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PURPOSE: This study aims to explore the feasibility and performance of three-dimensional ultrasound (3DUS) imaging in ophthalmology using commercially available ultrasound probes adapted to a slit lamp. SIGNIFICANCE: Despite ultrasounds long-standing application in eye care for visualizing ocular components, the evolution of 3DUS technology has remained inactive, with limited development and commercial availability. This study introduces a novel method that could potentially enhance ophthalmic diagnostics and treatment planning by providing comprehensive 3D views of ocular structures using existing ultrasound probes adapted to the conventional slit lamp. METHODS: A custom system was designed for mounting a linear actuator to a slit lamp and enabling the horizontal actuation of any ultrasound probe. Ophthalmic and non-ophthalmic ultrasound probes were tested for their ability to reconstruct 3DUS images of the posterior pole. The study involved designing and evaluating three US phantoms ex vivo and performing in vivo imaging on human subjects to assess the systems applicability. RESULTS: The system successfully acquired 3D volume scans of phantoms and live human eyes, demonstrating the systems potential for detailed ocular imaging. The adaptability of the device allowed for compatibility with various ultrasound probes. In vivo imaging revealed the systems capability to produce high-resolution 3D reconstructions of ocular structures, including eyes with pathological conditions. CONCLUSIONS: The introduction of a slit lamp-mounted 3DUS system represents a significant advancement in ophthalmic ultrasonography, offering a practical and accessible solution for comprehensive 3D ocular assessments. The devices modularity and compatibility with existing ultrasound probes make it a versatile tool for a wide range of ophthalmic applications. Further research and clinical trials are needed to optimize the systems performance and validate its clinical utility.

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

PurposeThe semisolid myelin sheath has very fast transverse relaxation and is invisible to conventional MRI sequences. UTE sequences can detect signal from myelin. The major challenge is the concurrent detection of various water components.MethodsThe inversion recovery (IR)-based UTE (IR-UTE) sequence employs an adiabatic inversion pulse to invert and suppress water magnetizations. TI plays a key role in water suppression, with negative water magnetizations (negative phase) before the null point and positive water magnetizations (positive phase) after the null point. A series of dual-echo IR-UTE images were acquired with different TIs to detect water phase transition. The effects of TR in phase transition and water suppression were also investigated using a relatively long TR of 500 ms and a short TR of 106 ms. The water phase transition in dual-echo IR-UTE imaging of myelin was investigated in five ex vivo and five in vivo human brains.ResultsAn apparent phase transition was observed in the second echo at the water signal null point, where the myelin signal was selectively detected by the UTE data acquisition at the optimal TI. The water phase transition point varied significantly across the brain when the long TR of 500 ms was used, whereas the convergence of TIs was observed when the short TR of 106 ms was used.ConclusionThe results suggest that the IR-UTE sequence with a short TR allows uniform inversion and nulling of water magnetizations, thereby providing volumetric imaging of myelin.

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

PURPOSE: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset. METHODS: The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p

BackgroundRadiation delivery with ultra-high dose rate (FLASH) radiotherapy (RT) holds promise for improving treatment outcomes and reducing side effects but poses challenges in radiation delivery accuracy due to its ultra-high dose rates. This necessitates the development of novel imaging and verification technologies tailored to these conditions.PurposeOur study explores the effectiveness of proton-induced acoustic imaging (PAI) in tracking the Bragg peak in three dimensions and in real time during FLASH proton irradiations, offering a method for volumetric beam imaging at both conventional and FLASH dose rates.MethodsWe developed a three-dimensional (3D) PAI technique using a 256-element ultrasound detector array for FLASH dose rate proton beams. In the study, we tested protoacoustic signal with a beamline of a FLASH-capable synchrocyclotron, setting the distal 90% of the Bragg peak around 35 mm away from the ultrasound array. This configuration allowed us to assess various total proton radiation doses, maintaining a consistent beam output of 21 pC/pulse. We also explored a spectrum of dose rates, from 15 Gy/s up to a FLASH rate of 48 Gy/s, by administering a set number of pulses. Furthermore, we implemented a three-dot scanning beam approach to observe the distinct movements of individual Bragg peaks using PAI. All these procedures utilized a proton beam energy of 180 MeV to achieve the maximum possible dose rate.ResultsOur findings indicate a strong linear relationship between protoacoustic signal amplitudes and delivered doses (R2 = 0.9997), with a consistent fit across different dose rates. The technique successfully provided 3D renderings of Bragg peaks at FLASH rates, validated through absolute Gamma index values.ConclusionsThe protoacoustic system demonstrates effectiveness in 3D visualization and tracking of the Bragg peak during FLASH proton therapy, representing a notable advancement in proton therapy quality assurance. This method promises enhancements in protoacoustic image guidance and real-time dosimetry, paving the way for more accurate and effective treatments in ultra-high dose rate therapy environments.

