The COMBO window: A chronic cranial implant for multiscale circuit interrogation in mice.

Neuroscientists studying the neural correlates of mouse behavior often lack access to the brain-wide activity patterns elicited during a specific task of interest. Fortunately, large-scale imaging is becoming increasingly accessible thanks to modalities such as Ca2+ imaging and functional ultrasound (fUS). However, these and other techniques often involve challenging cranial window procedures and are difficult to combine with other neuroscience tools. We address this need with an open-source 3D-printable cranial implant-the COMBO (ChrOnic Multimodal imaging and Behavioral Observation) window. The COMBO window enables chronic imaging of large portions of the brain in head-fixed mice while preserving orofacial movements. We validate the COMBO window stability using both brain-wide fUS and multisite two-photon imaging. Moreover, we demonstrate how the COMBO window facilitates the combination of optogenetics, fUS, and electrophysiology in the same animals to study the effects of circuit perturbations at both the brain-wide and single-neuron level. Overall, the COMBO window provides a versatile solution for performing multimodal brain recordings in head-fixed mice.

VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology.

Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data are often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefore, there is an exigent need for a multimodality pipeline that enables ex vivo vascular imaging with magnetic resonance imaging, computed tomography and optical microscopy of the same sample, while permitting imaging with complementary contrast mechanisms from the whole-organ to endothelial cell spatial scales. To achieve this, we developed 'VascuViz'-an easy-to-use method for simultaneous three-dimensional imaging and visualization of the vascular microenvironment using magnetic resonance imaging, computed tomography and optical microscopy in the same intact, unsectioned tissue. The VascuViz workflow permits multimodal imaging with a single labeling step using commercial reagents and is compatible with diverse tissue types and protocols. VascuViz's interdisciplinary utility in conjunction with new data visualization approaches opens up new vistas in image-based vascular systems biology.

Multimodal imaging analysis in silver fir reveals coordination in cellulose and lignin deposition.

Despite lignin being a key component of wood, the dynamics of tracheid lignification are generally overlooked in xylogenesis studies, which hampers our understanding of environmental drivers and blurs the interpretation of isotopic and anatomical signals stored in tree rings. Here, we analyzed cell wall formation in silver fir (Abies alba Mill.) tracheids to determine if cell wall lignification lags behind secondary wall deposition. For this purpose, we applied a multimodal imaging approach combining transmitted light microscopy (TLM), confocal laser scanning microscopy (CLSM), and confocal Raman microspectroscopy (RMS) on anatomical sections of wood microcores collected in northeast France on 11 dates during the 2010 growing season. Wood autofluorescence after laser excitation at 405 and 488 nm associated with the RMS scattering of lignin and cellulose, respectively, which allowed identification of lignifying cells (cells showing lignified and nonlignified wall fractions at the same time) in CLSM images. The number of lignifying cells in CLSM images mirrored the number of wall-thickening birefringent cells in polarized TLM images, revealing highly synchronized kinetics for wall thickening and lignification (similar timings and durations at the cell level). CLSM images and RMS chemical maps revealed a substantial incorporation of lignin into the wall at early stages of secondary wall deposition. Our results show that most of the cellulose and lignin contained in the cell wall undergo concurrent periods of deposition. This suggests a strong synchronization between cellulose and lignin-related features in conifer tree-ring records, as they originated over highly overlapped time frames.

Multimodality Cardiac Imaging in COVID.

Infection with SARS-CoV-2, the virus that causes COVID, is associated with numerous potential secondary complications. Global efforts have been dedicated to understanding the myriad potential cardiovascular sequelae which may occur during acute infection, convalescence, or recovery. Because patients often present with nonspecific symptoms and laboratory findings, cardiac imaging has emerged as an important tool for the discrimination of pulmonary and cardiovascular complications of this disease. The clinician investigating a potential COVID-related complication must account not only for the relative utility of various cardiac imaging modalities but also for the risk of infectious exposure to staff and other patients. Extraordinary clinical and scholarly efforts have brought the international medical community closer to a consensus on the appropriate indications for diagnostic cardiac imaging during this protracted pandemic. In this review, we summarize the existing literature and reference major societal guidelines to provide an overview of the indications and utility of echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance imaging for the diagnosis of cardiovascular complications of COVID.

Do we empathize humanoid robots and humans in the same way? Behavioral and multimodal brain imaging investigations.

