Single Imaging Research Articles

High speed single pixel imaging using a microLED-on-CMOS light projector.

Achieving high frame-rate operation in single pixel imaging schemes normally demands significant compromises in the flexibility of the imaging system, requiring either complex optical setups or a hardware-limited pattern mask set. Here, we demonstrate a single pixel imaging capability with pattern frame-rates approaching 400 kfps with a recently developed microLED light projector and an otherwise simple optical setup. The microLED array has individually addressable pixels and can operate significantly faster than digital micromirror devices, allowing flexibility with regards to the pattern masks employed for imaging even at the fastest frame-rates. Using a full set of Hadamard or Noiselet patterns, we demonstrate 128 × 128 pixel images being generated at 7.3 fps. We generate a pattern set specifically for the light projector using deep learning tools and use these patterns to demonstrate single pixel imaging at almost 800 fps.

Abstract PO-030: Single cell spatial analysis of large B cell lymphoma identifies biomarkers correlated with genetic subtypes and clincal outcomes

Abstract Single cell and multiplex imaging modalities have opened new insights into the organization and architecture of the immune microenvironment of cancer. Our group was one of the first to demonstrate these methods in the analysis of Hodgkin [1,2] and non-Hodgkin lymphoma [3]. Most recently, we demonstrated that spatial biomarkers outperform gene-expression biomarkers in predicting relapse after autologous transplant [4]. Here we present previously unpublished data demonstrating single cell multiplexed spatial analysis, using the imaging mass cytometry platform, in a cohort of 426 patients with large cell lymphoma. Gene expression and mutational analysis are available for a majority of cases allowing us to correlate spatial signature with genetic classes such as LymphGen [5], and deconvolution methods such as EcoTyper [6]. Using this cohort, we identify spatial biomarkers that improve prediction of clinical outcomes beyond cell of origin or genetic subgroup and identify novel targets for clinical development. Finally, we will present our approaches to move our discovery level spatial profiling into clinical diagnostic space through application of automated multiplex immunofluorescence imaging and a machine learning based image analysis pipeline. [1] Aoki T, et al. Single-Cell Transcriptome Analysis Reveals Disease-Defining T-cell Subsets in the Tumor Microenvironment of Classic Hodgkin Lymphoma. Cancer Discov. 2020 doi: 10.1158/2159-8290.CD-19-0680. [2] Xu, A. et al. Single cell spatial analysis and biomarker discovery in Hodgkin lymphoma. Biorxiv, 2023. https://doi.org/10.1101/2023.05.24.542195. [3] Colombo AR, et al. Single-cell spatial analysis of tumor immune architecture in diffuse large B-cell lymphoma. Blood Adv. 2022 doi: 10.1182/bloodadvances.2022007493. [4] Aoki T, et al. Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma. J Clin Oncol. 2024 doi: 10.1200/JCO.23.01115. [5] Wright GW, et al. A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications. Cancer Cell. 2020 doi: 10.1016/j.ccell.2020.03.015. [6] Steen CB, et al. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell. 2021 doi: 10.1016/j.ccell.2021.08.011. Citation Format: Alexander Xu, Joseph Lownik, Anton Villamejor, Simeon Mahov, Ying Li, Yidan Xu-Monette, Ken Young, Akil Merchant. Single cell spatial analysis of large B cell lymphoma identifies biomarkers correlated with genetic subtypes and clincal outcomes [abstract]. In: Proceedings of the Fourth AACR International Meeting on Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2024 Jun 19-22; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(3_Suppl):Abstract nr PO-030.

