Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems

Determination of optimal injection dose in a small animal-dedicated positron emission tomography for non-human primate neurological studies

This study aimed to determine the optimal injection dose for non-human primate positron emission tomography (PET). We first used a monkey brain phantom with a volume of 80,000 mm3 containing 250 MBq of [18F]FDG. Next, we compared the radioactivity difference between the PET images and the actual radioactivity from the dose calibrator to determine the low-error range. We then evaluated the image quality using the NEMA-NU phantom. Finally, [18F]FP-CIT PET images were obtained from two monkeys with middle and high doses. As a result, PET images with a middle injected dose generated reasonable image quality and showed a high signal-to-noise ratio in monkey brain PET with [18F]FP-CIT. These results are expected to be actively applied in PET research using non-human primates.

Dose estimation in patients from different protocols of 18F-FDG PET/CT studies and analysis of optimization strategies

This study aimed to evaluate the dose in different protocols of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) procedure. The retrospective study involves 207 patients with confirmed malignancies who underwent PET/CT. Effective dose (E) from PET was estimated based on injected activity and dose coefficient as per International Commission on Radiation Protection (ICRP) 128. Estimation of E from CT was done utilizing the dose length product (DLP) method and conversion factors as per ICRP 102. There was a significant statistical difference observed in E between different PET/CT protocols (P < .001). E of PET in the whole body (WB) was found to be 4.9 ± 0.9 mSv, whereas mean volume computed tomography dose indexvol, DLP, and E of CT in WB were 7.0 ± 0.2 mGy, 674.3 ± 80.7 mGy.cm, and 10.1 ± 1.2 mSv, respectively. No linear correlation was seen between the size-specific dose estimate and E of CT (r = -0.003; P = .978). The total mean E in WB PET/CT was 17.0 ± 1.7 mSv. CT dose was contributing more than PET dose in all protocols except brain PET/CT. Optimization strategies can be evaluated only if monitored periodically.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

FDG-PET-CT as an early detection method for tuberculosis: a systematic review and meta-analysis

Tuberculosis (TB) causes major public health problems worldwide. Fighting TB requires sustained efforts in health prevention, diagnosis and treatment. Previous literature has shown that conventional diagnostic methods like X-ray and sputum microscopy often miss early or extrapulmonary TB due to their limited sensitivity. Blood tests, while useful, lack the anatomical detail needed for precise localization of TB lesions. A possible step forward in the fight against TB could be the use of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and Computed Tomography (CT). This meta-analysis discusses the current literature, including the methods, results and implications of using FDG-PET-CT in the early diagnosis of TB. Analysis of the studies showed that the sensitivity of FDG-PET-CT as a potential method for early detection of TB was 82.6%.

Effect of dynamic exclusion and the use of FAIMS, DIA and MALDI-mass spectrometry imaging with ion mobility on amyloid protein identification

Amyloidosis is a disease characterized by local and systemic extracellular deposition of amyloid protein fibrils where its excessive accumulation in tissues and resistance to degradation can lead to organ failure. Diagnosis is challenging because of approximately 36 different amyloid protein subtypes. Imaging methods like immunohistochemistry and the use of Congo red staining of amyloid proteins for laser capture microdissection combined with liquid chromatography tandem mass spectrometry (LMD/LC-MS/MS) are two diagnostic methods currently used depending on the expertise of the pathology laboratory. Here, we demonstrate a streamlined in situ amyloid peptide spatial mapping by Matrix Assisted Laser Desorption Ionization-Mass Spectrometry Imaging (MALDI-MSI) combined with Trapped Ion Mobility Spectrometry for potential transthyretin (ATTR) amyloidosis subtyping. While we utilized the standard LMD/LC-MS/MS workflow for amyloid subtyping of 31 specimens from different organs, we also evaluated the potential introduction in the MS workflow variations in data acquisition parameters like dynamic exclusion, or testing Data Dependent Acquisition combined with High-Field Asymmetric Waveform Ion Mobility Spectrometry (DDA FAIMS) versus Data Independent Acquisition (DIA) for enhanced amyloid protein identification at shorter acquisition times. We also demonstrate the use of Mascot's Error Tolerant Search and PEAKS de novo sequencing for the sequence variant analysis of amyloidosis specimens.

Molecular imaging of thyroid and parathyroid diseases

INTRODUCTION

Molecular imaging of thyroid and parathyroid diseases has changed in recent years due to the introduction of new radiopharmaceuticals and new imaging techniques. Accordingly, we provided an clinicians-oriented overview of such techniques and their indications.

AREAS COVERED

A review of the literature was performed in the PubMed, Web of Science, and Scopus without time or language restrictions through the use of one or more fitting search criteria and terms as well as through screening of references in relevant selected papers. Literature up to and including December 2023 was included. Screening of titles/abstracts and removal of duplicates was performed and the full texts of the remaining potentially relevant articles were retrieved and reviewed.

EXPERT OPINION

Thyroid and parathyroid scintigraphy remains integral in patients with thyrotoxicosis, thyroid nodules, differentiated thyroid cancer and, respectively, hyperparathyroidism. In the last years positron-emission tomography with different tracers emerged as a more accurate alternative in evaluating indeterminate thyroid nodules [18F-fluorodeoxyglucose (FDG)], differentiated thyroid cancer [124I-iodide, 18F-tetrafluoroborate, 18F-FDG] and hyperparathyroidism [18F-fluorocholine]. Other PET tracers are useful in evaluating relapsing/advanced forms of medullary thyroid cancer (18F-FDOPA) and selecting patients with advanced follicular and medullary thyroid cancers for theranostic treatments (68Ga/177Ga-somatostatin analogues).

