High-Spatial-Resolution Benchtop X-ray Fluorescence Imaging through Bragg-Diffraction-Based Focusing with Bent Mosaic Graphite Crystals: A Simulation Study.

X-ray fluorescence imaging (XFI) can localize diagnostic or theranostic entities utilizing nanoparticle (NP)-based probes at high resolution in vivo, in vitro, and ex vivo. However, small-animal benchtop XFI systems demonstrating high spatial resolution (variable from sub-millimeter to millimeter range) in vivo are still limited to lighter elements (i.e., atomic number Z≤45). This study investigates the feasibility of focusing hard X-rays from solid-target tubes using ellipsoidal lens systems composed of mosaic graphite crystals with the aim of enabling high-resolution in vivo XFI applications with mid-Z (42≤Z≤64) elements. Monte Carlo simulations are performed to characterize the proposed focusing-optics concept and provide quantitative predictions of the XFI sensitivity, in silico tumor-bearing mice models loaded with palladium (Pd) and barium (Ba) NPs. Based on simulation results, the minimum detectable total mass of PdNPs per scan position is expected to be on the order of a few hundred nanograms under in vivo conform conditions. PdNP masses as low as 150 ng to 50 ng could be detectable with a resolution of 600 µm when imaging abdominal tumor lesions across a range of low-dose (0.8 µGy) to high-dose (8 µGy) exposure scenarios. The proposed focusing-optics concept presents a potential step toward realizing XFI with conventional X-ray tubes for high-resolution applications involving interesting NP formulations.

Gut-brain axis volatile organic compounds derived from breath distinguish between schizophrenia and major depressive disorder.

BACKGROUND: Signatures from the metabolome and microbiome have already been introduced as candidates for diagnostic and treatment support. The aim of this study was to investigate the utility of volatile organic compounds (VOCs) from the breath for detection of schizophrenia and depression. METHODS: Patients with a diagnosis of major depressive disorder (MDD) or schizophrenia, as well as healthy controls, were recruited to participate. After being clinically assessed and receiving instruction, each participant independently collected breath samples for subsequent examination by proton transfer-reaction mass spectrometry. RESULTS: The sample consisted of 104 participants: 36 patients with MDD, 34 patients with schizophrenia and 34 healthy controls. Through mixed-model and deep learning analyses, 5 VOCs contained in the participants' breath samples were detected that significantly differentiated between diagnostic groups and healthy controls, namely VOCs with mass-to-charge ratios (m/z) 60, 69, 74, 88 and 90, which had classification accuracy of 76.8% to distinguish participants with MDD from healthy controls, 83.6% to distinguish participants with schizophrenia from healthy controls and 80.9% to distinguish participants with MDD from those with schizophrenia. No significant associations with medication, illness duration, age of onset or time in hospital were detected for these VOCs. LIMITATIONS: The sample size did not allow generalization, and confounders such as nutrition and medication need to be tested. CONCLUSION: This study established promising results for the use of human breath gas for detection of schizophrenia and MDD. Two VOCs, 1 with m/z 60 (identified as trimethylamine) and 1 with m/z 90 (identified as butyric acid) could then be further connected to the interworking of the microbiota-gut-brain axis.

Modelling DTPA therapy following Am contamination in rats.

A major challenge in modelling the decorporation of actinides (An), such as americium (Am), with DTPA (diethylenetriaminepentaacetic acid) is the fact that standard biokinetic models become inadequate for assessing radionuclide intake and estimating the resulting dose, as DTPA perturbs the regular biokinetics of the radionuclide. At present, most attempts existing in the literature are empirical and developed mainly for the interpretation of one or a limited number of specific incorporation cases. Recently, several approaches have been presented with the aim of developing a generic model, one of which reported the unperturbed biokinetics of plutonium (Pu), the chelation process and the behaviour of the chelated compound An-DTPA with a single model structure. The aim of the approach described in this present work is the development of a generic model that is able to describe the biokinetics of Am, DTPA and the chelate Am-DTPA simultaneously. Since accidental intakes in humans present many unknowns and large uncertainties, data from controlled studies in animals were used. In these studies, different amounts of DTPA were administered at different times after contamination with known quantities of Am. To account for the enhancement of faecal excretion and reduction in liver retention, DTPA is assumed to chelate Am not only in extracellular fluids, but also in hepatocytes. A good agreement was found between the predictions of the proposed model and the experimental results for urinary and faecal excretion and accumulation and retention in the liver. However, the decorporation from the skeletal compartment could not be reproduced satisfactorily under these simple assumptions.