Humanoid robots have been designed to look more and more like humans to meet social demands. How do people empathize humanoid robots who look the same as but are essentially different from humans? We addressed this issue by examining subjective feelings, electrophysiological activities, and functional magnetic resonance imaging signals during perception of pain and neutral expressions of faces that were recognized as patients or humanoid robots. We found that healthy adults reported deceased feelings of understanding and sharing of humanoid robots' compared to patients' pain. Moreover, humanoid robot (vs. patient) identities reduced long-latency electrophysiological responses and blood oxygenation level-dependent signals in the left temporoparietal junction in response to pain (vs. neutral) expressions. Furthermore, we showed evidence that humanoid robot identities inhibited a causal input from the right ventral lateral prefrontal cortex to the left temporoparietal junction, contrasting the opposite effect produced by patient identities. These results suggest a neural model of modulations of empathy by humanoid robot identity through interactions between the cognitive and affective empathy networks, which provides a neurocognitive basis for understanding human-robot interactions.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Multimodal optoacoustic imaging: methods and contrast materials.

Optoacoustic (OA) imaging offers powerful capabilities for interrogating biological tissues with rich optical absorption contrast while maintaining high spatial resolution for deep tissue observations. The spectrally distinct absorption of visible and near-infrared photons by endogenous tissue chromophores facilitates extraction of diverse anatomic, functional, molecular, and metabolic information from living tissues across various scales, from organelles and cells to whole organs and organisms. The primarily blood-related contrast and limited penetration depth of OA imaging have fostered the development of multimodal approaches to fully exploit the unique advantages and complementarity of the method. We review the recent hybridization efforts, including multimodal combinations of OA with ultrasound, fluorescence, optical coherence tomography, Raman scattering microscopy and magnetic resonance imaging as well as ionizing methods, such as X-ray computed tomography, single-photon-emission computed tomography and positron emission tomography. Considering that most molecules absorb light across a broad range of the electromagnetic spectrum, the OA interrogations can be extended to a large number of exogenously administered small molecules, particulate agents, and genetically encoded labels. This unique property further makes contrast moieties used in other imaging modalities amenable for OA sensing.

Cardiovascular Imaging in Contemporary Cardio-Oncology: A Scientific Statement From the American Heart Association.

Advances in cancer therapeutics have led to dramatic improvements in survival, now inclusive of nearly 20 million patients and rising. However, cardiovascular toxicities associated with specific cancer therapeutics adversely affect the outcomes of patients with cancer. Advances in cardiovascular imaging have solidified the critical role for robust methods for detecting, monitoring, and prognosticating cardiac risk among patients with cancer. However, decentralized evaluations have led to a lack of consensus on the optimal uses of imaging in contemporary cancer treatment (eg, immunotherapy, targeted, or biological therapy) settings. Similarly, available isolated preclinical and clinical studies have provided incomplete insights into the effectiveness of multiple modalities for cardiovascular imaging in cancer care. The aims of this scientific statement are to define the current state of evidence for cardiovascular imaging in the cancer treatment and survivorship settings and to propose novel methodological approaches to inform the optimal application of cardiovascular imaging in future clinical trials and registries. We also propose an evidence-based integrated approach to the use of cardiovascular imaging in routine clinical settings. This scientific statement summarizes and clarifies available evidence while providing guidance on the optimal uses of multimodality cardiovascular imaging in the era of emerging anticancer therapies.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain.

PURPOSE: The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS: 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS: Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS: Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Multimodal neuroimaging fusion unravel structural-functional-neurotransmitter change in Parkinson's disease with impulse control disorders.

BACKGROUND: Impulse control disorders (ICD) in Parkinson's disease (PD) is highly multifactorial in etiology and has intricate neural mechanisms. Our multimodal neuroimaging study aimed to investigate the specific patterns of structure-function-neurotransmitter interactions underlying ICD. METHODS: Thirty PD patients with ICD (PD-ICD), 30 without ICD (PD-NICD) and 32 healthy controls (HCs) were recruited. Gyrification and perivascular spaces (PVS) were computed to capture the alternations of cortical surface morphology and glymphatic function. Seed-based functional connectivity (FC) were performed to identify the corresponding functional changes. Further, JuSpace toolbox were employed for cross-modal correlations to evaluate whether the spatial patterns of functional alterations in ICD patients were associated with specific neurotransmitter system. RESULTS: Compared to PD-NICD, PD-ICD patients showed hypogyrification and enlarged PVS volume fraction in the left orbitofrontal gyrus (OFG), as well as decreased FC between interhemispheric OFG. The interhemispheric OFG connectivity reduction was associated with spatial distribution of µ-opioid pathway (r = -0.186, p = 0.029, false discovery rate corrected). ICD severity was positively associated with the PVS volume fraction of left OFG (r = 0.422, p = 0.032). Furthermore, gyrification index (LGI) and percent PVS (pPVS) in OFG and their combined indicator showed good performance in differentiating PD-ICD from PD-NICD. CONCLUSIONS: Our findings indicated that the co-altered structure-function-neurotransmitter interactions of OFG might be involved in the pathogenesis of ICD.

