Intensity Imaging Research Articles

Growth dynamics of Rathke's Cleft cyst: a risk score system for surgical decision making

ObjectiveRathke's cleft cysts (RCCs) exhibit variable growth patterns, thus posing a challenge in predicting progression. While some RCCs may not cause symptoms, others can insidiously cause pituitary dysfunction, which is often irreversible, even following surgery. Hence, it is crucial to identify asymptomatic RCCs that grow rapidly and pose a higher risk of causing endocrinologic dysfunction. This enables timely surgical intervention to prevent permanent damage. Our study examines the growth rate of RCCs, identifies factors that accelerate growth, and discusses the clinical implications of these findings.MethodsA retrospective analysis of a prospectively maintained electronic database revealed 45 patients aged 18–80 years who underwent endoscopic endonasal surgery (EEA) for RCCs between 2010 and 2022 at our center. Of these, 20 required early operative intervention. The remaining 25 patients were followed closely clinically and radiologically before requiring surgery (initial conservative management group). We conducted an analysis of the factors predicting growth over time in this group. Using a regression model, we constructed a risk score system to predict RCC growth over time.ResultsPatients in the initial conservative group had smaller cysts and were generally older than those in the early surgery group. Patients with preoperative pituitary dysfunction showed a higher median growth of 1.0 mm in the longest diameter compared to those with normal pituitary function, with an increase of 0.5 mm. A sum of annual cyst growth of all (z, y, x) diameters, at a rate of 3 mm or greater, was associated with a clinically significant increase in the risk of pituitary dysfunction, exceeding 50%.The most significant factors predicting rapid growth in RCCs were smoking status, age, and T1-weighted magnetic resonance imaging (MRI) intensity of cysts. Smoking was the most critical risk factor for rapid cyst growth (p = < .001). Our risk score system accurately predicted RCC growth with a 74% accuracy rate, 73% sensitivity, and 75% specificity.ConclusionOur analysis showed a strong link between active smoking and the rapid growth of RCC. This novel finding has significant preventive implications but needs validation by a large population database. Surgical intervention for RCC currently is often reserved for symptomatic cases. However, utilizing our risk-based scoring system to predict rapidly growing cysts may indicate early surgery in minimally symptomatic patients, thereby potentially preserving pituitary function.

Intensity grading of kidney biopsy direct immunofluorescence IgG image via semantically enhanced feature network

The intensity of direct immunofluorescence IgG imaging for kidney biopsy is an important reference index for the diagnosis of common chronic kidney diseases (CKD) such as membranous nephropathy (MN). Although this index is widely used in diagnosing membranous nephropathy, the intensity levels are currently judged only by the subjective perception of fluorescence luminance in pathological images through physicians’ naked eyes. Therefore, consistent intensity level classification cannot be achieved. To address this issue, this paper proposes a Preprocessing Semantically Enhanced Feature image classification method. The segmentation network is used to segment the original kidney biopsy direct immunofluorescence IgG images to isolate the diseased glomeruli. These segmented glomeruli images are then compared with the original glomeruli images to standardize the kidney biopsy direct immunofluorescence images. Simultaneously, considering the characteristics of small inter-class differences and large intra-class differences in fine-grained images, a semantically enhanced feature image classification model is employed to automatically classify the intensity of the standardized images. The method was evaluated on a constructed real clinical dataset, and the experimental results demonstrated that the proposed method achieved high classification accuracy on this dataset. The source code is made available on https://github.com/SnowRain510/PSEF-NET

In Vivo Lifetime Imaging of the Internal O2 Dynamics in Corals with near-Infrared-Emitting Sensor Nanoparticles.

Mapping of O2 with luminescent sensors within intact animals is challenging due to attenuation of excitation and emission light caused by tissue absorption and scattering as well as interfering background fluorescence. Here we show the application of luminescent O2 sensor nanoparticles (∼50-70 nm) composed of the O2 indicator platinum(II) tetra(4-fluoro)phenyltetrabenzoporphyrin (PtTPTBPF) immobilized in poly(methyl methacrylate-co-methacrylic acid) (PMMA-MA). We injected the sensor nanoparticles into the gastrovascular system of intact colony fractions of reef-building tropical corals that harbor photosynthetic microalgae in their tissues. The sensor nanoparticles are excited by red LED light (617 nm) and emit in the near-infrared (780 nm), which enhances the transmission of excitation and emission light through biological materials. This enabled us to map the internal O2 concentration via time-domain luminescence lifetime imaging through the outer tissue layers across several coral polyps in flowing seawater. After injection, nanoparticles dispersed within the coral tissue for several hours. While luminescence intensity imaging showed some local aggregation of sensor particles, lifetime imaging showed a more homogeneous O2 distribution across a larger area of the coral colony. Local stimulation of symbiont photosynthesis in corals induced oxygenation of illuminated tissue areas and formation of lateral O2 gradients toward surrounding respiring tissues, which were dissipated rapidly after the onset of darkness. Such measurements are key to improving our understanding of how corals regulate their internal chemical microenvironment and metabolic activity, and how they are affected by environmental stress such as ocean warming, acidification, and deoxygenation. Our experimental approach can also be adapted for in vivo O2 imaging in other natural systems such as biofilms, plant and animal tissues, as well as in organoids and other cell constructs, where imaging internal O2 conditions are relevant and challenging due to high optical density and background fluorescence.

Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models.

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically over time metabolic function in 3D engineered brain tissue models comprising human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable development of methods to mitigate lipofuscin's overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and triculture systems, to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.

Signaling dynamics are crucial in biological systems, and biosensor-based real-time imaging has revolutionized their analysis. Fluorescence lifetime imaging microscopy (FLIM) excels over the widely used fluorescence intensity imaging by allowing the measurement of absolute signal levels, independent of sensor concentration. This capability enables the comparison of signaling dynamics across different animals, body regions, and timeframes. However, FLIM's advantage can be compromised by factors like autofluorescence in biological experiments. To address this, we introduce FLiSimBA, a flexible computational framework for realistic F luorescence Li fetime Sim ulation for B iological A pplications. Through simulations, we analyze the signal-to-noise ratios of fluorescence lifetime data, determining measurement uncertainty and providing necessary error bars for lifetime measurements. Furthermore, we challenge the belief that fluorescence lifetime is unaffected by sensor expression and establish quantitative limits to this insensitivity in biological applications. Additionally, we propose innovations, notably multiplexed dynamic imaging that combines fluorescence intensity and lifetime measurements. This innovation can transform the number of signals that can be simultaneously monitored, thereby enabling a systems approach in studying signaling dynamics. Thus, by incorporating diverse factors into our simulation framework, we uncover surprises, identify limitations, and propose advancements for fluorescence lifetime imaging in biology. This quantitative framework supports rigorous experimental design, facilitates accurate data interpretation, and paves the way for technological advancements in fluorescence lifetime imaging.

Adequacy of an In–Neonatal Intensive Care Unit 1T Magnetic Resonance Imaging Compared With 3T Magnetic Resonance Imaging for Clinical Management

BackgroundThis retrospective study aims to assess the added diagnostic utility and clinical value of a 3-Tesla neonatal brain magnetic resonance imaging after obtaining a 1-Tesla magnetic resonance imaging within the neonatal intensive care unit. MethodsA cohort of 34 infants had an initial 1-Tesla magnetic resonance imaging and repeat imaging within 14 days in a 3-Tesla scanner. All infants were admitted to the level III neonatal intensive care unit at Brigham and Women's Hospital, and all images were interpreted by pediatric neuroradiologists. ResultsFor 31 infants (91%), the 3-Tesla magnetic resonance imaging showed similar or expected evolution of known findings found on 1 Tesla. For infants with change between the 1-Tesla and 3-Tesla imaging results, there was no clinical impact. ConclusionImages from 1-Tesla magnetic resonance imaging were sufficient for characterizing a wide range of neonatal brain injuries and abnormalities and repeated 3-Tesla magnetic resonance imaging did not yield further clinical benefit.

Ultra-high spatial resolutions in photopatterning molecular orientations

Accurately aligning liquid crystal molecules into predetermined spatially variant orientations is crucial for fabricating devices such as flat optical elements, soft actuators and robots. Despite the developments of various photopatterning techniques for this purpose, the limits of their spatial resolutions have been rarely addressed. In this study, we delve into the physical constraints governing the spatial resolutions of two prominent photopatterning methods: single exposure to light fields with structured polarizations and multi-exposures to light fields with structured intensities. Theoretical analyses show that the minimal grating period of the first method is only half of the Abbe limit of an intensity imaging system, and that the minimal grating period for the second system can surpass the Rayleigh limit. Experimental studies demonstrate unprecedent high spatial resolution with minimal grating periods of 1 µm. We further establish that the minimal core size in photopatterned singular topological defects is linearly proportional to the minimal grating period and the topological charge and that these photopatterning techniques can yield less than 1 µm defect cores that are in high demand for applications such as coronagraphs.

Electrical Stimulation Promotes Endocytosis of Magnetic Nanoparticles by Cancer Cells.