Dynamic Stability of Volatile Organic Compounds in Respiratory Air in Schizophrenic Patients and Its Potential Predicting Efficacy of TAAR Agonists.

OBJECTIVES: Volatile organic compounds (VOCs) in the breathing air were found to be altered in schizophrenia patients compared to healthy participants. The aim of this study was to confirm these findings and to examine for the first time whether these VOCs are stable or change in concentration during the early treatment course. Moreover, it was investigated whether there is a correlation of the VOCs with the existing psychopathology of schizophrenia patients, i.e., whether the concentration of masses detected in the breath gas changes when the psychopathology of the participants changes. METHODS: The breath of a total of 22 patients with schizophrenia disorder was examined regarding the concentration of VOCs using proton transfer reaction mass spectrometry. The measurements were carried out at baseline and after two weeks at three different time points, the first time immediately after waking up in the morning, after 30 min, and then after 60 min. Furthermore, 22 healthy participants were investigated once as a control group. RESULTS: Using bootstrap mixed model analyses, significant concentration differences were found between schizophrenia patients and healthy control participants (m/z 19, 33, 42, 59, 60, 69, 74, 89, and 93). Moreover, concentration differences were detected between the sexes for masses m/z 42, 45, 57, 69, and 91. Mass m/z 67 and 95 showed significant temporal changes with decreasing concentration during awakening. Significant temporal change over two weeks of treatment could not be detected for any mass. Masses m/z 61, 71, 73, and 79 showed a significant relationship to the respective olanzapine equivalents. The length of hospital stay showed no significant relationship to the masses studied. CONCLUSION: Breath gas analysis is an easy-to-use method to detect differences in VOCs in the breath of schizophrenia patients with high temporal stability. m/z 60 corresponding to trimethylamine might be of potential interest because of its natural affinity to TAAR receptors, currently a novel therapeutic target under investigation. Overall, breath signatures seemed to stable over time in patients with schizophrenia. In the future, the development of a biomarker could potentially have an impact on the early detection of the disease, treatment, and, thus, patient outcome.

Exploring the translational challenge for medical applications of ionising radiation and corresponding radiation protection research.

BACKGROUND: Medical applications of ionising radiation and associated radiation protection research often encounter long delays and inconsistent implementation when translated into clinical practice. A coordinated effort is needed to analyse the research needs for innovation transfer in radiation-based high-quality healthcare across Europe which can inform the development of an innovation transfer framework tailored for equitable implementation of radiation research at scale. METHODS: Between March and September 2021 a Delphi methodology was employed to gain consensus on key translational challenges from a range of professional stakeholders. A total of three Delphi rounds were conducted using a series of electronic surveys comprised of open-ended and closed-type questions. The surveys were disseminated via the EURAMED Rocc-n-Roll consortium network and prominent medical societies in the field. Approximately 350 professionals were invited to participate. Participants' level of agreement with each generated statement was captured using a 6-point Likert scale. Consensus was defined as median ≥ 4 with ≥ 60% of responses in the upper tertile of the scale. Additionally, the stability of responses across rounds was assessed. RESULTS: In the first Delphi round a multidisciplinary panel of 20 generated 127 unique statements. The second and third Delphi rounds recruited a broader sample of 130 individuals to rate the extent to which they agreed with each statement as a key translational challenge. A total of 60 consensus statements resulted from the iterative Delphi process of which 55 demonstrated good stability. Ten statements were identified as high priority challenges with ≥ 80% of statement ratings either 'Agree' or 'Strongly Agree'. CONCLUSION: A lack of interoperability between systems, insufficient resources, unsatisfactory education and training, and the need for greater public awareness surrounding the benefits, risks, and applications of ionising radiation were identified as principal translational challenges. These findings will help to inform a tailored innovation transfer framework for medical radiation research.

Notifications and alerts in patient dose values for computed tomography and fluoroscopy-guided interventional procedures.