Combining Nuclear Medicine With Other Modalities: Future Prospect for Multimodality Imaging.

This meeting report summarizes a consultants meeting that was held at International Atomic Energy Agency Headquarters, Vienna, in July 2022 to provide an update on the development of multimodality imaging by combining nuclear medicine imaging agents with other nonradioactive molecular probes and/or biomedical imaging techniques.

Multimodal cross-examination of progressive apraxia of speech by diffusion tensor imaging-based tractography and Tau-PET scans.

Progressive apraxia of speech (PAOS) is a 4R tauopathy characterized by difficulties with motor speech planning. Neurodegeneration in PAOS targets the premotor cortex, particularly the supplementary motor area (SMA), with degeneration of white matter (WM) tracts connecting premotor and motor cortices and Broca's area observed on diffusion tensor imaging (DTI). We aimed to assess flortaucipir uptake across speech-language-related WM tracts identified using DTI tractography in PAOS. Twenty-two patients with PAOS and 26 matched healthy controls were recruited by the Neurodegenerative Research Group (NRG) and underwent MRI and flortaucipir-PET. The patient population included patients with primary progressive apraxia of speech (PPAOS) and non-fluent variant/agrammatic primary progressive aphasia (agPPA). Flortaucipir PET scans and DTI were coregistered using rigid registration with a mutual information cost function in subject space. Alignments between DTI and flortaucipir PET were inspected in all cases. Whole-brain tractography was calculated using deterministic algorithms by a tractography reconstruction tool (DSI-studio) and specific tracts were identified using an automatic fiber tracking atlas-based method. Fractional anisotropy (FA) and flortaucipir standardized uptake value ratios (SUVRs) were averaged across the frontal aslant tract, arcuate fasciculi, inferior frontal-occipital fasciculus, inferior and middle longitudinal fasciculi, as well as the SMA commissural fibers. Reduced FA (p < .0001) and elevated flortaucipir SUVR (p = .0012) were observed in PAOS cases compared to controls across all combined WM tracts. For flortaucipir SUVR, the greatest differentiation of PAOS from controls was achieved with the SMA commissural fibers (area under the receiver operator characteristic curve [AUROC] = 0.83), followed by the left arcuate fasciculus (AUROC = 0.75) and left frontal aslant tract (AUROC = 0.71). Our findings demonstrate that flortaucipir uptake is increased across WM tracts related to speech/language difficulties in PAOS.

Multimodal neuroimaging exploration of the mechanisms of sleep quality deterioration after SARS-CoV-2 Omicron infection.

BACKGROUND: To evaluate the neurological alterations induced by Omicron infection, to compare brain changes in chronic insomnia with those in exacerbated chronic insomnia in Omicron patients, and to examine individuals without insomnia alongside those with new-onset insomnia. METHODS: In this study, a total of 135 participants were recruited between January 11 and May 4, 2023, including 26 patients with chronic insomnia without exacerbation, 24 patients with chronic insomnia with exacerbation, 40 patients with no sleep disorder, and 30 patients with new-onset insomnia after infection with Omicron (a total of 120 participants with different sleep statuses after infection), as well as 15 healthy controls who were never infected with Omicron. Neuropsychiatric data, clinical symptoms, and multimodal magnetic resonance imaging data were collected. The gray matter thickness and T1, T2, proton density, and perivascular space values were analyzed. Associations between changes in multimodal magnetic resonance imaging findings and neuropsychiatric data were evaluated with correlation analyses. RESULTS: Compared with healthy controls, gray matter thickness changes were similar in the patients who have and do not have a history of chronic insomnia groups after infection, including an increase in cortical thickness near the parietal lobe and a reduction in cortical thickness in the frontal, occipital, and medial brain regions. Analyses showed a reduced gray matter thickness in patients with chronic insomnia compared with those with an aggravation of chronic insomnia post-Omicron infection, and a reduction was found in the right medial orbitofrontal region (mean [SD], 2.38 [0.17] vs. 2.67 [0.29] mm; P < 0.001). In the subgroups of Omicron patients experiencing sleep deterioration, patients with a history of chronic insomnia whose insomnia symptoms worsened after infection displayed heightened medial orbitofrontal cortical thickness and increased proton density values in various brain regions. Conversely, patients with good sleep quality who experienced a new onset of insomnia after infection exhibited reduced cortical thickness in pericalcarine regions and decreased proton density values. In new-onset insomnia patients post-Omicron infection, the thickness in the right pericalcarine was negatively correlated with the Self-rating Anxiety Scale (r = - 0.538, P = 0.002, PFDR = 0.004) and Self-rating Depression Scale (r = - 0.406, P = 0.026, PFDR = 0.026) scores. CONCLUSIONS: These findings help us understand the pathophysiological mechanisms involved when Omicron invades the nervous system and induces various forms of insomnia after infection. In the future, we will continue to pay attention to the dynamic changes in the brain related to insomnia caused by Omicron infection.