Nanomaterials are increasingly used in biomedical imaging and cancer therapy, and how to improve the endocytosis of nanomaterials by cells is a key issue. The application of alternating current (AC) electrical stimulation to osteosarcoma cells (MG-63) here can increase the cellular endocytosis of Fe3O4 nanoparticles (diameter: 50nm) by 52.46% via macropinocytosis. This can be ascribed to the decrease in F-actin content and the increase in intracellular Ca2+ concentration. Transmission electron microscope, immunofluorescence staining, western blot, flow cytometry, and inductively coupled plasma emission spectrometer analyses support this interpretation. The application of electrical stimulation decreases the cell viability in magnetic hyperthermia by 47.6% and increases the signal intensity of magnetic resonance imaging by 29%. Similar enhanced endocytosis is observed for breast cancer cells (MCF-7), glioblastoma cells (U-87 MG), melanoma cells (A-375), and bladder cancer cells (TCCSUP), and also for Fe3O4 nanoparticles with the diameters of 20 and 100nm, and Zn0.54Co0.46Cr0.65Fe1.35O4 nanoparticles with the diameter of 70nm. It seems the electrical stimulation has the potential to improve the diagnostic and therapeutic effects of magnetic nanoparticles by promoting endocytosis.

Polarization Characteristics of Massive HVI Debris Clouds Using an Improved Monte Carlo Ray Tracing Method for Remote Sensing Applications

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc.

Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data

Context. In very long baseline interferometry (VLBI), the combination of multiple antennas permits the synthesis of a virtual telescope with a larger diameter and consequently higher resolution than the individual antennas. However, due to the sparse nature of the array, recovering an image from the observed data is a challenging ill-posed inverse problem. Aims. The VLBI community is interested in not only recovering an image in total intensity from interferometric data, but also in obtaining results in the polarimetric and the temporal domain. Only a few algorithms are able to work in all these domains simultaneously. In particular, the algorithms based on optimization that consider various penalty terms specific to static total intensity imaging, time-variability and polarimetry are restricted to grids in the domain of the objective function. In this work we present a novel algorithm, multiobjective particle swarm optimization (MO-PSO), that is able to recover the optimal weights without any space-gridding, and to obtain the marginal contribution of each of the playing terms. Methods. To this end, we utilized multiobjective optimization together with particle swarm metaheuristics. We let the swarm of weights converge to the best position. Results. We evaluate our algorithm with synthetic data sets that are representative for the main science targets and instrumental configuration of the Event Horizon Telescope Collaboration (EHTC) and its planned successors. We successfully recover the polarimetric, static, and time-dynamic signature of the ground truth movie' even with relative sparsity, and a set of realistic data corruptions. Conclusions. We have built a novel, fast, hyperparameter space gridding-free algorithm that successfully recovers static and dynamic polarimetric reconstructions. Compared to regularized maximum likelihood (RML) methods, it avoids the need for parameter surveys, and it is not limited to the number of pixels, unlike recently proposed multiobjective imaging algorithms. Hence, this technique is a novel useful alternative tool to characterize full Stokes time-(in)dependent signatures in a VLBI data set robustly with a minimal set of user-based choices.

Severe scrotal hematoma after hydrocelectomy

Hydrocelectomy is a common and simple urological procedure which may sometimes develop complications. The majority of them are minor, but rarely more significant ones may have an intense clinical image. We present a case of immediate postoperative severe scrotal hematoma after hydrocelectomy that required surgical intervention in a young man.

Improving correlation based super-resolution microscopy images through image fusion by self-supervised deep learning

Super-resolution imaging is a powerful tool in modern biological research, allowing for the optical observation of subcellular structures with great detail. In this paper, we present a deep learning approach for image fusion of intensity and super-resolution optical fluctuation imaging (SOFI) microscopy images. We construct a network that can successfully combine the advantages of these two imaging methods, producing a fused image with a resolution comparable to that of SOFI and an SNR comparable to that of the intensity image. We also demonstrate the effectiveness of our approach experimentally, specifically on cell samples where microtubules were stained with ATTO647N and imaged using a confocal microscope with a single photon fiber bundle camera, allowing for the simultaneous acquisition of an image scanning microscopy (ISM) image and a SOFISM (ISM and SOFI) image. Our network is designed as a self-supervised network and shows the ability to train on a single pair of images and to generalize to other image pairs without the need for additional training. Our approach offers a flexible and efficient way to combine the strengths of correlation based imaging techniques along with traditional intensity based microscopy, and can be readily applied to other fluctuation based imaging modalities.

Research of Pulmonary Fibrosis Lesions Based on FLIM and SHG Imaging Microscopy.