The terms "notifications" and "alerts" for medical exposures are used by several national and international organisations. Recommendations for CT scanners have been published by the American Association of Physicists in Medicine. Some interventional radiology societies as well as national authorities have also published dose notifications for fluoroscopy-guided interventional procedures. Notifications and alerts may also be useful for optimisation and to avoid unintended and accidental exposures. The main interest in using these values for high-dose procedures (CT and interventional) is to optimise imaging procedures, reducing the probability of stochastic effects and avoiding tissue reactions. Alerts in X-ray systems may be considered before procedures (as in CT), during procedures (in some interventional radiology systems), and after procedures, when the patient radiation dose results are known and processed. This review summarises the different uses of notifications and alerts to help in optimisation for CT and for fluoroscopy-guided interventional procedures as well as in the analysis of unintended and accidental medical exposures. The paper also includes cautions in setting the alert values and discusses the benefits of using patient dose management systems for the alerts, their registry and follow-up, and the differences between notifications, alerts, and trigger levels for individual procedures and the terms used for the collective approach, such as diagnostic reference levels. KEY POINTS: • Notifications and alerts on patient dose values for computed tomography (CT) and fluoroscopy-guided interventional procedures (FGIP) allow to improve radiation safety and contribute to the avoidance of radiation injuries and unintended and accidental exposures. • Alerts may be established before the imaging procedures (as in CT) or during and after the procedures as for FGIP. • Dose management systems should include notifications and alerts and their registry for the hospital quality programmes.

Assessment of radiation protection and awareness level among radiation workers and members of the public in Afghanistan-a pilot study.

In this study, the level of radiation protection and awareness among radiation workers and members of the public in Afghanistan is assessed using two different survey mechanisms. The onsite survey conducted by the regulatory authorities covered 472 facilities while the online survey covering 1200 responses was conducted via the Survey Monkey online survey platform. Both the onsite and the online surveys show that more than 70% of radiation workers have insufficient knowledge of radiation safety and protection rules and regulations while more than 70% of the public just heard about radiation from one source or another. More than 50% of those members of the public, who have been through some sort of medical imaging using radiation were not given any instruction about the radiation hazards. The study is concluded with recommendations to the authorities for raising the knowledge of the radiation workers and general public awareness through launching regular radiation safety and protection training, TV spots and other means of mass media.

Lifetime study in mice after acute low-dose ionizing radiation: a multifactorial study with special focus on cataract risk.

Because of the increasing application of ionizing radiation in medicine, quantitative data on effects of low-dose radiation are needed to optimize radiation protection, particularly with respect to cataract development. Using mice as mammalian animal model, we applied a single dose of 0, 0.063, 0.125 and 0.5 Gy at 10 weeks of age, determined lens opacities for up to 2 years and compared it with overall survival, cytogenetic alterations and cancer development. The highest dose was significantly associated with increased body weight and reduced survival rate. Chromosomal aberrations in bone marrow cells showed a dose-dependent increase 12 months after irradiation. Pathological screening indicated a dose-dependent risk for several types of tumors. Scheimpflug imaging of the lens revealed a significant dose-dependent effect of 1% of lens opacity. Comparison of different biological end points demonstrated long-term effects of low-dose irradiation for several biological end points.

Online breath gas analysis in unrestrained mice by hs-PTR-MS.

The phenotyping of genetic mouse models for human disorders may greatly benefit from breath gas analysis as a noninvasive tool to identify metabolic alterations in mice. Phenotyping screens such as the German Mouse Clinic demand investigations in unrestrained mice. Therefore, we adapted a breath screen in which exhaled volatile organic compounds (VOCs) were online monitored by proton transfer reaction mass spectrometry (hs-PTR-MS). The source strength of VOCs was derived from the dynamics in the accumulation profile of exhaled VOCs of a single mouse in a respirometry chamber. A careful survey of the accumulation revealed alterations in the source strength due to confounders, e.g., urine and feces. Moreover changes in the source strength of humidity were triggered by changes in locomotor behavior as mice showed a typical behavioral pattern from activity to settling down in the course of subsequent accumulation profiles. We demonstrated that metabolic changes caused by a dietary intervention, e.g., after feeding a high-fat diet (HFD) a sample of 14 male mice, still resulted in a statistically significant shift in the source strength of exhaled VOCs. Applying a normalization which was derived from the distribution of the source strength of humidity and accounted for varying locomotor behaviors improved the shift. Hence, breath gas analysis may provide a noninvasive, fast access to monitor the metabolic adaptation of a mouse to alterations in energy balance due to overfeeding or fasting and dietary macronutrient composition as well as a high potential for systemic phenotyping of mouse mutants, intervention studies, and drug testing in mice.

