VOIDWI and VOIPET was 82% (VOIDWI in VOIPET) and 62% (VOIPET in VOIDWI) on scan 1 and scan 2, respectively. Across patients, the correlation between SUV and apparent diffusion coefficient was weak and nonsignificant. However, in individual patients a weak but significant correlation was identified on a voxel-by-voxel basis.In multiparametric imaging with the integrated PET/MR scanner, the VOIs from DWI and 18F-FDG PET were both within the target volume for radiotherapy and overlapped substantially although not completely. No correlation between 18F-FDG uptake and DWI could be found across patients, but within individual patients a statistically significant, but weak, voxel-by-voxel correlation was found. The findings suggest that information on glucose uptake and diffusion coefficient carries complementary information of interest that may be relevant for radiotherapy treatment planning.
View details for DOI 10.2967/jnumed.116.180091
View details for Web of Science ID 000391343400020
View details for PubMedID 27609790
Estimation of Regional Seasonal Variations in SERT-levels using the FreeSurfer PET pipeline: a reproducibility study
MICCAI workshop on Computational Methods for Molecular Imaging 2015
View details for DOI 10.13140/RG.2.1.4365.7688