Highly sensitive and specific detection of human papillomavirus type 16 using CRISPR/Cas12a assay coupled with an enhanced single nanoparticle dark-field microscopy imaging technique

Human papillomavirus (HPV) is a prevalent sexually transmitted pathogen associated with cervical cancer. Detecting high-risk HPV (hr-HPV) infections is crucial for cervical cancer prevention, particularly in resource-limited settings. Here, we present a highly sensitive and specific sensor for HPV-16 detection based on CRISPR/Cas12a coupled with enhanced single nanoparticle dark-field microscopy (DFM) imaging techniques. Ag–Au satellites were assembled through the hybridization of AgNPs-based spherical nucleic acid (Ag-SNA) and AuNPs-based spherical nucleic acid (Au-SNA), and their disassembly upon target-mediated cleavage by the Cas12a protein was monitored using DFM for HPV-16 quantification. To enhance the cleavage efficiency and detection sensitivity, the composition of the ssDNA sequences on Ag-SNA and Au-SNA was optimized. Additionally, we explored using the SynSed technique (synergistic sedimentation of Brownian motion suppression and dehydration transfer) as an alternative particle transfer method in DFM imaging to traditional electrostatic deposition. This addresses the issue of inconsistent deposition efficiency of Ag–Au satellites and their disassembly due to their size and charge differences. The sensor achieved a remarkable limit of detection (LOD) of 10 fM, lowered by 9-fold compared to traditional electrostatic deposition methods. Clinical testing in DNA extractions from 10 human cervical swabs demonstrated significant response differences between the positive and negative samples. Our sensor offers a promising solution for sensitive and specific HPV-16 detection, with implications for cancer screening and management.

The Predictive Role of Early Postoperative Magnetic Resonance Imaging After Endoscopic Third Ventriculostomy

ObjectiveEndoscopic third ventriculostomy (ETV) is a treatment option for obstructive hydrocephalus; reported success rates vary. We investigated immediate postoperative magnetic resonance imaging (MRI) to evaluate the role of imaging parameters associated with outcomes. MethodsRetrospective chart review was performed patients undergoing initial ETV between 2005 -2019. Patients with pre- and postoperative MRI with follow-up >one year were included. The following were noted: changes in subarachnoid CSF, third ventricle diameter (TV), bowing of the TV floor, and postoperative flow void. Kaplan-Meier survival methods were used to assess ETV success, Univariable and multivariable Cox proportional-hazards models were fitted to assess factors contributing to ETV success. ResultsFifty-eight subjects were included. Nineteen (32.8%) experienced failure within one year, individually, no single imaging parameter predicted success. However, all cases with failure had no identifiable flow void. Any postoperative radiological change was not consistently associated with decreased odds of failure. Obstructive hydrocephalus treated with ETV demonstrated significantly better ETV success than patients treated for non-obstructive hydrocephalus Inter-observer reliability was moderate for two of the radiological variables and substantial for one of the radiological variables. ConclusionsIndividually none of the qualitative radiologic parameters measured in our study predicted ETV success. Absence of a flow void predicted ETV failure, but additional studies are needed to determine its true negative predictive value. Inability to clarify which specific parameter predicts success, reflects the limited role of immediate postoperative imaging in influencing clinical management.

Isotropic and anisotropic edge detection based on Fourier single pixel imaging

In this paper, we propose a novel edge detection scheme based on Fourier single pixel imaging, capable of realizing both isotropic and anisotropic edge detection. In the scheme, we use a series of specific designed sinusoidal speckle patterns to illuminate the object. The corresponding total intensity of reflective light is then measured by a bucket detector. By performing the inverse Fourier transformation on the bucket detection results, an isotropic or anisotropic edge image of the object could be reconstructed directly. Simulation and experimental results demonstrate that our scheme can capture the isotropic or anisotropic edge images of the object with both direct and indirect line of sight to the object. Our scheme offers the advantages of achieving a clear and high-quality isotropic or anisotropic edge image with only one imaging reconstruction computing operation from sub-Nyquist measurements, which significantly reduces the number of measurements required for single pixel imaging. In addition, our scheme provides the benefits of achieving the single pixel anisotropic edge imaging, allowing one to decide the edges in an arbitrary direction to disappear while preserving the edges in other directions.

Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.

The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis. An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict "flipping" cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h. An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol's time length without compromising diagnostic power.

Diagnostic Sensitivity and Symptomatic Relevance of Dopamine Transporter Imaging and Myocardial Sympathetic Scintigraphy in Patients with Dementia with Lewy Bodies.