3D-visualization of segmented contacts of directional deep brain stimulation electrodes via registration and fusion of CT and FDCT.

OBJECTIVES: 3D-visualization of the segmented contacts of directional deep brain stimulation (DBS) electrodes is desirable since knowledge about the position of every segmented contact could shorten the timespan for electrode programming. CT cannot yield images fitting that purpose whereas highly resolved flat detector computed tomography (FDCT) can accurately image the inner structure of the electrode. This study aims to demonstrate the applicability of image fusion of highly resolved FDCT and CT to produce highly resolved images that preserve anatomical context for subsequent fusion to preoperative MRI for eventually displaying segmented contactswithin anatomical context in future studies. MATERIAL AND METHODS: Retrospectively collected datasets from 15 patients who underwent bilateral directional DBS electrode implantation were used. Subsequently, after image analysis, a semi-automated 3D-registration of CT and highly resolved FDCT followed by image fusion was performed. The registration accuracy was assessed by computing the target registration error. RESULTS: Our work demonstrated the feasibility of highly resolved FDCT to visualize segmented electrode contacts in 3D. Semiautomatic image registration to CT was successfully implemented in all cases. Qualitative evaluation by two experts revealed good alignment regarding intracranial osseous structures. Additionally, the average for the mean of the target registration error over all patients, based on the assessments of two raters, was computed to be 4.16 mm. CONCLUSION: Our work demonstrated the applicability of image fusion of highly resolved FDCT to CT for a potential workflow regarding subsequent fusion to MRI in the future to put the electrodes in an anatomical context.

Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence Computed Tomography through Deep-Learning-Based Denoising.

X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing high-resolution molecular-level data. However, increased sensitivity with current benchtop X-ray sources comes at the cost of high radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for background noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized Swin-Conv-UNet (SCUNet) model for background noise reduction in X-ray fluorescence (XRF) images at low tracer concentrations. Our method's effectiveness is evaluated against higher-dose images, while various denoising techniques exist for X-ray and computed tomography (CT) techniques, only a few address XFCT. The DL model is trained and assessed using augmented data, focusing on background noise reduction. Image quality is measured using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), comparing outcomes with 100% X-ray-dose images. Results demonstrate that the proposed algorithm yields high-quality images from low-dose inputs, with maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms block-matching and 3D filtering (BM3D), block-matching and 4D filtering (BM4D), non-local means (NLM), denoising convolutional neural network (DnCNN), and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.

[Opportunities for artificial intelligence in radiation protection : Improving safety of diagnostic imaging]. / Möglichkeiten der künstlichen Intelligenz im Strahlenschutz : Verbesserung der Sicherheit bei medizinischen Bildgebungsuntersuchungen.

CLINICAL/METHODOLOGICAL ISSUE: Imaging of structures of internal organs often requires ionizing radiation, which is a health risk. Reducing the radiation dose can increase the image noise, which means that images provide less information. STANDARD RADIOLOGICAL METHODS: This problem is observed in commonly used medical imaging modalities such as computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), angiography, fluoroscopy, and any modality that uses ionizing radiation for imaging. METHODOLOGICAL INNOVATIONS: Artificial intelligence (AI) can improve the quality of low-dose images and help minimize radiation exposure. Potential applications are explored, and frameworks and procedures are critically evaluated. PERFORMANCE: The performance of AI models varies. High-performance models could be used in clinical settings in the near future. Several challenges (e.g., quantitative accuracy, insufficient training data) must be addressed for optimal performance and widespread adoption of this technology in the field of medical imaging. PRACTICAL RECOMMENDATIONS: To fully realize the potential of AI and deep learning (DL) in medical imaging, research and development must be intensified. In particular, quality control of AI models must be ensured, and training and testing data must be uncorrelated and quality assured. With sufficient scientific validation and rigorous quality management, AI could contribute to the safe use of low-dose techniques in medical imaging.

Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation.