Synthetic 18F labeled biomolecules that are selective and promising for PET imaging: major advances and applications

The concept of positron emission tomography (PET) based imaging was developed more than 40 years ago. It has been a widely adopted technique for detecting and staging numerous diseases in clinical settings, particularly cancer, neuro- and cardio-diseases. Here, we reviewed the evolution of PET and its advantages over other imaging modalities in clinical settings. Primarily, this review discusses recent advances in the synthesis of 18F radiolabeled biomolecules in light of the widely accepted performance for effective PET. The discussion particularly emphasizes the 18F-labeling chemistry of carbohydrates, lipids, amino acids, oligonucleotides, peptides, and protein molecules, which have shown promise for PET imaging in recent decades. In addition, we have deliberated on how 18F-labeled biomolecules enable the detection of metabolic changes at the cellular level and the selective imaging of gross anatomical localization via PET imaging. In the end, the review discusses the future perspective of PET imaging to control disease in clinical settings. We firmly believe that collaborative multidisciplinary research will further widen the comprehensive applications of PET approaches in the clinical management of cancer and other pathological outcomes.

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.

A Review of Advances in Molecular Imaging of Rheumatoid Arthritis: From In Vitro to Clinic Applications Using Radiolabeled Targeting Vectors with Technetium-99m

Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in synovitis and pannus formation. Timely detection and effective management of RA are pivotal for mitigating inflammatory arthritis consequences, potentially influencing disease progression. Nuclear medicine using radiolabeled targeted vectors presents a promising avenue for RA diagnosis and response to treatment assessment. Radiopharmaceutical such as technetium-99m (99mTc), combined with single photon emission computed tomography (SPECT) combined with CT (SPECT/CT), introduces a more refined diagnostic approach, enhancing accuracy through precise anatomical localization, representing a notable advancement in hybrid molecular imaging for RA evaluation. This comprehensive review discusses existing research, encompassing in vitro, in vivo, and clinical studies to explore the application of 99mTc radiolabeled targeting vectors with SPECT imaging for RA diagnosis. The purpose of this review is to highlight the potential of this strategy to enhance patient outcomes by improving the early detection and management of RA.

Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data

BACKGROUND

Cardiac positron emission tomography (PET) can visualize and quantify the molecular and physiological pathways of cardiac function. However, cardiac and respiratory motion can introduce blurring that reduces PET image quality and quantitative accuracy. Dual cardiac- and respiratory-gated PET reconstruction can mitigate motion artifacts but increases noise as only a subset of data are used for each time frame of the cardiac cycle.

AIM

The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images.

METHODS

Our study included retrospective list-mode data from 40 patients who underwent an 18F-fluorodeoxyglucose (18F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data.

RESULTS

Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE.

CONCLUSION

The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network.

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

Intravital observation of high-scattering and dense-labeling hepatic tissues using multi-photon fluorescence microscopy

Achieving high-resolution and large-depth microscopic imaging in vivo under conditions characterized by high-scattering and dense-labeling, as commonly encountered in the liver, poses a formidable challenge. Here, through the optimization of multi-photon fluorescence excitation window, tailored to the unique optical properties of the liver, intravital microscopic imaging of hepatocytes and hepatic blood vessels with high spatial resolution was attained. It's worth noting that resolution degradation caused by tissue scattering of excitation light was mitigated by accounting for moderate tissue self-absorption. Leveraging high-quality multi-photon fluorescence microscopy, we discerned structural and functional alterations in hepatocytes during drug-induced acute liver failure. Furthermore, a reduction in indocyanine green metabolism rates associated with acute liver failure was observed using NIR-II fluorescence macroscopic imaging.

Enhancing the Diagnostic Utility of ASL Imaging in Temporal Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework

Background and Significance

Positron Emission Tomography (PET) using fluorodeoxyglucose (FDG-PET) is a standard imaging modality for detecting areas of hypometabolism associated with the seizure onset zone (SOZ) in temporal lobe epilepsy (TLE). However, FDG-PET is costly and involves the use of a radioactive tracer. Arterial Spin Labeling (ASL) offers an MRI-based quantification of cerebral blood flow (CBF) that could also help localize the SOZ, but its performance in doing so, relative to FDG-PET, is limited. In this study, we seek to improve ASL's diagnostic performance by developing a deep learning framework for synthesizing FDG-PET-like images from ASL and structural MRI inputs.

Methods

We included 68 epilepsy patients, out of which 36 had well lateralized TLE. We compared the coupling between FDG-PET and ASL CBF values in different brain regions, as well as the asymmetry of these values across the brain. We additionally assessed each modality's ability to lateralize the SOZ across brain regions. Using our paired PET-ASL data, we developed FlowGAN, a generative adversarial neural network (GAN) that synthesizes PET-like images from ASL and T1-weighted MRI inputs. We tested our synthetic PET images against the actual PET images of subjects to assess their ability to reproduce clinically meaningful hypometabolism and asymmetries in TLE.

Results

We found variable coupling between PET and ASL CBF values across brain regions. PET and ASL had high coupling in neocortical temporal and frontal brain regions (Spearman's r > 0.30, p < 0.05) but low coupling in mesial temporal structures (Spearman's r < 0.30, p > 0.05). Both whole brain PET and ASL CBF asymmetry values provided good separability between left and right TLE subjects, but PET (AUC = 0.96, 95% CI: [0.88, 1.00]) outperformed ASL (AUC = 0.81; 95% CI: [0.65, 0.96]). FlowGAN-generated images demonstrated high structural similarity to actual PET images (SSIM = 0.85). Globally, asymmetry values were better correlated between synthetic PET and original PET than between ASL CBF and original PET, with a mean correlation increase of 0.15 (95% CI: [0.07, 0.24], p<0.001, Cohen's d = 0.91). Furthermore, regions that had poor ASL-PET correlation (e.g. mesial temporal structures) showed the greatest improvement with synthetic PET images.