Dementia with Lewy bodies (DLB) presents with various symptoms, posing challenges for early diagnosis challenging. Dopamine transporter (123I-FP-CIT) single-photon emission tomography (SPECT) and 123I-meta-iodobenzylguanidine (123I-MIBG) imaging are crucial diagnostic biomarkers. Hypothesis about body- and brain-first subtypes of DLB indicate that some DLB may show normal 123I-FP-CIT or 123I-MIBG results; but the characteristic expression of these two subtypes remains unclear. This study aimed to evaluate the diagnostic sensitivity of 123I-FP-CIT and 123I-MIBG imaging alone, combined in patients with DLB and explore symptoms associated with the abnormal imaging results. Demographic data, clinical status, and imaging results were retrospectively collected from patients diagnosed with possible DLB. Both images were quantified using semi-automated software, and the sensitivity of each imaging modality and their combination was calculated. Demographic data, cognition, and motor and non-motor symptoms were compared among the subgroups based on the imaging results. Symptoms related to each imaging abnormality were examined using binomial logistic regression analyses. Among 114 patients with DLB, 80 underwent 123I-FP-CIT SPECT (sensitivity: 80.3%), 83 underwent 123I-MIBG imaging (68.2%), and 66 both (sensitivity of either abnormal result: 93.9%). Visual hallucinations differed among the four subgroups based on imaging results. Additionally, nocturia and orthostatic hypotension differed between abnormal and normal 123I-MIBG images. Overall, 123I-FP-CIT SPECT was slightly higher sensitivity than 123I-MIBG imaging, with combined imaging increasing diagnostic sensitivity. Normal results of a single imaging test may not refute DLB. Autonomic symptoms may lead to abnormal 123I-MIBG scintigraphy findings indicating body-first subtype of patients with DLB.

Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis.

Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is suggested to represent atypical developmental or degenerative changes accompanying an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate into volume loss is crucial. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Of the CHR individuals, 33 developed psychosis (CHR-P), while 127 did not (CHR-NP). Among all participants, longitudinal data was available for 45 HCs, 17 CHR-P, and 66 CHR-NP. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the CHR-P from the CHR-NP. In addition, for completeness, we also investigated changes in cortical thickness and in white matter (WM) microstructure. At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in many brain areas, the CHR-P group demonstrated significantly accelerated changes (iFW increase and volume reduction) with time than the CHR-NP group. Cortical thickness provided similar results as volume, and there were no significant changes in WM microstructure. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes or microstructural WM changes, and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes, as reflected by the increased iFW, are thus an early pathology at the prodromal stage of psychosis that may be useful for a better mechanistic understanding of psychosis development.

Neuroimaging for Early Diagnosis of Alzheimer's Disease: a Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and back-ground of this study. PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology.

Sequence building block for magnetic resonance spectroscopy on Siemens VE-series scanners.

We present a sequence building block (SBB) that embeds magnetic resonance spectroscopy (MRS) into another sequence on the Siemens VE platform without any custom hardware. This enables dynamic studies such as functional MRS (fMRS), dynamic shimming and frequency correction, and acquisition of navigator images for motion correction. The SBB supports nonlocalised spectroscopy (free induction decay), STimulated Echo Acquisition Mode single voxel spectroscopy, and 1D, 2D and 3D phase-encoded chemical shift imaging. It can embed 1H or X-nuclear MRS into a 1H sequence; and 1H-MRS into an X-nuclear sequence. We demonstrate integration into the vendor's gradient-recalled echo sequence. We acquire test data in phantoms with three coils (31P/1H, 13C/1H and 2H/1H) and in two volunteers on a 7-T Terra MRI scanner. Fifteen lines of code are required to insert the SBB into a sequence. Spectra and images are acquired successfully in all cases in phantoms, and in human abdomen and calf muscle. Phantom comparison of signal-to-noise ratio and linewidth showed that the SBB has negligible effects on image and spectral quality, except that it sometimes produces a nuclear Overhauser effect (NOE) signal enhancement for multinuclear applications in line with conventional 1H NOE pulses. Our new SBB embeds MRS into a host imaging or spectroscopy sequence in 15 lines of code. It allows homonuclear and heteronuclear interleaving. The package is available through the standard C2P procedure. We hope this will lower the barrier for entry to studies applying dynamic fMRS and for online motion correction and B0-shim updating.