Background: Central blood pressure (cBP) is a better indicator of cardiovascular morbidity and mortality than peripheral BP (pBP). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP. Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP. The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model. Methods: The intra-arterial femoral BPfe and intra-aortic pressure BPao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BPfe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole cBP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure (PP) was evaluated by the regression and Bland-Altman analyses. Results: The SCICA tool estimated the cBP waveform non-invasively from the intra-arterial BPfe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland-Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of -0.54 ± 2.42 mmHg [95% confidence interval (CI): -5.28 to 4.20] for SBP, -1.97 ± 1.62 mmHg (95% CI: -5.14 to 1.20) for DBP, -1.49 ± 1.40 mmHg (95% CI: -4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: -4.03 to 6.90) for PP. Conclusions: The SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP.

Multi-class Tissue Segmentation of CT images using an Ensemble Deep Learning method.

Microwave ablation (MWA) therapy is a well-known technique for locally destroying lung tumors with the help of computed tomography (CT) images. However, tumor recurrence occurs because of insufficient ablation of the tumor. In order to perform an accurate treatment of lung cancer, there is a demand to determine the tumor area precisely. To address the problem at hand, which involves accurately segmenting organs and tumors in CT images obtained during MWA therapy, physicians could benefit from a semantic segmentation method. However, such a method typically requires a large number of images to achieve optimal results through deep learning techniques. To overcome this challenge, our team developed four different (multiple) U-Net based semantic segmentation models that work in conjunction with one another to produce a more precise segmented image, even when working with a relatively small dataset. By combining the highest weight value of segmentation from multiple methods into a single output, we can achieve a more reliable and accurate segmentation outcome. Our approach proved successful in segmenting four different tissue structures, including lungs, lung tumors, and ablated tissues in CT medical images. The Intersection over Union (IoU) is employed to quantitatively evaluate the proposed method. The method shows the highest average IoU, with 0.99 for the background, 0.98 for the lung, 0.77 for the ablated, and 0.54 for the tumor tissue. The results show that employing multiple DL methods is superior to that of individual base-learner models for all four different tissue structures, even in the presence of the relatively small dataset.Clinical relevance- An essential issue of tumor ablation therapy is to know when the entire tumor tissue has completely been destroyed. However, as it is difficult to distinguish between destroyed and living tumor, this is hardly reliable in clinical practice during MWA therapy, especially when working with a small dataset. Improved AI segmentation methods can help to improve performance to reduce recurrence.

Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.

Purpose: Earlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients. Methods: 317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics. Results: A correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration. Conclusion: The machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.

Education and training in radiation protection in Europe: results from the EURAMED Rocc-n-Roll project survey.

PURPOSE: To analyse the existing radiation protection (RP) education and training (E&T) capabilities in the European Union and identify associated needs, problems and challenges. METHOD: An online survey was disseminated via the EURAMED Rocc-n-Roll consortium network and prominent medical societies in the field of radiological research. The survey sections analyse the RP E&T during undergraduate, residency/internship and continuous professional development; RP E&T problems and legal implementation. Differences were analysed by European geographic regions, profession, years of professional experience and main area of practice/research. RESULTS: The majority of the 550 respondents indicated that RP topics are part of undergraduate curricula in all courses for their profession and country (55%); however, hands-on practical training is not included according to 30% of the respondents. The lack of E&T, practical aspects in current E&T, and mandatory continuing E&T were considered the major problems. The legal requirement that obtained higher implementation score was the inclusion of the practical aspects of medical radiological procedures on education (86%), and lower score was obtained for the inclusion of RP E&T on medical and dental school curriculums (61%). CONCLUSIONS: A heterogeneity in RP E&T during undergraduate, residency/internship and continuous professional development is evident across Europe. Differences were noted per area of practice/research, profession, and European geographic region. A large variation in RP E&T problem rating was also obtained.

Signal detection and location-dependent noise in cone-beam computed tomography using the spatial definition of the Hotelling SNR.

