Comparison of a 3D CZT and conventional SPECT/CT system for quantitative Lu-177 SPECT imaging.

PURPOSE: Next-generation SPECT/CT systems with CdZnTe (CZT) digital detectors in a ring-like setup are emerging to perform quantitative Lu-177 SPECT imaging in clinical routine. It is essential to assess how the shorter acquisition time might affect the image quality and uncertainty on the mean absorbed dose of the tumors and organs at risk compared to a conventional system. METHODS: A NEMA Image Quality phantom was scanned with a 3D CZT SPECT/CT system (Veriton, by Spectrum Dynamics) using 6 min per bed position and with a conventional SPECT/CT system (Symbia T16, by Siemens) using 16 min per bed position. The sphere-to-background ratio was 12:1 and the background activity concentration ranged from 0.52 to 0.06 MBq/mL. A clinical reconstruction protocol for dosimetry purposes was determined for both systems by maximizing the sphere-to-background ratio while keeping the coefficient of variation of the background as low as possible. The corresponding image resolution was determined by the matching filter method and used for a dose uncertainty assessment of both systems following an established uncertainty model.. RESULTS: The optimized iterative reconstruction protocol included scatter and attenuation correction for both systems and detector response modeling for the Siemens system. For the 3D CZT system, 6 iterations and 8 subsets were combined with a Gaussian post-filter of 3 mm Full Width Half Maximum (FWHM) for post-smoothing. For the conventional system, 16 iterations and 16 subsets were applied with a Gaussian post-smoothing filter of 1 mm FWHM. For these protocols, the sphere-to-background ratio was 18.5% closer to the true ratio for the conventional system compared to the 3D CZT system when considering the four largest spheres. Meanwhile, the background coefficient of variation was very similar for both systems. These protocols resulted in SPECT image resolution of 14.8 mm and 13.6 mm for the 3D CZT and conventional system respectively. Based on these resolution estimates, a 50% dose uncertainty corresponded to a lesion volume of 28 mL for the conventional system and a lesion volume of 33 mL for the 3D CZT system. CONCLUSIONS: An optimized reconstruction protocol for a Veriton system with 6 min of acquisition time per bed position resulted in slightly higher dose uncertainties than a conventional Symbia system using 16 min of acquisition time per bed position. Therefore, a 3D CZT SPECT/CT allows to significantly reduce the acquisition times with only a very limited impact on dose uncertainties such that quantitative Lu-177 SPECT/CT imaging becomes much more accessible for treatment concurrent dosimetry. Nevertheless, the uncertainty of SPECT-based dose estimates remains high.

Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction.

PURPOSE: Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. METHODS: The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS: Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION: AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.

Cerebral glucose changes after chemotherapy and their relation to long-term cognitive complaints and fatigue.

Purpose: To investigate the short-term cerebral metabolic effects of intravenous chemotherapy and their association with long-term fatigue/cognitive complaints. Experimental design: Using [18F]-FDG-PET/CT whole-body scans, we retrospectively quantified relative cerebral glucose metabolism before and after neoadjuvant chemotherapy in a cohort of patients treated for non-metastatic breast cancer (2009-2019). Self-report of cognitive complaints and fatigue were prospectively assessed 7 ± 3 years after therapy. Metabolic changes were estimated with i) robust mixed-effects modelling in regions-of-interest (frontal, parietal, temporal, occipital, and insular cortex) and ii) general-linear modelling of whole-brain voxel-wise outcomes. iii) The association between metabolic changes and self-reported outcomes was evaluated using linear regression-analysis. Results: Of the 667 screened patients, 263 underwent PET/CT before and after chemotherapy and 183 (48 ± 9 years) met the inclusion criteria. After chemotherapy, decreased frontal and increased parietal and insular metabolism were observed (|ß|>0.273, pFDR <0.008). Separately, additional increased occipital metabolism after epiribucin+ cyclophosphamide (EC) and temporal metabolism after EC+ fluorouracil chemotherapy were observed (ß>0.244, pFDR ≤0.048). Voxel-based analysis (pcluster-FWE <0.001) showed decreased metabolism in the paracingulate gyrus (-3.2 ± 3.9%) and putamen (3.1 ± 4.1%) and increased metabolism in the lateral cortex (L=2.9 ± 3.1%) and pericentral gyri (3.0 ± 4.4%). Except for the central sulcus, the same regions showed changes in EC, but not in FEC patients. Of the 97 self-reported responders, 23% and 27% experienced extreme fatigue and long-term cognitive complaints, respectively, which were not associated with metabolic changes. Conclusion: Both hyper- and hypometabolism were observed after chemotherapy for breast cancer. Combined with earlier findings, this study could support inflammatory mechanisms resulting in relative hypermetabolism, mainly in the parietal/occipital cortices. As early metabolic changes did not precede long-term complaints, further research is necessary to identify vulnerable patients.

Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET.

Objective.Complete time of flight (TOF) sinograms of state-of-the-art TOF PET scanners have a large memory footprint. Currently, they contain ∼4 · 109data bins which amount to ∼17 GB in 32 bit floating point precision. Moreover, their size will continue to increase with advances in the achievable detector TOF resolution and increases in the axial field of view. Using iterative algorithms to reconstruct such enormous TOF sinograms becomes increasingly challenging due to the memory requirements and the computation time needed to evaluate the forward model for every data bin. This is especially true for more advanced optimization algorithms such as the stochastic primal-dual hybrid gradient (SPDHG) algorithm which allows for the use of non-smooth priors for regularization using subsets with guaranteed convergence. SPDHG requires the storage of additional sinograms in memory, which severely limits its application to data sets from state-of-the-art TOF PET systems using conventional computing hardware.Approach.Motivated by the generally sparse nature of the TOF sinograms, we propose and analyze a new listmode (LM) extension of the SPDHG algorithm for image reconstruction of sparse data following a Poisson distribution. The new algorithm is evaluated based on realistic 2D and 3D simulationsn, and a real data set acquired on a state-of-the-art TOF PET/CT system. The performance of the newly proposed LM SPDHG algorithm is compared against the conventional sinogram SPDHG and the listmode EM-TV algorithm.Main results.We show that the speed of convergence of the proposed LM-SPDHG is equivalent the original SPDHG operating on binned data (TOF sinograms). However, we find that for a TOF PET system with 400 ps TOF resolution and 25 cm axial FOV, the proposed LM-SPDHG reduces the required memory from approximately 56 to 0.7 GB for a short dynamic frame with 107prompt coincidences and to 12.4 GB for a long static acquisition with 5·108prompt coincidences.Significance.In contrast to SPDHG, the reduced memory requirements of LM-SPDHG enables a pure GPU implementation on state-of-the-art GPUs-avoiding memory transfers between host and GPU-which will substantially accelerate reconstruction times. This in turn will allow the application of LM-SPDHG in routine clinical practice where short reconstruction times are crucial.

Can nuclear imaging accurately detect scar in ischemic cardiac resynchronization therapy candidates?