PURPOSE: There is variability in clinical outcomes with vertebral body tethering (VBT) partly due to a limited understanding of the growth modulation (GM) response. We used the largest sample of patients with 3D spine reconstructions to characterize the vertebra and disc morphologic changes that accompany growth modulation during the first two years following VBT. METHODS: A multicenter registry was used to identify idiopathic scoliosis patients who underwent VBT with 2 years of follow-up. Calibrated biplanar X-rays obtained at longitudinal timepoints underwent 3D reconstruction to obtain precision morphological measurements. GM was defined as change in instrumented coronal angulation from post-op to 2-years. RESULTS: Fifty patients (mean age: 12.5 ± 1.3yrs) were analyzed over a mean of 27.7 months. GM was positively correlated with concave vertebra height growth (r = 0.57, p  10°) experienced an additional 1.6 mm (229% increase) of mean concave vertebra growth during study period compared to the Poor Modulators (GM

Fast, nondestructive three-dimensional (3D) imaging of live suspension cells remains challenging without substrate treatment or fixation, precluding scalable single-cell morphometry with minimal alterations. While optical sectioning techniques achieve 3D live cell imaging, lateral versus depth resolution differences further complicate analysis. We present a scalable microfluidic method capable of 3D fluorescent isotropic imaging of live, nonadherent cells suspended inside picoliter droplets with high-speed single-cell volumetric readout (800 to 1,200 slices in 5 to 8 s) and near-diffraction limit resolution (~216 nm). The platform features a droplet trap array that leverages flow-induced droplet interfacial shear to generate intradroplet microvortices, which rotate single cells on their axis to enable optical projection tomography (OPT)-based imaging. This allows gentle (~1 mPa shear stress) observation of cells encapsulated inside nontoxic isotonic buffer droplets, facilitating scalable OPT acquisition by simultaneous spinning of hundreds of cells. We demonstrate 3D imaging of live myeloid and lymphoid cells in suspension, including K562 cells, as well as naive and activated T cells-small cells prone to movement in their suspended phenotype. Our fully suspended, orientation-independent cell morphometry, driven by isotropic imaging and spherical harmonic analysis, enabled the study of primary T cells across various immunological activation states. This approach unveiled six distinct nuclear content distributions, contrasting with conventional 2D images that typically portray spheroid and bean-like nuclear shapes associated with lymphocytes. Our arrayed-droplet OPT technology is capable of isotropic, single live-cell 3D imaging, with the potential to perform large-scale morphometry of immune cell effector function states while providing compatibility with microfluidic droplet operations.

BackgroundProtoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individual patients.PurposeIn this study, we developed a patient-specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individual patients.MethodsOur method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre-trained group model using patient-specific dataset derived from a novel data augmentation method to tune it into a patient-specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t-test with the p-value threshold of 0.05 compared with the results from the group model.ResultsThe patient-specific model achieved an average RMSE of 0.014 ( p

The severe mismatch between solid bioelectronics and dynamic biological tissues has posed enduring challenges in the biomonitoring community. Here, we developed a reconfigurable liquid cardiac sensor capable of adapting to dynamic biological tissues, facilitating ambulatory cardiac monitoring unhindered by motion artifacts or interference from other biological activities. We employed an ultrahigh-resolution 3D scanning technique to capture tomographic images of the skin on the wrist. Then, we established a theoretical model to gain a deep understanding of the intricate interaction between our reconfigurable sensor and dynamic biological tissues. To properly elucidate the advantages of this sensor, we conducted cardiac monitoring alongside benchmarks such as the electrocardiogram. The liquid cardiac sensor was demonstrated to produce stable signals of high quality (23.1 dB) in ambulatory settings.