Conclusions

FlowGAN improves ASL's diagnostic performance, generating synthetic PET images that closely mimic actual FDG-PET in depicting hypometabolism associated with TLE. This approach could improve non-invasive SOZ localization, offering a promising tool for epilepsy presurgical assessment. It potentially broadens the applicability of ASL in clinical practice and could reduce reliance on FDG-PET for epilepsy and other neurological disorders.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient

Nuclear cardiology, employing advanced imaging technologies like positron emission tomography (PET) and single photon emission computed tomography (SPECT), is instrumental in diagnosing, risk stratifying, and managing heart diseases. Concurrently, precision medicine advocates for treatments tailored to each patient's genetic, environmental, and lifestyle specificities, promising a revolution in personalized cardiovascular care. This review explores the synergy between nuclear cardiology and precision medicine, highlighting advancements, potential enhancements in patient outcomes, and the challenges and opportunities of this integration. We examined the evolution of nuclear cardiology technologies, including PET and SPECT, and their role in cardiovascular diagnostics. We also delved into the principles of precision medicine, focusing on genetic and molecular profiling, data analytics, and individualized treatment strategies. The integration of these domains aims to optimize diagnostic accuracy, therapeutic interventions, and prognostic evaluations in cardiovascular care. Advancements in molecular imaging and the application of artificial intelligence in nuclear cardiology have significantly improved the precision of diagnostics and treatment plans. The adoption of precision medicine principles in nuclear cardiology enables the customization of patient care, leveraging genetic information and biomarkers for enhanced therapeutic outcomes. However, challenges such as data integration, accessibility, cost, and the need for specialized expertise persist. The confluence of nuclear cardiology and precision medicine offers a promising pathway toward revolutionizing cardiovascular healthcare, providing more accurate, effective, and personalized patient care. Addressing existing challenges and fostering interdisciplinary collaboration is crucial for realizing the full potential of this integration in improving patient outcomes.

The detection instrumentation and geometric design of clinical PET scanner: towards better performance and broader clinical applications

Positron emission tomography (PET) is a powerful medical imaging modality used in nuclear medicine to diagnose and monitor various clinical diseases in patients. It is more sensitive and produces a highly quantitative mapping of the three-dimensional biodistribution of positron-emitting radiotracers inside the human body. The underlying technology is constantly evolving, and recent advances in detection instrumentation and PET scanner design have significantly improved the medical diagnosis capabilities of this imaging modality, making it more efficient and opening the way to broader, innovative, and promising clinical applications. Some significant achievements related to detection instrumentation include introducing new scintillators and photodetectors as well as developing innovative detector designs and coupling configurations. Other advances in scanner design include moving towards a cylindrical geometry, 3D acquisition mode, and the trend towards a wider axial field of view and a shorter diameter. Further research on PET camera instrumentation and design will be required to advance this technology by improving its performance and extending its clinical applications while optimising radiation dose, image acquisition time, and manufacturing cost. This article comprehensively reviews the various parameters of detection instrumentation and PET system design. Firstly, an overview of the historical innovation of the PET system has been presented, focusing on instrumental technology. Secondly, we have characterised the main performance parameters of current clinical PET and detailed recent instrumental innovations and trends that affect these performances and clinical practice. Finally, prospects for this medical imaging modality are presented and discussed. This overview of the PET system's instrumental parameters enables us to draw solid conclusions on achieving the best possible performance for the different needs of different clinical applications.

Motion-correction strategies for enhancing whole-body PET imaging

Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study

PURPOSE

Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks.

METHODS

The pre-trained p2p-U-Net-GAN and HighRes3D architectures were fine-tuned with PET-only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL-assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)-derived S-values to be used for internal dosimetry.

RESULTS

A fair comparison with manual low-dose CT-aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89-0.93, 0.98-0.99, and 0.87-0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S-values varied between 0.1% and 6% with a maximum attributed to the stomach.

CONCLUSION

The findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL-based methods provide automated and fast segmentation of whole-body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F-FDG-PET image segmentation.

Halide Perovskites and Their Derivatives for Efficient, High-Resolution Direct Radiation Detection: Design Strategies and Applications

The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead-halide perovskites (LHPs). The large mobility-lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well-suited for radiation detectors, especially given the heavy elements present, which is essential for strong X-ray and γ-ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 106 µC Gyair -1 cm-2 ), limit of detection (directly measured values down to 1.5 nGyair s-1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead-free perovskite-inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non-invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite-inspired material detectors, and how their properties and performance link with critical applications in non-invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Synthesis and preclinical evaluation of a 89Zr-labelled human single chain antibody for non-invasive detection of hepatic myofibroblasts in acute liver injury

Synaptophysin is expressed on fibrogenic hepatic myofibroblasts. C1-3 is a single chain human antibody (scAb) that binds specifically to synaptophysin on hepatic myofibroblasts, providing a targeting vector for novel in vivo imaging agents of chronic liver disease. C1-3 and a negative control scAb, CSBD9, were radiolabelled with zirconium-89 via desferrioxamine chelation to enable non-invasive molecular imaging with positron emission tomography (PET). DFO-scAb conjugates were characterised by gel electrophoresis (SDS-PAGE) and MALDI-TOF spectrometry, and 89Zr-labelled with high radiolabelling efficiency (99%). [89Zr]Zr-DFO-C1-3 exhibited high in vitro stability (> 99%) in mouse and human sera over 3 days at 25 and 37 °C. Activated hepatic myofibroblasts incubated with [89Zr]Zr-DFO-C1-3 displayed significantly higher internalised activity (59.46%, P = 0.001) compared to the [89Zr]Zr-DFO-CSBD9 control, indicating synaptophysin-mediated uptake and high binding specificity of [89Zr]Zr-DFO-C1-3. Mice with CCl4-induced acute liver damage exhibited significantly higher liver uptake of [89Zr]Zr-DFO-C1-3, compared to controls, confirmed by both Cerenkov imaging and ex vivo gamma counting (4.41 ± 0.19%ID/g, P < 0.0001). CCl4-induced liver damage and the number of hepatic myofibroblasts was confirmed by αSMA staining of liver sections. These findings indicate that [89Zr]Zr-DFO-C1-3 has promising utility as a PET imaging agent for non-invasive detection of hepatic myofibroblasts following acute liver injury.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Significance

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ a and reduced scattering coefficient μ s ' ) and scalp and skull thicknesses.