BACKGROUND: Accurate scar assessment is crucial in cardiac resynchronization therapy (CRT) candidates, since its presence is a negative predictor for CRT response. Therefore, we assessed the performance of different PET parameters to detect scar in CRT candidates. METHODS: Twenty-nine CRT candidates underwent 18F-fluorodeoxyglucose (18F-FDG)-PET/computed tomography (CT), resting 13N-NH3-PET/CT and cardiac magnetic resonance (CMR) prior to CRT implantation. Segmental 18F-FDG uptake, late 13N-NH3 uptake and absolute myocardial blood flow (MBF) were evaluated for scar detection using late gadolinium enhancement (LGE) CMR as reference. A receiver operator characteristic (ROC) area under the curve (AUC) ≥0.8 indicated a good accuracy of the methods evaluated. RESULTS: Scar was present in 111 of 464 segments. None of the approaches could reliably identify segments with nontransmural scar, except for 18F-FDG uptake in the lateral wall (AUC 0.83). Segmental transmural scars could be detected with all methods (AUC ≥ 0.8), except for septal 18F-FDG uptake and MBF in the inferior wall (AUC < 0.8). Late 13N-NH3 uptake was the best parameter for transmural scar detection, independent of its location, with a sensitivity of 80% and specificity of 92% using a cutoff of 66% of the maximum tracer activity. CONCLUSIONS: Late 13N-NH3 uptake is superior to 13N-NH3 MBF and 18F-FDG in detecting transmural scar, independently of its location. However, none of the tested PET parameters was able to accurately detect nontransmural scar.

Regional glucose metabolic decreases with ageing are associated with microstructural white matter changes: a simultaneous PET/MR study.

PURPOSE: Human ageing is associated with a regional reduction in cerebral neuronal activity as assessed by numerous studies on brain glucose metabolism and perfusion, grey matter (GM) density and white matter (WM) integrity. As glucose metabolism may impact energetics to maintain myelin integrity, but changes in functional connectivity may also alter regional metabolism, we conducted a cross-sectional simultaneous FDG PET/MR study in a large cohort of healthy volunteers with a wide age range, to directly assess the underlying associations between reduced glucose metabolism, GM atrophy and decreased WM integrity in a single ageing cohort. METHODS: In 94 healthy subjects between 19.9 and 82.5 years (mean 50.1 ± 17.1; 47 M/47F, MMSE ≥ 28), simultaneous FDG-PET, structural MR and diffusion tensor imaging (DTI) were performed. Voxel-wise associations between age and grey matter (GM) density, RBV partial-volume corrected (PVC) glucose metabolism, white matter (WM) fractional anisotropy (FA) and mean diffusivity (MD), and age were assessed. Clusters representing changes in glucose metabolism correlating significantly with ageing were used as seed regions for tractography. Both linear and quadratic ageing models were investigated. RESULTS: An expected age-related reduction in GM density was observed bilaterally in the frontal, lateral and medial temporal cortex, striatum and cerebellum. After PVC, relative FDG uptake was negatively correlated with age in the inferior and midfrontal, cingulate and parietal cortex and subcortical regions, bilaterally. FA decreased with age throughout the entire brain WM. Four white matter tracts were identified connecting brain regions with declining glucose metabolism with age. Within these, relative FDG uptake in both origin and target clusters correlated positively with FA (0.32 ≤ r ≤ 0.71) and negatively with MD (- 0.75 ≤ r ≤ - 0.41). CONCLUSION: After appropriate PVC, we demonstrated that regional cerebral glucose metabolic declines with age and that these changes are related to microstructural changes in the interconnecting WM tracts. The temporal course and potential causality between ageing effects on glucose metabolism and WM integrity should be further investigated in longitudinal cohort PET/MR studies.

Use of Micro-Computed Tomography to Visualize and Quantify COVID-19 Vaccine Efficiency in Free-Breathing Hamsters.

The SARS-CoV-2 pandemic has impacted the health of humanity after the outbreak in Hubei, China in late December 2019. Ever since, it has taken unprecedented proportions and rapidity causing over a million fatal cases. Recently, a robust Syrian golden hamster model recapitulating COVID-19 was developed in search for effective therapeutics and vaccine candidates. However, overt clinical disease symptoms were largely absent despite high levels of virus replication and associated pathology in the respiratory tract. Therefore, we used micro-computed tomography (µCT) to longitudinally visualize lung pathology and to preclinically assess candidate vaccines. µCT proved to be crucial to quantify and noninvasively monitor disease progression, to evaluate candidate vaccine efficacy, and to improve screening efforts by allowing longitudinal data without harming live animals. Here, we give a comprehensive guide on how to use low-dose high-resolution µCT to follow-up SARS-CoV-2-induced disease and test the efficacy of COVID-19 vaccine candidates in hamsters. Our approach can likewise be applied for the preclinical assessment of antiviral and anti-inflammatory drug treatments in vivo.