Three-dimensional (3D) printing promises a revolution in laboratory creativity by enabling rapid prototyping, broader availability of scientific apparatuses, and transformative scientific workflows. We believe all chemistry and biology laboratories should equip themselves with one or more 3D printers and a critical mass of scientists trained to operate them. This overview surveys the techniques, intricacies, and pitfalls associated with 3D printing of functional parts, including measurements, computer-aided design, slicing, limitations of 3D printing, troubleshooting, tips for tricky filaments, and 3D printer maintenance. A flow cells are essential tools in chemistry and biology laboratories, we discuss techniques relevant to the construction of watertight 3D-printed parts. Finally, we articulate a set of principles required for reporting 3D-printed innovations to improve the field's reproducibility and encourage iterative improvements by other scientists. Ideally, authors, peer reviewers, and editors will adopt these principles. We hope these protocols inspire a new generation of publications applying 3D printing in chemistry and biology-especially highly reproducible inventions with the requisite detail and associated documentation. Such reports will facilitate broad adoption and creative iteration of the most innovative designs, thus accelerating discovery in chemistry and biology. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.

BACKGROUND: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. METHODS: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. RESULTS: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. LIMITATIONS: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. CONCLUSIONS: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) to enhance clinical decision-making in Peyronies disease (PD) has generated significant interest. This review explores the current landscape of AI in PD evaluation. RECENT FINDINGS: Recent advances in 3D modeling offer a more sophisticated approach to assessing PD deformities; however, the implementation of 3D modeling in clinical practice faces challenges, including the need for specialized equipment and time-consuming data processing, sometimes taking several hours of labor. AI holds promise for overcoming these hurdles through its ability to efficiently process large volumes of data and to perform accurate predictions based on such data. Future integration of AI with 3D modeling techniques could revolutionize PD evaluation by improving patient counseling, surgical planning, and clinical decision-making. Significant gaps in the literature have yet to be addressed, including the absence of robust evidence that incorporating such technology is superior to standard diagnostics.

INTRODUCTION: The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). METHODS: In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. RESULTS: In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). CONCLUSIONS: Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

In this study, we describe a comprehensive 3D magnetic resonance imaging (MRI) protocol designed to assess major tissue and fluid components in the brain. The protocol comprises four different sequences: 1) magnetization transfer prepared Cones (MT-Cones) for two-pool MT modeling to quantify macromolecular content; 2) short-TR adiabatic inversion-recovery prepared Cones (STAIR-Cones) for myelin water imaging; 3) proton-density weighted Cones (PDw-Cones) for total water imaging; and 4) highly T2 weighted Cones (T2w-Cones) for free water imaging. By integrating these techniques, we successfully mapped key brain components-namely macromolecules, myelin water, intra/extracellular water, and free water-in ten healthy volunteers and five patients with multiple sclerosis (MS) using a 3T clinical scanner. Brain macromolecular proton fraction (MMPF), myelin water proton fraction (MWPF), intra/extracellular water proton fraction (IEWPF), and free water proton fraction (FWPF) values were generated in white matter (WM), grey matter (GM), and MS lesions. Excellent repeatability of the protocol was demonstrated with high intra-class correlation coefficient (ICC) values. In MS patients, the MMPF and MWPF values of the lesions and normal-appearing WM (NAWM) were significantly lower than those in normal WM (NWM) in healthy volunteers. Moreover, we observed significantly higher FWPF values in MS lesions compared to those in NWM and NAWM regions. This study demonstrates the capability of our technique to volumetrically map major brain components. The technique may have particular value in providing a comprehensive assessment of neuroinflammatory and neurodegenerative diseases of the brain.

OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: ACD 3D U-Net achieved rapid ( 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

The dry mass and the orientation of biomolecules can be imaged without a label by measuring their permittivity tensor (PT), which describes how biomolecules affect the phase and polarization of light. Three-dimensional (3D) imaging of PT has been challenging. We present a label-free computational microscopy technique, PT imaging (PTI), for the 3D measurement of PT. PTI encodes the invisible PT into images using oblique illumination, polarization-sensitive detection and volumetric sampling. PT is decoded from the data with a vectorial imaging model and a multi-channel inverse algorithm, assuming uniaxial symmetry in each voxel. We demonstrate high-resolution imaging of PT of isotropic beads, anisotropic glass targets, mouse brain tissue, infected cells and histology slides. PTI outperforms previous label-free imaging techniques such as vector tomography, ptychography and light-field imaging in resolving the 3D orientation and symmetry of organelles, cells and tissue. We provide open-source software and modular hardware to enable the adoption of the method.

SIGNIFICANCE: Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required. AIM: Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample. APPROACH: We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack. RESULTS: We found that a normalized Dice overlap ( Dice norm ) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the mean Dice norm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼ 5    mm along the nerves length. CONCLUSIONS: Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.