Aim

We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance.

Approach

We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β ) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.

Results

DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ a and μ s ' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.

Conclusions

DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.

Receptor for advanced glycation end-products: Biological significance and imaging applications

The receptor for advanced glycation end-products (RAGE or AGER) is a transmembrane, immunoglobulin-like receptor that, due to its multiple isoform structures, binds to a diverse range of endo- and exogenous ligands. RAGE activation caused by the ligand binding initiates a cascade of complex pathways associated with producing free radicals, such as reactive nitric oxide and oxygen species, cell proliferation, and immunoinflammatory processes. The involvement of RAGE in the pathogenesis of disorders such as diabetes, inflammation, tumor progression, and endothelial dysfunction is dictated by the accumulation of advanced glycation end-products (AGEs) at pathologic states leading to sustained RAGE upregulation. The involvement of RAGE and its ligands in numerous pathologies and diseases makes RAGE an interesting target for therapy focused on the modulation of both RAGE expression or activation and the production or exogenous administration of AGEs. Despite the known role that the RAGE/AGE axis plays in multiple disease states, there remains an urgent need to develop noninvasive, molecular imaging approaches that can accurately quantify RAGE levels in vivo that will aid in the validation of RAGE and its ligands as biomarkers and therapeutic targets. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Diagnostic Tools > Biosensing.

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain

BACKGROUND

Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low signal-to-noise ratio. Deep learning (DL) techniques such as generative adversarial networks (GANs) and convolutional neural network (CNN) have the capability to enhance the quality of images derived from noisy or low-quality PET data, which encodes critical information about radioactivity distribution in the body.

PURPOSE

Our objective was to optimize the image quality and reduce noise in preclinical PET images by utilizing the sinogram domain as input for DL models, resulting in improved image quality as compared to LD-PET images.

METHODS

A GAN and CNN model were utilized to predict high-dose (HD) preclinical PET sinograms from the corresponding LD preclinical PET sinograms. In order to generate the datasets, experiments were conducted on micro-phantoms, animal subjects (rats), and virtual simulations. The quality of DL-generated images was weighted by performing the following quantitative measures: structural similarity index measure (SSIM), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Additionally, DL input and output were both subjected to a spatial resolution calculation of full width half maximum (FWHM) and full width tenth maximum (FWTM). DL outcomes were then compared with the conventional denoising algorithms such as non-local means (NLM), block-matching, and 3D filtering (BM3D).

RESULTS

The DL models effectively learned image features and produced high-quality images, as reflected in the quantitative metrics. Notably, the FWHM and FWTM values of DL PET images exhibited significantly improved accuracy compared to LD, NLM, and BM3D PET images, and just as precise as HD PET images. The MSE loss underscored the excellent performance of the models, indicating that the models performed well. To further improve the training, the generator loss (G loss) was increased to a value higher than the discriminator loss (D loss), thereby achieving convergence in the GAN model.

CONCLUSIONS

The sinograms generated by the GAN network closely resembled real HD preclinical PET sinograms and were more realistic than LD. There was a noticeable improvement in image quality and noise factor in the predicted HD images. Importantly, DL networks did not fully compromise the spatial resolution of the images.

Quo Vadis Hyperpolarized 13C MRI?

Over the last two decades, hyperpolarized 13C MRI has gained significance in both preclinical and clinical studies, hereby relying on technologies like PHIP-SAH (ParaHydrogen-Induced Polarization-Side Arm Hydrogenation), SABRE (Signal Amplification by Reversible Exchange), and dDNP (dissolution Dynamic Nuclear Polarization), with dDNP being applied in humans. A clinical dDNP polarizer has enabled studies across 24 sites, despite challenges like high cost and slow polarization. Parahydrogen-based techniques like SABRE and PHIP offer faster, more cost-efficient alternatives but require molecule-specific optimization. The focus has been on imaging metabolism of hyperpolarized probes, which requires long T1, high polarization and rapid contrast generation. Efforts to establish novel probes, improve acquisition techniques and enhance data analysis methods including artificial intelligence are ongoing. Potential clinical value of hyperpolarized 13C MRI was demonstrated primarily for treatment response assessment in oncology, but also in cardiology, nephrology, hepatology and CNS characterization. In this review on biomedical hyperpolarized 13C MRI, we summarize important and recent advances in polarization techniques, probe development, acquisition and analysis methods as well as clinical trials. Starting from those we try to sketch a trajectory where the field of biomedical hyperpolarized 13C MRI might go.

Elucidating the neuroimmunology of traumatic brain injury: methodological approaches to unravel intercellular communication and function

The neuroimmunology of traumatic brain injury (TBI) has recently gained recognition as a crucial element in the secondary pathophysiological consequences that occur following neurotrauma. Both immune cells residing within the central nervous system (CNS) and those migrating from the periphery play significant roles in the development of secondary brain injury. However, the precise mechanisms governing communication between innate and adaptive immune cells remain incompletely understood, partly due to a limited utilization of relevant experimental models and techniques. Therefore, in this discussion, we outline current methodologies that can aid in the exploration of TBI neuroimmunology, with a particular emphasis on the interactions between resident neuroglial cells and recruited lymphocytes. These techniques encompass adoptive cell transfer, intra-CNS injection(s), selective cellular depletion, genetic manipulation, molecular neuroimaging, as well as in vitro co-culture systems and the utilization of organoid models. By incorporating key elements of both innate and adaptive immunity, these methods facilitate the examination of clinically relevant interactions. In addition to these preclinical approaches, we also detail an emerging avenue of research that seeks to leverage human biofluids. This approach enables the investigation of how resident and infiltrating immune cells modulate neuroglial responses after TBI. Considering the growing significance of neuroinflammation in TBI, the introduction and application of advanced methodologies will be pivotal in advancing translational research in this field.