2D feasibility study of joint reconstruction of attenuation and activity in limited angle TOF-PET.

Several research groups are studying organ-dedicated limited angle positron emission tomography (PET) systems to optimize performance-cost ratio, sensitivity, access to the patient and/or flexibility. Often open systems are considered, typically consisting of two detector panels of various sizes. Such systems provide incomplete sampling due to limited angular coverage and/or truncation, which leads to artefacts in the reconstructed activity images. In addition, these organ-dedicated PET systems are usually stand-alone systems, and as a result, no attenuation information can be obtained from anatomical images acquired in the same imaging session. It has been shown that the use of time-of-flight information reduces incomplete data artefacts and enables the joint estimation of the activity and the attenuation factors. In this work, we explore with simple 2D simulations the performance and stability of a joint reconstruction algorithm, for imaging with a limited angle PET system. The reconstruction is based on the so-called MLACF (Maximum Likelihood Attenuation Correction Factors) algorithm and uses linear attenuation coefficients in a known-tissue-class region to obtain absolute quantification. Different panel sizes and different time-of-flight (TOF) resolutions are considered. The noise propagation is compared to that of MLEM reconstruction with exact attenuation correction (AC) for the same PET system. The results show that with good TOF resolution, images of good visual quality can be obtained. If also a good scatter correction can be implemented, quantitative PET imaging will be possible. Further research, in particular on scatter correction, is required.

Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography.

The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.

Rigid motion tracking using moments of inertia in TOF-PET brain studies.

A data-driven method is proposed for rigid motion estimation directly from time-of-flight (TOF)-positron emission tomography (PET) emission data. Rigid motion parameters (translations and rotations) are estimated from the first and second moments of the emission data masked in a spherical volume. The accuracy of the method is analyzed on 3D analytical simulations of the PET-SORTEO brain phantom, and subsequently tested on18F-FDG as well as11C-PIB brain datasets acquired on a TOF-PET/CT scanner. The estimated inertia-based motion is later compared to rigid motion parameters obtained by directly registering the short frame backprojections. We find that the method provides sub mm/degree accuracies for the estimated rigid motion parameters for counts corresponding to typical 0.5 s, 1 s, and 2 s18F-FDG brain scans, with the current TOF resolutions clinically available. The method provides robust motion estimation for different types of patient motion, most notably for a continuous patient motion case where conventional frame-based approaches which rely on little to no intra-frame motion of short time intervals could fail. The method relies on the detection of stable eigenvectors for accurate motion estimation, and a monitoring of this condition can reveal time-frames where the motion estimation is less accurate, such as in dynamic PET studies.

Lung Function and Health Status in Individuals with Severe Alpha-1-Antitrypsin Deficiency at the Age of 42.

BACKGROUND: Severe hereditary alpha-1-antitrypsin deficiency (AATD) is a known risk factor for the early development of pulmonary emphysema and COPD, especially in smokers. By the Swedish national screening programme carried out from 1972 to 1974, a cohort of individuals with severe (PiZZ) AATD was identified and has been followed up regularly. The aim of this study was to investigate health status, quality of life and lung function in this cohort at the age of 42 years compared with an age-matched control group randomly selected from the population registry. METHODS: All study participants answered a questionnaire on smoking habits, symptoms, occupation, exposure to airway irritants and quality of life using Saint George's Respiratory Questionnaire (SGRQ). They underwent complete pulmonary function tests (PFT) and forced oscillation technique (FOT) for the measurement of airway resistance and reactance. Blood samples were taken for allergies and IgG-subclasses as an indicator of increased risk of airway infections. RESULTS: The residual volume (RV), total lung capacity (TLC) and RV/TLC ratio were significantly higher in the PiZZ ever-smokers compared to the PiMM ever-smokers and PiZZ never-smokers (p < 0.05). The resistance in the upper, small and total airways was significantly lower in PiZZ subjects compared to PiMM subjects (p < 0.05). A greater proportion of PiZZ never-smokers had an FEV1/VC ratio <0.7 than PiMM never-smokers (p = 0.043). PiZZ subjects with occupational exposure to airway irritants showed a significantly lower FEV1, VC and higher RV/TLC ratio than PiMM individuals with exposure (p < 0.05). CONCLUSION: At the age of 42, ever-smoking PiZZ individuals have signs of COPD, and also PiZZ never-smokers have early, physiological signs of emphysema.