Removing independent noise in systems neuroscience data using DeepInterpolation.

Progress in many scientific disciplines is hindered by the presence of independent noise. Technologies for measuring neural activity (calcium imaging, extracellular electrophysiology and functional magnetic resonance imaging (fMRI)) operate in domains in which independent noise (shot noise and/or thermal noise) can overwhelm physiological signals. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatiotemporal nonlinear interpolation model using only raw noisy samples. Applying DeepInterpolation to two-photon calcium imaging data yielded up to six times more neuronal segments than those computed from raw data with a 15-fold increase in the single-pixel signal-to-noise ratio (SNR), uncovering single-trial network dynamics that were previously obscured by noise. Extracellular electrophysiology recordings processed with DeepInterpolation yielded 25% more high-quality spiking units than those computed from raw data, while DeepInterpolation produced a 1.6-fold increase in the SNR of individual voxels in fMRI datasets. Denoising was attained without sacrificing spatial or temporal resolution and without access to ground truth training data. We anticipate that DeepInterpolation will provide similar benefits in other domains in which independent noise contaminates spatiotemporally structured datasets.

Surgical evidence-based comparison of [68Ga]Ga-FAPI-04 PET and MRI-DWI for assisting debulking surgery in ovarian cancer patients.

PURPOSE: Imaging assessment of abdominopelvic tumor burden is crucial for debulking surgery decision in ovarian cancer patients. This study aims to compare the efficiency of [68Ga]Ga-FAPI-04 FAPI PET and MRI-DWI in the preoperative evaluation and its potential impact to debulking surgery decision. METHODS: Thirty-six patients with suspected/confirmed ovarian cancer were enrolled and underwent integrated [68Ga]Ga-FAPI-04 PET/MRI. Nineteen patients (15 stage III-IV and 4 I-II stage) who underwent debulking surgery were involved in the diagnostic efficiency analysis. The images of [68Ga]Ga-FAPI-04 PET and MRI-DWI were visually analyzed respectively. Immunohistochemistry on FAP was performed in metastatic lesions to investigate the radiological missing of [68Ga]Ga-FAPI-04 PET as well as its different performance in primary debulking surgery (PDS) and interval debulking surgery (IDS) patients. Potential imaging impact on management was also studied in 35 confirmed ovarian cancer patients. RESULTS: [68Ga]Ga-FAPI-04 PET displayed higher sensitivity (76.8% vs.59.9%), higher accuracy (84.9% vs. 80.7%), and lower missing rate (23.2% vs. 40.1%) than MRI-DWI in detecting abdominopelvic metastasis. The diagnostic superiority of [68Ga]Ga-FAPI-04 PET is more obvious in PDS patients but diminished in IDS patients. [68Ga]Ga-FAPI-04 PET outperformed MRI-DWI in 70.8% abdominopelvic regions (17/24), which contained seven key regions that impact the resectability and surgical complexity. MRI-DWI hold advantage in the peritoneal surface of the bladder and the central tendon of the diaphragm. Of the contradictory judgments between the two modalities (14.9%), [68Ga]Ga-FAPI-04 PET correctly identified more lesions, particularly in PDS patients (73.8%). In addition, FAP expression was independent of lesion size and decreased in IDS patients. [68Ga]Ga-FAPI-04 PET changed 42% of surgical planning that was previously based on MRI-DWI. CONCLUSION: [68Ga]Ga-FAPI-04 PET is more efficient in assisting debulking surgery in ovarian cancer patients than MRI-DWI. Integrated [68Ga]Ga-FAPI-04 PET/MR imaging is a potential method for planning debulking surgery in ovarian cancer patients.

Thoracic aortic microcalcification activity in combined positron emission tomography and magnetic resonance imaging.