CycloSiFA: The Next Generation of Silicon-Based Fluoride Acceptors for Positron Emission Tomography (PET)

The ring-opening Si-fluorination of a variety of azasilole derivatives cyclo-1-(iPr2 Si)-4-X-C6 H3 -2-CH2 NR (4: R=2,6-iPr2 C6 H3 , X=H; 4 a: R=2,4,6-Me3 C6 H2 , X=H; 9: R=2,6-iPr2 C6 H3 , X=tBuMe2 SiO; 10: R=2,6-iPr2 C6 H3 , X=OH; 13: R=2,6-iPr2 C6 H3 , X=HCCCH2 O; 22: R=2,6-iPr2 C6 H3 , X=tBuMe2 SiCH2 O) with different 19 F-fluoride sources was studied, optimized and the experience gained was used in a translational approach to create a straightforward 18 F-labelling protocol for the azasilole derivatives [18 F]6 and [18 F]14. The latter constitutes a potential clickable CycloSiFA prosthetic group which might be used in PET tracer development using Cu-catalysed triazole formation. Based on our findings, CycloSiFA has the potential to become a new entry into non-canonical labelling methodologies for radioactive PET tracer development.

Second Wave, Late-Stage Neuroinflammation in Cleared Brains of Aged 5xFAD Alzheimer's Mice Detected by Macrolaser Light Sheet Microscopy Imaging

This study leverages the innovative imaging capabilities of macrolaser light-sheet microscopy to elucidate the 3D spatial visualization of AD-associated neuropathologic networks in the transparent brains of 44-week-old 5xFAD mice. Brain samples from ten AD and seven control mice were prepared through a hydrophilic tissue-clearing pipeline and immunostained with thioflavin S (β-amyloid), anti-CD11b antibody (microglia), and anti-ACSA-2 antibody (astrocytes). The 5xFAD group exhibited significantly higher average total surface volumes of β-amyloid accumulation than the control group (AD, 898,634,368 µm3 [383,355,488-1,324,986,752]; control, 33,320,178 µm3 [11,156,785-65,390,988], p = 0.0006). Within the AD group, there was significant interindividual and interindividual variability concerning the number and surface volume of individual amyloid particles throughout the entire brain. In the context of neuroinflammation, the 5xFAD group showed significantly higher average total surface volumes of anti-ACSA-2-labeled astrocytes (AD, 59,064,360 µm3 [27,815,500-222,619,280]; control, 20,272,722 µm3 [9,317,288-27,223,352], p = 0.0047) and anti-CD11b labeled microglia (AD, 51,210,100 µm3 [15,309,118-135,532,144]; control, 23,461,593 µm3 [14,499,170-27,924,110], p = 0.0162) than the control group. Contrary to the long-standing finding that early-stage neuroinflammation precedes the subsequent later-stage of neurodegeneration, our data reveal that the second wave, late-stage active neuroinflammation persists in the aged AD brains, even as they continue to show signs of ongoing neurodegeneration and significant amyloid accumulation.

OMNI: Gas Chromatograph Captures Seven Common PET Radiotracer Analytes in under 5 Minutes

A novel gas chromatography method was developed using automatic injections to identify and quantify the amount of residual solvents or analytes in samples of fluorine-18 and carbon-11 radiopharmaceuticals. This approach evaluates seven analytes in less than 5 versus 13 min of acquisition time. The method additionally includes a 3 min bakeout to aid in the removal and carry-over of higher-boiling impurities. Chromatographic parameters such as column temperature, hold time, column pressure, flow rate, and split ratios were adjusted and optimized to analyze radioactive drug samples containing analytes which include methanol, ethanol, acetone, acetonitrile, triethylamine, N,N-dimethylformamide, and dimethyl sulfoxide. The relative standard deviation for each solvent was determined to be no greater than 1.6%. The method limit of detection (LOD) and limit of quantification (LOQ) were between 0.053 and 0.163 and 0.000 (5.791 × 10-6) and 0.520 mg/mL, respectively. This GC technique, using flame ionization detection (FID), was validated and is currently employed for the routine quality control of all approved IND and RDRC PET radiopharmaceuticals at our center.

Neuroimaging Techniques in Differentiating Parkinson's Disease from Drug-Induced Parkinsonism: A Comprehensive Review

Neuroimaging can provide significant benefits in evaluating patients with movement disorders associated with drugs. This literature review describes neuroimaging techniques performed to distinguish Parkinson's disease from drug-induced parkinsonism. The dopaminergic radiotracers already reported to assess patients with drug-induced parkinsonism are [123I]-FP-CIT, [123I]-β-CIT, [99mTc]-TRODAT-1, [18F]-DOPA, [18F]-AV-133, and [18F]-FP-CIT. The most studied one and the one with the highest number of publications is [123I]-FP-CIT. Fludeoxyglucose (18F) revealed a specific pattern that could predict individuals susceptible to developing drug-induced parkinsonism. Another scintigraphy method is [123I]-MIBG cardiac imaging, in which a relationship between abnormal cardiac imaging and normal dopamine transporter imaging was associated with a progression to degenerative disease in individuals with drug-induced parkinsonism. Structural brain magnetic resonance imaging can be used to assess the striatal region. A transcranial ultrasound is a non-invasive method with significant benefits regarding costs and availability. Optic coherence tomography only showed abnormalities in the late phase of Parkinson's disease, so no benefit in distinguishing early-phase Parkinson's disease and drug-induced parkinsonism was found. Most methods demonstrated a high specificity in differentiating degenerative from non-degenerative conditions, but the sensitivity widely varied in the studies. An algorithm was designed based on clinical manifestations, neuroimaging, and drug dose adjustment to assist in the management of patients with drug-induced parkinsonism.