PURPOSE: Quality assurance in computed tomography (CT) is commonly performed with the Fourier-based modulation transfer function (MTF) and the noise variance, while more recently the noise power spectrum (NPS) has increased in popularity. The Fourier-based methods make assumptions such as shift-invariance and cyclostationarity. These assumptions are violated in real clinical systems and consequently are expected to result in systematic errors. A spatial approach, based on the object transfer matrix (T) and the covariance matrix (K) theory, does not require these assumptions and can provide a more general description of the imaging system. In this paper, the authors present an experimental methodology and data treatment for quality assessment of a lab cone-beam CT system by comparing the spatial with the Fourier approach in 2D reconstructed slices. METHODS: In order to have control over all experimental parameters and image reconstruction, a bench-top flat-panel-based cone-beam CT scanner and a cylindrical water-filled poly(methyl methacrylate) (PMMA) phantom were used for the noise measurements. An aluminum foil inserted in the water phantom enabled the estimation of the line response function (LRF) with a limited number of assumptions. The authors evaluated the spatial blur, the noise and the signal-to-noise ratio (SNR) using the spatial approach as well as the Fourier-based approach. In order to evaluate the degree of noise nonstationarity of their cone-beam CT system, the authors evaluated both the local and global CT noise properties and compared them using both approaches. RESULTS: For the laboratory cone-beam CT, the location-dependent noise evaluation showed that in addition to the noise variance, the NPS and covariance eigenvector symmetry depend on the location in the image. The estimated signal transfer was similar for both approaches. Unlike the Fourier approach which uses the same exponential wave function basis for both MTF and NPS, the eigenvectors of T and K were significantly different. CONCLUSIONS: By using the eigenvectors of the noise and object transfer to characterize the system, the spatial approach provides additional information to the Fourier approach and is therefore an important tool for a thorough understanding of a CT system.

Investigating tomographic reconstruction with a priori geometrical information.

A novel strategy to perform tomographic image reconstruction is presented, based on the integration of a priori information about the target image. Such information may come from a different imaging tool or a synthetic model. For a given image quality, providing a priori image information reduces the amount of image information to be reconstructed. According to the data processing inequality this requires less input data or physical measurements, therefore reducing exposure to ionising radiation. A prototype algorithm is described, consisting of a penalized ART where some a priori edge information is encoded in an inhomogeneous, anisotropic smoothing kernel. The algorithm is tested on a 2-dimensional set-up based on the Shepp-Logan phantom.

Volatile organic compounds from exhaled breath in schizophrenia.

OBJECTIVES: This study aims to find out whether volatile organic compounds (VOCs) from exhaled breath differ significantly between patients with schizophrenia and healthy controls and whether it might be possible to create an algorithm that can predict the likelihood of suffering from schizophrenia. METHODS: To test this theory, a group of patients with clinically diagnosed acute schizophrenia as well as a healthy comparison group has been investigated, which have given breath samples during awakening response right after awakening, after 30 min and after 60 min. The VOCs were measured using Proton-Transfer-Reaction Mass Spectrometry. RESULTS: By applying bootstrap with mixed model analysis (n = 1000), we detected 10 signatures (m/z 39, 40, 59, 60, 69, 70, 74, 85, 88 and 90) showing reduced concentration in patients with schizophrenia compared to healthy controls. These could safely discriminate patients and controls and were not influenced by smoking. Logistic regression forward method achieved an area under the receiver operating characteristic curve (AUC) of 0.91 and an accuracy of 82% and a machine learning approach with bartMachine an AUC of 0.96 and an accuracy of 91%. CONCLUSION: Breath gas analysis is easy to apply, well tolerated and seems to be a promising candidate for further studies on diagnostic and predictive clinical utility.

Volatile Organic Compounds From Breath Differ Between Patients With Major Depression and Healthy Controls.

Major depressive disorder (MDD) is a widespread common disorder. Up to now, there are no easy and frequent to use non-invasive biomarkers that could guide the diagnosis and treatment of MDD. The aim of this study was to investigate whether there are different mass concentrations of volatile organic compounds (VOCs) in the exhaled breath between patients with MDD and healthy controls. For this purpose, patients with MDD according to DSM-V and healthy subjects were investigated. VOCs contained in the breath were collected immediately after awakening, after 30 min, and after 60 min in a respective breath sample and measured using PRT-MS (proton-transfer-reaction mass spectrometry). Concentrations of masses m/z 88, 89, and 90 were significantly decreased in patients with MDD compared with healthy controls. Moreover, changes during the time in mass concentrations of m/z 93 and 69 significantly differed between groups. Differentiation between groups was possible with an AUCs of 0.80-0.94 in ROC analyses. In this first study, VOCs differed between patients and controls, and therefore, might be a promising tool for future studies. Altered masses are conceivable with energy metabolism in a variety of biochemical processes and involvement of the brain-gut-lung-microbiome axis.