Spatially resolved lipidomics shows conditional transfer of lipids produced by Bacteroides thetaiotaomicron into the mouse gut

The extent to which bacterial lipids produced by the gut microbiota penetrate host tissues is unclear. Here, we combined mass spectrometry approaches to identify lipids produced by the human gut symbiont Bacteroides thetaiotaomicron (B.theta) and spatially track these bacterial lipids in the mouse colon. We characterize 130 B.theta lipids by liquid chromatography-tandem mass spectrometry (LC-MS/MS), using wild-type and mutant B.theta strains to confidently identify lipid structures and their interconnected pathways invitro. Of these, 103 B.theta lipids can be detected and spatially mapped in a single MALDI mass spectrometry imaging run. We map unlabeled bacterial lipids across colon sections of germ-free and specific-pathogen-free (SPF) mice and mice mono-colonized with wild-type or sphingolipid-deficient (BTMUT) B.theta. We observe co-localization of bacterially derived phosphatidic acid with host tissues in BTMUT mice, consistent with lipid penetration into host tissues. These results indicate limited and selective transfer of bacterial lipids to the host.

NMDA receptor autoantibodies primarily impair the extrasynaptic compartment.

Autoantibodies directed against the N-methyl-D-aspartate receptor (NMDAR-Ab) are pathogenic immunoglobulins detected in patients suffering from NMDAR encephalitis. NMDAR-Ab alter the receptor membrane trafficking, synaptic transmission and neuronal network properties, leading to neurological and psychiatric symptoms in patients. Patients often have very little neuronal damage but rapid and massive (treatment-responsive) brain dysfunctions related to an unknown early mechanism of NMDAR-Ab. Our understanding of this early molecular cascade remains surprisingly fragmented. Here, we used a combination of single molecule-based imaging of membrane proteins to unveil the spatiotemporal action of NMDAR-Ab on live hippocampal neurons. We first demonstrate that different clones of NMDAR-Ab primarily affect extrasynaptic (and not synaptic) NMDARs. In the first minutes, NMDAR-Ab increase extrasynaptic NMDAR membrane dynamics, declustering its surface interactome. NMDAR-Ab also rapidly reshuffle all membrane proteins located in the extrasynaptic compartment. Consistent with this alteration of multiple proteins, effects of NMDAR-Ab were not mediated through the sole interaction between the NMDAR and EphB2 receptor. In the long term, NMDAR-Ab reduce the NMDAR synaptic pool by slowing down receptor membrane dynamics in a cross-linking-independent manner. Remarkably, exposing only extrasynaptic NMDARs to NMDAR-Ab was sufficient to produce their full-blown effect on synaptic receptors. Collectively, we demonstrate that NMDAR-Ab initially impair extrasynaptic proteins, then the synaptic ones. These data thus shed new and unsuspected light on the mode of action of NMDAR-Ab and, probably, our understanding of (extra)synaptopathies.

Correlative single molecule lattice light sheet imaging reveals the dynamic relationship between nucleosomes and the local chromatin environment

In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we present an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrate that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacts nucleosome diffusive properties in a manner that is dependent both on local chromatin density and on relative location within the nucleus. Our results support a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Additionally, they reveal that nuclear heterogeneity arises from both active and passive processes and highlight the need to account for different organizational principles when modeling different chromatin environments.

Hippocampal gray matter volume in young adulthood varies with adolescent alcohol use.