Autofluorescence imaging of endogenous metabolic cofactors in response to cytokine stimulation of classically activated macrophages

BACKGROUND

Macrophages are one of the most prevalent subsets of immune cells within the tumor microenvironment and perform a range of functions depending on the cytokines and chemokines released by surrounding cells and tissues. Recent research has revealed that macrophages can exhibit a spectrum of phenotypes, making them highly plastic due to their ability to alter their physiology in response to environmental cues. Recent advances in examining heterogeneous macrophage populations include optical metabolic imaging, such as fluorescence lifetime imaging (FLIM), and multiphoton microscopy. However, the method of detection for these systems is reliant upon the coenzymes NAD(P)H and FAD, which can be affected by factors other than cytoplasmic metabolic changes. In this study, we seek to validate these optical measures of metabolism by comparing optical results to more standard methods of evaluating cellular metabolism, such as extracellular flux assays and the presence of metabolic intermediates.

METHODS

Here, we used autofluorescence imaging of endogenous metabolic co-factors via multiphoton microscopy and FLIM in conjunction with oxygen consumption rate and extracellular acidification rate through Seahorse extracellular flux assays to detect changes in cellular metabolism in quiescent and classically activated macrophages in response to cytokine stimulation.

RESULTS

Based on our Seahorse XFP flux analysis, M0 and M1 macrophages exhibit comparable trends in oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Autofluorescence imaging of M0 and M1 macrophages was not only able to show acute changes in the optical redox ratio from pre-differentiation (0 hours) to 72 hours post-cytokine differentiation (M0: 0.320 to 0.258 and M1: 0.316 to 0.386), mean NADH lifetime (M0: 1.272 ns to 1.379 ns and M1: 1.265 ns to 1.206 ns), and A1/A2 ratio (M0: 3.452 to ~ 4 and M1: 3.537 to 4.529) but could also detect heterogeneity within each macrophage population.

CONCLUSIONS

Overall, the findings of this study suggest that autofluorescence metabolic imaging could be a reliable technique for longitudinal tracking of immune cell metabolism during activation post-cytokine stimulation.

PET reporter systems for the brain

Positron emission tomography (PET) can be used as a noninvasive method to longitudinally monitor and quantify the expression of proteins in the brain in vivo. It can be used to monitor changes in biomarkers of mental health disorders, and to assess therapeutic interventions such as stem cell and molecular genetic therapies. The utility of PET monitoring depends on the availability of a radiotracer with good central nervous system (CNS) penetration and high selectivity for the target protein. This review evaluates existing methods for the visualization of reporter proteins and/or protein function using PET imaging, focusing on engineered systems, and discusses possible approaches for future success in the development of high-sensitivity and high-specificity PET reporter systems for the brain.

Quantitative Magnetic Resonance Imaging to Assess Progression and Rupture Risk of Aortic Aneurysms: A Scoping Review

PURPOSE

In current practice, the diameter of an aortic aneurysm is utilized to estimate the rupture risk and decide upon timing of elective repair, although it is known to be imprecise and not patient-specific. Quantitative magnetic resonance imaging (MRI) enables the visualization of several biomarkers that provide information about processes within the aneurysm and may therefore facilitate patient-specific risk stratification. We performed a scoping review of the literature on quantitative MRI techniques to assess aortic aneurysm progression and rupture risk, summarized these findings, and identified knowledge gaps.

METHODS

Literature concerning primary research was of interest and the medical databases PubMed, Scopus, Embase, and Cochrane were systematically searched. This study used the PRISMA protocol extension for scoping reviews. Articles published between January 2010 and February 2023 involving animals and/or humans were included. Data were extracted by 2 authors using a predefined charting method.

RESULTS

A total of 1641 articles were identified, of which 21 were included in the scoping review. Quantitative MRI-derived biomarkers were categorized into hemodynamic (8 studies), wall (5 studies) and molecular biomarkers (8 studies). Fifteen studies included patients and/or healthy human subjects. Animal models were investigated in the other 6 studies. A cross-sectional study design was the most common, whereas 5 animal studies had a longitudinal component and 2 studies including patients had a prospective design. A promising hemodynamic biomarker is wall shear stress (WSS), which is estimated based on 4D-flow MRI. Molecular biomarkers enable the assessment of inflammatory and wall deterioration processes. The ADAMTS4-specific molecular magnetic resonance (MR) probe showed potential to predict abdominal aortic aneurysm (AAA) formation and rupture in a murine model. Wall biomarkers assessed using dynamic contrast-enhanced (DCE) MRI showed great potential for assessing AAA progression independent of the maximum diameter.

CONCLUSION

This scoping review provides an overview of quantitative MRI techniques studied and the biomarkers derived from them to assess aortic aneurysm progression and rupture risk. Longitudinal studies are needed to validate the causal relationships between the identified biomarkers and aneurysm growth, rupture, or repair. In the future, quantitative MRI could play an important role in the personalized risk assessment of aortic aneurysm rupture.

CLINICAL IMPACT

The currently used maximum aneurysm diameter fails to accurately assess the multifactorial pathology of an aortic aneurysm and precisely predicts rupture in a patient-specific manner. Quantitative magnetic resonance imaging (MRI) enables the detection of various quantitative parameters involved in aneurysm progression and subsequent rupture. This scoping review provides an overview of the studied quantitative MRI techniques, the biomarkers derived from them, and recommendations for future research needed for the implementation of these biomarkers. Ultimately, quantitative MRI could facilitate personalized risk assessment for patients with aortic aneurysms, thereby reducing untimely repairs and improving rupture prevention.

Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques

Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.