Impact of left bundle branch block on myocardial perfusion and metabolism: A positron emission tomography study.

BACKGROUND: Better understanding of pathophysiological changes, induced by left bundle branch block (LBBB), may improve patient selection for cardiac resynchronization therapy (CRT). Therefore, we assessed the effect of LBBB on regional glucose metabolism, 13N-NH3-derived absolute and semiquantitative myocardial blood flow (MBF), and their relation in non-ischemic CRT candidates. METHODS: Twenty-five consecutive non-ischemic patients with LBBB underwent 18F-FDG and resting dynamic 13N-NH3 PET/CT prior to CRT implantation. Regional 18F-FDG uptake, absolute MBF, and late 13N-NH3 uptake were analyzed and corresponding septal-to-lateral wall ratios (SLR) were calculated. Segmental analysis was performed to evaluate "reverse mismatch," "mismatch," and "match" patterns, based on late 13N-NH3/18F-FDG uptake ratios. RESULTS: A significantly lower 18F-FDG uptake was observed in the septum compared to the lateral wall (SLR 0.53 ± 0.17). A similar pattern was observed for MBF (SLR 0.68 ± 0.18), whereas late 13N-NH3 uptake showed a homogeneous distribution (SLR 0.96 ± 0.13). 13N-NH3/18F-FDG "mismatch" and "reverse mismatch" segments were predominantly present in the lateral (52%) and septal wall (61%), respectively. CONCLUSIONS: Non-ischemic CRT candidates with LBBB demonstrate lower glucose uptake and absolute MBF in the septum compared to the lateral wall. However, late static 13N-NH3 uptake showed a homogenous distribution, reflecting a composite measure of altered regional MBF and metabolism, induced by LBBB.

Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings. In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [18F]FDG and 10 [18F]PE2I data sets were used, lead to a CNN that is robust against the used PET tracer, the noise level of the input PET images and the input MRI contrast. A detailed analysis of our CNN in 36 [18F]FDG, 18 [18F]PE2I, and 7 [18F]FET test data sets demonstrates that the image quality of our trained CNN is very close to the one of the target reconstructions in terms of regional mean recovery and regional structural similarity.

Quantitative PET in the 2020s: a roadmap.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

STAT2 signaling restricts viral dissemination but drives severe pneumonia in SARS-CoV-2 infected hamsters.

Emergence of SARS-CoV-2 causing COVID-19 has resulted in hundreds of thousands of deaths. In search for key targets of effective therapeutics, robust animal models mimicking COVID-19 in humans are urgently needed. Here, we show that Syrian hamsters, in contrast to mice, are highly permissive to SARS-CoV-2 and develop bronchopneumonia and strong inflammatory responses in the lungs with neutrophil infiltration and edema, further confirmed as consolidations visualized by micro-CT alike in clinical practice. Moreover, we identify an exuberant innate immune response as key player in pathogenesis, in which STAT2 signaling plays a dual role, driving severe lung injury on the one hand, yet restricting systemic virus dissemination on the other. Our results reveal the importance of STAT2-dependent interferon responses in the pathogenesis and virus control during SARS-CoV-2 infection and may help rationalizing new strategies for the treatment of COVID-19 patients.