Adolescent substance use is linked with negative future outcomes (e.g., depression, anxiety, substance use disorder). Given that the brain undergoes significant maturation during adolescence, this developmental period may represent a time of particular vulnerability to substance use. Neuroimaging research has largely focused on heavy or binge patterns of substance use; thus, relatively less is known about the neural impact of a broader range of adolescent substance use. Characterizing the neural impact of a broader range of adolescent substance use may inform prevention and treatment efforts. The present study investigated relationships between adolescent substance use trajectories (i.e., alcohol, tobacco, and cannabis) and gray matter volume in young adulthood. Substance use was assessed in 1,594 participants at ages 11, 13, 16, and 19. Following the last assessment, 320 participants completed a single magnetic resonance imaging session to assess brain gray matter volume. Latent growth curve models were used to estimate growth parameters characterizing alcohol, tobacco, and cannabis use trajectories for each participant. These growth parameters (i.e., intercept, linear slope, and quadratic slope) were then used as predictors of gray matter volume. The gray matter volume of the hippocampus was positively associated with age 14 alcohol use (i.e., intercept) but not other trajectories (i.e., progression or acceleration) or substances (tobacco or cannabis). These results provide new insight into the neural impact of distinct adolescent alcohol, tobacco, and cannabis use trajectories, which may help to refine prevention and treatment efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Direct investigations of interactions between nucleolins and aptamers on pancreatic cancer and normal cells by atomic force microscopy

Nucleolin is overexpressed on the surface of pancreatic cancer cells and are regarded as the remarkable therapeutic target. Aptamers are capable of binding the external domain of nucleolin on the cell surface with high affinity and specificity. But nucleolin has not been localized on pancreatic cancer cells at very high spatial resolution, and the interactions between nucleolin and aptamers have not been investigated at very high force resolution level. In this work, nucleolin was localized on pancreatic cancer and normal cells by aptamers (9FU-AS1411-NH2, AS1411-NH2 and CRONH2) in Single Molecule Recognition Imaging mode of Atomic Force Microscopy. There are plenty of nucleolin on the surfaces of pancreatic cancer cells (area percentage about 5 %), while there are little nucleolin on the surfaces of normal cells. The interactions between three types of aptamers and nucleolins on the surfaces of pancreatic cancer cells were investigated by Single Molecule Force Spectroscopy. The unbinding forces of nucleolins-(9FU-AS1411-NH2) are larger than nucleolins-(AS1411-NH2). The dissociation activation energy on nucleolin-(9FU-AS1411-NH2) is higher than nucleolin-(AS1411-NH2), which indicates that the former complex is more stable and harder to dissociate than the later complex. There are no unbinding forces between nucleolin and CRONH2. All these demonstrate that nucleolin was localized on pancreatic cancer and normal cells at single molecule level quantitatively, and the interactions (unbinding forces and kinetics) between nucleolin and aptamers were studied at picoNewton level. The approaches and results of this work will pave new ways in the investigations of nucleolin and aptamers, and will also be useful in the studies on other proteins and their corresponding aptamers.

Single shot depth-resolved imaging through dynamic turbid media

Guide star assisted wavefront shaping techniques have been exploited for focusing and imaging through turbid media by addressing a scatter inverse pattern. However, the turbid medium is required to be steady before finding the proper correction pattern, which limits applications in focusing and imaging through dynamic media, such as turbid water or blood. This study proposes a holography-based dual-polarization computational wavefront shaping method for imaging objects at variant depths behind dynamic turbid media. The orthogonal polarized output speckles of a point source (considered as a guide star) and an object are simultaneously recorded in holograms in separate regions of a single CCD camera. The holograms of the point source and object are subjected to the same distortion regardless of whether the media is static or dynamic. The hologram of the point source is used to determine the correction phase pattern for the distortion, while that of the object is used to record the complex scattered wavefront of the object. To reconstruct a clear object image, the wavefront of the scattered object is digitally corrected using the correction phase pattern and is then transferred to the image plane by calculating the transmission of the angular spectrum. Benefiting from the autofocusing feature of digital holography, objects at different depths can be recovered from a single shot hologram pair. The potential applications of the proposed method in diverse dynamic scattering media are demonstrated by imaging through a moving diffuser, turbid water, and pig blood with optical depth beyond 10.