Development and Biodistribution of a Nerve Growth Factor Radioactive Conjugate for PET Imaging

PURPOSE

The purpose of these studies was to develop a nerve growth factor (NGF) radiometal-chelator conjugate to determine the biodistribution and brain uptake of NGF by positron emission tomography/computerized tomography (PET-CT).

PROCEDURES

Purified NGF from llama seminal plasma was conjugated with FITC, and the chelator NOTA or DFO. NGF conjugates were evaluated for bioactivity. NOTA- and DFO-conjugated NGF were radiolabeled with gallium-68 or zirconium-89 ([68 Ga]GaCl3, half-life = 68 min; [89Zr]Zr(oxalate)4, half-life = 3.3 days). [89Zr]Zr-NGF was evaluated for biodistribution (0.5, 1, or 24 h), PET imaging (60 min), and brain autoradiography in mice.

RESULTS

Cell-based in vitro assays confirmed that the NGF conjugates maintained NGF receptor-binding and biological activity. Zirconium-89 and gallium-68 radiolabeling showed a high efficiency; however, only[89Zr]Zr-NGF was stable in vitro. Biodistribution studies showed that, as with most small proteins < 70 kDa, [89Zr]Zr-NGF uptake was predominantly in the kidney and was cleared rapidly with almost complete elimination of NGF at 24 h. Dynamic PET imaging from 0-60 min showed a similar pattern to ex vivo biodistribution with some transient liver uptake. Interestingly, although absolute brain uptake was very low, at 24 h after treatment, cerebral cortex uptake was higher than any other brain area examined and blood.

CONCLUSIONS

We conclude that conjugation of DFO to NGF through a thiourea linkage allows effective radiolabeling with zirconium-89 while maintaining NGF bioactivity. Following intravenous administration, the radiolabeled NGF targets non-neuronal tissues (e.g., kidney, liver), and although absolute brain uptake was very low, the brain uptake that was observed was restricted to the cortex.

Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation

The significance of performing large-depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical throughput of the microscope limits the available information per unit of time, i.e., it is difficult to obtain both high spatial and temporal resolution at once. Here, a method is proposed to construct a kind of intravital microscopy with high optical throughput, by making near-infrared-II (NIR-II, 900-1880 nm) wide-field fluorescence microscopy learn from two-photon fluorescence microscopy based on a scale-recurrent network. Using this upgraded NIR-II fluorescence microscope, vessels in the opaque brain of a rodent are reconstructed three-dimensionally. Five-fold axial and thirteen-fold lateral resolution improvements are achieved without sacrificing temporal resolution and light utilization. Also, tiny cerebral vessel dilatations in early acute respiratory failure mice are observed, with this high optical throughput NIR-II microscope at an imaging speed of 30 fps.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Ferroptosis Detection: From Approaches to Applications

Understanding the intricate molecular machinery that governs ferroptosis and leveraging this accumulating knowledge could facilitate disease prevention, diagnosis, treatment, and prognosis. Emerging approaches for the in situ detection of the major regulators and biological events across cellular, tissue, and in living subjects provide a multiscale perspective for studying ferroptosis. Furthermore, advanced applications that integrate ferroptosis detection and the latest technologies hold tremendous promise in ferroptosis research. In this review, we first briefly summarize the mechanisms and key regulators underlying ferroptosis. Ferroptosis detection approaches are then presented to delineate their design, mechanisms of action, and applications. Special interest is placed on advanced ferroptosis applications that integrate multifunctional platforms. Finally, we discuss the prospects and challenges of ferroptosis detection approaches and applications, with the aim of providing a roadmap for the theranostic development of a broad range of ferroptosis-related diseases.

Fluorescent nano- and microparticles for sensing cellular microenvironment: past, present and future applications

The tumor microenvironment (TME) demonstrates distinct hallmarks, including acidosis, hypoxia, reactive oxygen species (ROS) generation, and altered ion fluxes, which are crucial targets for early cancer biomarker detection, tumor diagnosis, and therapeutic strategies. Various imaging and sensing techniques have been developed and employed in both research and clinical settings to visualize and monitor cellular and TME dynamics. Among these, ratiometric fluorescence-based sensors have emerged as powerful analytical tools, providing precise and sensitive insights into TME and enabling real-time detection and tracking of dynamic changes. In this comprehensive review, we discuss the latest advancements in ratiometric fluorescent probes designed for the optical mapping of pH, oxygen, ROS, ions, and biomarkers within the TME. We elucidate their structural designs and sensing mechanisms as well as their applications in in vitro and in vivo detection. Furthermore, we explore integrated sensing platforms that reveal the spatiotemporal behavior of complex tumor cultures, highlighting the potential of high-resolution imaging techniques combined with computational methods. This review aims to provide a solid foundation for understanding the current state of the art and the future potential of fluorescent nano- and microparticles in the field of cellular microenvironment sensing.

Emulsion Technology in Nuclear Medicine: Targeted Radionuclide Therapies, Radiosensitizers, and Imaging Agents

Radiopharmaceuticals serve as a major part of nuclear medicine contributing to both diagnosis and treatment of several diseases, especially cancers. Currently, most radiopharmaceuticals are based on small molecules with targeting ability. However, some concerns over their stability or non-specific interactions leading to off-target localization are among the major challenges that need to be overcome. Emulsion technology has great potential for the fabrication of carrier systems for radiopharmaceuticals. It can be used to create particles with different compositions, structures, sizes, and surface characteristics from a wide range of generally recognized as safe (GRAS) materials, which allows their functionality to be tuned for specific applications. In particular, it is possible to carry out surface modifications to introduce targeting and stealth properties, as well as to control the particle dimensions to manipulate diffusion and penetration properties. Moreover, emulsion preparation methods are usually simple, economic, robust, and scalable, which makes them suitable for medical applications. In this review, we highlight the potential of emulsion technology in nuclear medicine for developing targeted radionuclide therapies, for use as radiosensitizers, and for application in radiotracer delivery in gamma imaging techniques.