Use of Multimodal Imaging and Clinical Biomarkers in Presymptomatic Carriers of C9orf72 Repeat Expansion.

Importance: During a time with the potential for novel treatment strategies, early detection of disease manifestations at an individual level in presymptomatic carriers of a hexanucleotide repeat expansion in the C9orf72 gene (preSxC9) is becoming increasingly relevant. Objectives: To evaluate changes in glucose metabolism before symptom onset of amyotrophic lateral sclerosis or frontotemporal dementia in preSxC9 using simultaneous fluorine 18-labeled fluorodeoxyglucose ([18F]FDG positron emission tomographic (PET) and magnetic resonance imaging as well as the mutation's association with clinical and fluid biomarkers. Design, Setting, and Participants: A prospective, case-control study enrolled 46 participants from November 30, 2015, until December 11, 2018. The study was conducted at the neuromuscular reference center of the University Hospitals Leuven, Leuven, Belgium. Main Outcomes and Measures: Neuroimaging data were spatially normalized and analyzed at the voxel level at a height threshold of P < .001, cluster-level familywise error-corrected threshold of P < .05, and statistical significance was set at P < .05 for the volume-of-interest level analysis, using Benjamini-Hochberg correction for multiple correction. W-score maps were computed using the individuals serving as controls as a reference to quantify the degree of [18F]FDG PET abnormality. The threshold for abnormality on the W-score maps was designated as an absolute W-score greater than or equal to 1.96. Neurofilament levels and performance on cognitive and neurologic examinations were determined. All hypothesis tests were 1-sided. Results: Of the 42 included participants, there were 17 with the preSxC9 mutation (12 women [71%]; mean [SD] age, 51 [9] years) and 25 healthy controls (12 women [48%]; mean [SD] age, 47 [10] years). Compared with control participants, significant clusters of relative hypometabolism were found in frontotemporal regions, basal ganglia, and thalami of preSxC9 participants and relative hypermetabolism in the peri-Rolandic region, superior frontal gyrus, and precuneus cortex. W-score frequency maps revealed reduced glucose metabolism with local maxima in the insular cortices, central opercular cortex, and thalami in up to 82% of preSxC9 participants and increased glucose metabolism in the precentral gyrus and precuneus cortex in up to 71% of preSxC9 participants. Other findings in the preSxC9 group were upper motor neuron involvement in 10 participants (59%), cognitive abnormalities in 5 participants (29%), and elevated neurofilament levels in 3 of 16 individuals (19%) who underwent lumbar puncture. Conclusions and Relevance: The results suggest that [18F]FDG PET can identify glucose metabolic changes in preSxC9 at an individual level, preceding significantly elevated neurofilament levels and onset of symptoms.

Long-term Ashtanga yoga practice decreases medial temporal and brainstem glucose metabolism in relation to years of experience.

BACKGROUND: Yoga is increasingly popular worldwide with several physical and mental benefits, but the underlying neurobiology remains unclear. Whereas many studies have focused on pure meditational aspects, the triad of yoga includes meditation, postures, and breathing. We conducted a cross-sectional study comparing experienced yoga practitioners to yoga-naive healthy subjects using a multiparametric 2 × 2 design with simultaneous positron emission tomography/magnetic resonance (PET/MR) imaging. METHODS: 18F-FDG PET, morphometric and diffusion tensor imaging, resting state fMRI, and MR spectroscopy were acquired in 10 experienced (4.8 ± 2.3 years of regular yoga experience) yoga practitioners and 15 matched controls in rest and after a single practice (yoga practice and physical exercise, respectively). RESULTS: In rest, decreased regional glucose metabolism in the medial temporal cortex, striatum, and brainstem was observed in yoga practitioners compared to controls (p < 0.0001), with a significant inverse correlation of resting parahippocampal and brainstem metabolism with years of regular yoga practice (ρ < - 0.63, p < 0.05). A single yoga practice resulted in significant hypermetabolism in the cerebellum (p < 0.0001). None of the MR measures differed, both at rest and after intervention. CONCLUSIONS: Experienced yoga practitioners show regional long-term decreases in glucose metabolism related to years of practice. To elucidate a potential causality, a prospective longitudinal study in yoga-naive individuals is warranted.