INTRODUCTION: Non-invasive detection of pathological changes in thoracic aortic disease remains an unmet clinical need particularly for patients with congenital heart disease. Positron emission tomography combined with magnetic resonance imaging (PET-MRI) could provide a valuable low-radiation method of aortic surveillance in high-risk groups. Quantification of aortic microcalcification activity using sodium [18F]fluoride holds promise in the assessment of thoracic aortopathies. We sought to evaluate aortic sodium [18F]fluoride uptake in PET-MRI using three methods of attenuation correction compared to positron emission tomography computed tomography (PET-CT) in patients with bicuspid aortic valve, METHODS: Thirty asymptomatic patients under surveillance for bicuspid aortic valve disease underwent sodium [18F]fluoride PET-CT and PET-MRI of the ascending thoracic aorta during a single visit. PET-MRI data were reconstructed using three iterations of attenuation correction (Dixon, radial gradient recalled echo with two [RadialVIBE-2] or four [RadialVIBE-4] tissue segmentation). Images were qualitatively and quantitatively analysed for aortic sodium [18F]fluoride uptake on PET-CT and PET-MRI. RESULTS: Aortic sodium [18F]fluoride uptake on PET-MRI was visually comparable with PET-CT using each reconstruction and total aortic standardised uptake values on PET-CT strongly correlated with each PET-MRI attenuation correction method (Dixon R = 0.70; RadialVIBE-2 R = 0.63; RadialVIBE-4 R = 0.64; p < 0.001 for all). Breathing related artefact between soft tissue and lung were detected using Dixon and RadialVIBE-4 but not RadialVIBE-2 reconstructions, with the presence of this artefact adjacent to the atria leading to variations in blood pool activity estimates. Consequently, quantitative agreements between radiotracer activity on PET-CT and PET-MRI were most consistent with RadialVIBE-2. CONCLUSION: Ascending aortic microcalcification analysis in PET-MRI is feasible with comparable findings to PET-CT. RadialVIBE-2 tissue attenuation correction correlates best with the reference standard of PET-CT and is less susceptible to artefact. There remain challenges in segmenting tissue types in PET-MRI reconstructions, and improved attenuation correction methods are required.

Recent advances in multimodal imaging in tetralogy of fallot and double outlet right ventricle.

PURPOSE OF REVIEW: In the ever-evolving field of medical imaging, this review highlights significant advancements in preoperative and postoperative imaging for Tetralogy of Fallot (TOF) and double outlet right ventricle (DORV) over the past 18 months. RECENT FINDINGS: This review showcases innovations in echocardiography such as 3D speckle tracking echocardiography (3DSTE) for assessing right ventricle-pulmonary artery coupling (RVPAC) and Doppler velocity reconstruction (DoVeR) for intracardiac flow fields evaluation. Furthermore, advances in assessment of cardiovascular anatomy using computed tomography (CT) improve the integration of imaging in ablation procedures. Additionally, the inclusion of cardiac magnetic resonance (CMR) parameters as risk score predictors for morbidity, and mortality and for timing of pulmonary valve replacement (PVR) indicates its significance in clinical management. The utilization of 4D flow techniques for postoperative hemodynamic assessment promises new insights into pressure mapping. Lastly, emerging technologies such as 3D printing and 3D virtual reality are expected to improve image quality and surgical confidence in preoperative planning. SUMMARY: Developments in multimodality imaging in TOF and DORV are poised to shape the future of clinical practice in this field.

Microarray-Based CD38 Peptide Probe Screening for Multiple Myeloma Imaging.

Assessing CD38 expression in vivo has become a significant element in multiple myeloma (MM) therapy, as it can be used to detect lesions and forecast the effectiveness of treatment. Accurate diagnosis requires a multifunctional, high-throughput probe screening platform to develop molecular probes for tumor-targeted multimodal imaging and treatment. Here, we investigated a microarray chip-based strategy for high-throughput screening of peptide probes for CD38. We obtained two new target peptides, CA-1 and CA-2, from a 105 peptide library with a dissociation constant (KD) of 10-7 M. The specificity and affinity of the target peptides were confirmed at the molecular and cellular levels. Peptide probes were labeled with indocyanine green (ICG) dye and 68Ga-DOTA, which were injected into a CD38-positive Ramos tumor-bearing mouse via its tail vein, and small animal fluorescence and positron emission tomography (PET) imaging showed that the peptide probes could show specific enrichment in the tumor tissue. Our study shows that a microchip-based screening of peptide probes can be used as a promising imaging tool for MM diagnosis.