A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images

The human upper airway is comprised of many anatomical volumes. The obstructions in the upper airway volumes are needed to be diagnosed which requires volumetric segmentation. Manual segmentation is time-consuming and requires expertise in the field. Automatic segmentation provides reliable results and also saves time and effort for the expert. The objective of this study is to systematically review the literature to study various techniques used for the automatic segmentation of the human upper airway regions in volumetric images. PRISMA guidelines were followed to conduct the systematic review. Four online databases Scopus, Google Scholar, PubMed, and JURN were used for the searching of the relevant papers. The relevant papers were shortlisted using inclusion and exclusion eligibility criteria. Three review questions were made and explored to find their answers. The best technique among all the literature studies based on the Dice coefficient and precision was identified and justified through the analysis. This systematic review provides insight to the researchers so that they shall be able to overcome the prominent issues in the field identified from the literature. The outcome of the review is based on several parameters, e.g., accuracy, techniques, challenges, datasets, and segmentation of different sub-regions. Flowchart of the search process as per PRISMA guidelines along with inclusion and exclusion criteria.

Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits

The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer's disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely available technique offering high-spatial-resolution imaging. It can be used to visualize amyloid deposits with the help of amyloid-binding diagnostic agents injected into the body. In recent years, a number of amyloid-targeted MRI probes have been developed, but none of them has entered clinical practice. We review the advances in the field and deduce the requirements for the molecular structure and properties of a diagnostic probe candidate. These requirements make up the base for the rational design of MRI-active small molecules targeting amyloid deposits. Particular attention is paid to the novel cryo-EM structures of the fibril aggregates and their complexes, with known binders offering the possibility to use computational structure-based design methods. With continued research and development, MRI probes may revolutionize the diagnosis and treatment of neurodegenerative diseases, ultimately improving the lives of millions of people worldwide.

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction

In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.

Multimodality PET and Near-Infrared Fluorescence Intraoperative Imaging of CEA-Positive Colorectal Cancer

PURPOSE

Molecular imaging is a major diagnostic component for cancer management, enabling detection, staging of disease, targeting therapy, and monitoring the therapeutic response. The coordination of multimodality imaging techniques further enhances tumor localization. The development of a single agent for real-time non-invasive targeted positron emission tomography (PET) imaging and fluorescence guided surgery (FGS) will provide the next generation tool in the surgical management of cancer.

PROCEDURES

The humanized anti-CEA M5A-IR800 "sidewinder" (M5A-IR800-SW) antibody-dye conjugate was designed with a NIR 800 nm dye incorporated into a PEGylated linker and conjugated with the metal chelate p-SCN-Bn-deferoxamine (DFO) for zirconium-89 PET imaging (⁸⁹Zr, half-life 78.4 h). The dual-labeled ⁸⁹Zr-DFO-M5A-SW-IR800 was evaluated for near infrared (NIR) fluorescence imaging, PET/MRI imaging, terminal tissue biodistribution, and blood clearance in a human colorectal cancer LS174T xenograft mouse model.

RESULTS

The ⁸⁹Zr-DFO-M5A-SW-IR800 NIR fluorescence imaging showed high tumor targeting with normal liver uptake. Serial PET/MRI imaging was performed at 24 h, 48 h, and 72 h and showed tumor localization visible at 24 h that persisted throughout the experiment. However, the PET scans showed higher activity for the liver than the tumor, compared to the NIR fluorescence imaging. This difference is an important finding as it quantifies the expected difference due to the sensitivity and depth of penetration between the 2 modalities.

CONCLUSIONS

This study demonstrates the potential of a pegylated anti-CEA M5A-IR800-Sidewinder for NIR fluorescence/PET/MR multimodality imaging for intraoperative fluorescence guided surgery.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

An Fe complex for 19F magnetic resonance-based reversible redox sensing and multicolor imaging

We report a first-in-class responsive, pentafluorosulfanyl (-SF₅)-tagged ¹⁹F MRI agent capable of reversibly detecting reducing environments via an Fe^II/III redox couple. In the Fe^III form, the agent displays no ¹⁹F MR signal due to paramagnetic relaxation enhancement-induced signal broadening; however, upon rapid reduction to Fe^II with one equivalent of cysteine, the agent displays a robust ¹⁹F signal. Successive oxidation and reduction studies validate the reversibility of the agent. The -SF₅ tag in this agent enables 'multicolor imaging' in conjunction with sensors containing alternative fluorinated tags and this was demonstrated via simultaneous monitoring of the ¹⁹F MR signal of this -SF₅ agent and a hypoxia-responsive agent containing a -CF₃ group.

Synthesized Image Reconstruction for Post-Reconstruction Resolution Recovery

Resolution recovery (RR) techniques in positron emission tomography (PET) imaging aim to mitigate spatial resolution losses and related inaccuracies in quantification by using a model of the system's point spread function (PSF) during reconstruction or post-processing. However, including PSF modeling in fully 3-D image reconstruction is far from trivial as access to the scanner-specific forward and back-projectors is required, along with access to the 3-D sinogram data. Hence, post-reconstruction RR methods, such as the Richardson-Lucy (RL) algorithm, can be more practical. However, the RL method leads to relatively rapid noise amplification in early image iterations, giving inferior image quality compared to iterates obtained by placing the PSF model in the reconstruction algorithm. We propose a post-reconstruction RR method by synthesizing PET data by a forward projection of an initial real data reconstruction (such reconstructions are usually available via a scanner's standard reconstruction software). The synthetic PET data are then used to reconstruct an image, but crucially now including a modeled PSF within the system model used during reconstruction. Results from simulations and real data demonstrate the proposed method improves image quality compared to the RL algorithm, whilst avoiding the need for scanner-specific projectors and raw sinogram data (as required by standard PSF modeling within reconstruction).