MolDStruct: Modeling the dynamics and structure of matter exposed to ultrafast x-ray lasers with hybrid collisional-radiative/molecular dynamics.

We describe a method to compute photon-matter interaction and atomic dynamics with x-ray lasers using a hybrid code based on classical molecular dynamics and collisional-radiative calculations. The forces between the atoms are dynamically determined based on changes to their electronic occupations and the formation of a free electron cloud created from the irradiation of photons in the x-ray spectrum. The rapid transition from neutral solid matter to dense plasma phase allows the use of screened potentials, reducing the number of non-bonded interactions. In combination with parallelization through domain decomposition, the hybrid code handles large-scale molecular dynamics and ionization. This method is applicable for large enough samples (solids, liquids, proteins, viruses, atomic clusters, and crystals) that, when exposed to an x-ray laser pulse, turn into a plasma in the first few femtoseconds of the interaction. We present four examples demonstrating the applicability of the method. We investigate the non-thermal heating and scattering of bulk water and damage-induced dynamics of a protein crystal using an x-ray pump-probe scheme. In both cases, we compare to the experimental data. For single particle imaging, we simulate the ultrafast dynamics of a methane cluster exposed to a femtosecond x-ray laser. In the context of coherent diffractive imaging, we study the fragmentation as given by an x-ray pump-probe setup to understand the evolution of radiation damage in the time range of hundreds of femtoseconds.

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

Computational study of diffraction image formation from XFEL irradiated single ribosome molecule

Single particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation.

Assessing Response to PSMA Radiopharmaceutical Therapies with Single SPECT Imaging at 24 Hours After Injection.

Understanding the relationship between lesion-absorbed dose and tumor response in 177Lu-PSMA-617 radiopharmaceutical therapies (RPTs) remains complex. We aimed to investigate whether baseline lesion-absorbed dose can predict lesion-based responses and to explore the connection between lesion-absorbed dose and prostate-specific antigen (PSA) response. Methods: In this retrospective study, we evaluated 50 patients with 335 index lesions undergoing 177Lu-PSMA-617 RPT, who had dosimetry analysis performed on SPECT/CT at 24 h after cycles 1 and 2. First, we identified the index lesions for each patient and measured the lesion-based absorbed doses. Lesion-based response was calculated after cycle 2. Additionally, PSA50 response (a decline of 50% from baseline PSA) after cycle 2 was also calculated. The respective responses for mean and maximum absorbed doses and prostate-specific membrane antigen (PSMA) volumetric intensity product (VIP-PSMA) at cycles 1 and 2 were termed SPECTmean, SPECTmaximum, and SPECTVIP-PSMA, respectively. Results: Of the 50 patients reviewed, 46% achieved a PSA50 response after cycle 2. Of the 335 index lesions, 58% were osseous, 32% were lymph nodes, and 10% were soft-tissue metastatic lesions. The SPECT lesion-based responses were higher in PSA responders than in nonresponders (SPECTmean response of 46.8% ± 26.1% vs. 26.2% ± 24.5%, P = 0.007; SPECTmaximum response of 45% ± 25.1% vs. 19% ± 27.0%, P = 0.001; SPECTVIP-PSMA response of 49.2% ± 30.3% vs. 14% ± 34.7%, P = 0.0005). An association was observed between PSA response and SPECTVIP-PSMA response (R 2 = 0.40 and P < 0.0001). A limited relationship was found between baseline absorbed dose measured with a 24-h single time point and SPECT lesion-based response (R 2 = 0.05, P = 0.001, and R 2 = 0.03, P = 0.007, for mean and maximum absorbed doses, respectively). Conclusion: In this retrospective study, quantitative lesion-based response correlated with patient-level PSA response. We observed a limited relationship between baseline absorbed dose and lesion-based responses. Most of the variance in response remains unexplained solely by baseline absorbed dose. Establishment of a dose-response relationship in RPT with a single time point at 24 h presented some limitations.