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

PURPOSE: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation. METHODS: A multi-scale CNN was modified for liver segmentation for SIRT patients. The CNN model was trained with 139 datasets from three liver segmentation challenges and 12 SIRT patient datasets from our hospital. Validation was performed on 13 SIRT datasets and 12 challenge datasets. The model was tested on 40 SIRT datasets. One expert manually delineated the livers and adjusted the liver segmentations from CNN for 40 test SIRT datasets. Another expert performed the same tasks for 20 datasets randomly selected from the 40 SIRT datasets. The CNN segmentations were compared with the manual and adjusted segmentations from the experts. The difference between the manual segmentations was compared with the difference between the adjusted segmentations to investigate the inter-observer variability. Segmentation difference was evaluated through dice similarity coefficient (DSC), volume ratio (RV), mean surface distance (MSD), and Hausdorff distance (HD). RESULTS: The CNN segmentation achieved a median DSC of 0.94 with the manual segmentation and of 0.98 with the manually corrected CNN segmentation, respectively. The DSC between the adjusted segmentations is 0.98, which is 0.04 higher than the DSC between the manual segmentations. CONCLUSION: The CNN model achieved good liver segmentations on CT images of good image quality, with relatively normal liver shapes and low tumor burden. 87.5% of the 40 CNN segmentations only needed slight adjustments for clinical use. However, the trained model failed on SIRT data with low dose or contrast, lesions with large density difference from their surroundings, and abnormal liver position and shape. The abovementioned scenarios were not adequately represented in the training data. Despite this limitation, the current CNN is already a useful clinical tool which improves inter-observer agreement and therefore contributes to the standardization of the dosimetry. A further improvement is expected when the CNN will be trained with more data from SIRT patients.

Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics.

PURPOSE: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [18F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [18F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting. METHODS: Patients with a suspected diagnosis of ALS (n = 98; 64.8 ± 11 years; 61 M) underwent brain and spine [18F]-FDG PET-CT scans. In 62 patients, ALS diagnosis was confirmed (67.8 ± 10 years; 35 M) after longitudinal follow-up (average 18.1 ± 8.4 months). In 23 patients, another disease was diagnosed (ALS mimics, 60.9 ± 12.9 years; 17 M) and 13 had a variant motor neuron disease, primary lateral sclerosis (PLS; n = 4; 53.6 ± 2.5 years; 2 M) and progressive muscular atrophy (PMA; n = 9; 58.4 ± 7.3 years; 7 M). Spine metabolism was determined after manual and automated segmentation. VOI- and voxel-based comparisons were performed. Moreover, a support vector machine (SVM) approach was applied to investigate the discriminative power of regional brain metabolism, spine metabolism and the combination of both. RESULTS: Brain metabolism was very similar between ALS mimics and ALS, whereas cervical and thoracic spine metabolism was significantly different (in standardised uptake values; cervical: ALS 2.1 ± 0.5, ALS mimics 1.9 ± 0.4; thoracic: ALS 1.8 ± 0.3, ALS mimics 1.5 ± 0.3). As both brain and spine metabolisms were very similar between ALS mimics and PLS/PMA, groups were pooled for accuracy analyses. Mean discrimination accuracy was 65.4%, 80.0% and 81.5%, using only brain metabolism, using spine metabolism and using both, respectively. CONCLUSION: The combination of brain and spine FDG PET-CT with SVM classification is useful as discriminative biomarker between ALS and ALS mimics in a real-life clinical setting.