Two-photon fluorescence lifetime microscopy (TP-FLIM) is a powerful quantitative imaging technique that characterizes and analyzes the structure and function of biological samples through a combination of intensity and lifetime imaging. Because TP-FLIM is independent of the fluorescence signal intensity and the fluorophore concentration, it is widely used in high-throughput, high-content drug screening and clinical diagnostics. Second harmonic generation (SHG) imaging technology has the advantages of high spatial resolution and imaging depth inherent to nonlinear optical imaging. Second harmonics often appear in noncentrosymmetric structures. Collagen tissue in biological organisms is a good example of these structures, showing strong harmonic effects. Therefore, SHG has been widely used for imaging of specific tissue structure imaging. TP-FLIM technology is highly sensitive for quantitatively detecting changes in microenvironments. The objective of this study is to examine pathological pulmonary fibrosis slices using a combined approach of TP-FLIM and SHG technology. The fluorescence lifetime data of pulmonary collagen fibers are analyzed by using phasor plot analysis methods, and normal collagen fibers and fibrotic collagen fibers are distinguished by calculating the aspect ratio from the SHG images formed by the collagen fibers. Our study provides a new method for a deeper understanding of the pathological mechanisms and clinical diagnosis of pulmonary fibrosis and other collagen fiber-related disorders.

Fourier analysis in single photon imaging

Single photon imaging has become an established sensing approach. Compared to intensity imaging, versatile advantages have been demonstrated, such as imaging with high sensitivity, at a high frame rate, and with a high dynamic range. In this paper, we investigate the Fourier analysis of single photon counting at a frame rate of approximately 100 kHz and a high spatial resolution of 512 px × 512 px. We observed signal modulation in raw data as well as in data converted to photon flux, but with the data processing, the signal’s frequency response is affected by significant damping. Thus, analysis sensible to signal frequency should work on the raw single photon counting signal. Furthermore, imaging of magnitude and phase in the Fourier domain can visualize areas of certain signal modulation, and the gradient of the phase angle can reveal the direction of movements. Finally, we have applied our method to real world scenarios by analyzing unmanned aerial vehicle’s motion in outdoor experiments.

Surveillance for Distant Metastasis in Breast Cancer Patients Who Underwent Contemporary Management: A Report from the Korean Breast Cancer Society Survivor Research Group

BackgroundCurrent guidelines recommend against the use of routine imaging tests to detect distant metastasis in asymptomatic breast cancer patients. However, recent advancements in effective therapeutics and diagnostic accuracy have raised the need to reassess the clinical efficacy of intensive metastasis surveillance. We report the results of a multicenter retrospective study to investigate the association between intensive imaging studies and survival outcomes.Patients and MethodsWe retrospectively reviewed the data of 4130 patients who underwent surgery from 11 hospitals in Korea between January 2010 and December 2011. Patients were divided into two groups on the basis of the intensity of metastasis imaging studies during their disease-free period. The types and intervals of the imaging studies were based on each physician’s decisions.ResultsHigh-intensive screening showed a shorter distant metastasis-free survival [p < 0.001, hazard ratio (HR) 1.62; 95% confidence interval (CI) 1.29–2.04], especially for patients in whom bone or lung was the first site of metastasis. With a median follow-up period of 110.0 months, the 5-year breast cancer-specific survival (BCSS) rate was 96.5%. The high-intensity screening group showed significantly poorer BCSS compared with the low-intensity screening group (p < 0.001, HR 3.13; 95% CI 2.32–4.21). However, both multivariable analysis and propensity score matching analysis showed no significant association between the screening intensity and BCSS.ConclusionsFrequent imaging studies to detect distant metastasis were associated with earlier detection of distant metastasis, especially for lung and bone metastasis. However, intensive surveillance showed no apparent association with BCSS despite the use of currently available treatments.

Neuroimaging and clinical features of bilateral Wallerian degeneration of middle cerebellar peduncles subsequent to pontine infarction.

Wallerian degeneration (WD) of the middle cerebellar peduncles (MCPs) following pontine infarction is a rare secondary degenerative neurological condition. Due to its infrequency, there is limited research on its characteristics. This study aims to present three cases of WD of MCPs following pontine infarction and to analyze the prognosis, clinical manifestations, and neuroimaging features by amalgamating our cases with previously reported ones. The cohort consisted of 25 cases, comprising 18 men and 7 women aged 29 to 77 years (mean age: 66.2 years). The majority of patients (94%) exhibit risk factors for cerebrovascular disease, with hypertension being the primary risk factor. Magnetic resonance imaging (MRI) can detect WD of MCPs within a range of 21 days to 12 months following pontine infarction. This degeneration is characterized by bilateral symmetric hyperintensities on T2/FLAIR-weighted images (WI) lesions in the MCPs. Moreover, restricted diffusion, with hyperintensity on diffusion-weighted imaging (DWI) and low apparent diffusion coefficient (ADC) signal intensity may be observed as early as 21 days after the infarction. Upon detection of WD, it was observed that 20 patients (80%) remained asymptomatic during subsequent clinic visits, while four (16%) experienced a worsening of pre-existing symptoms. These findings underscore the importance of neurologists enhancing their understanding of this condition by gaining fresh insights into the neuroimaging characteristics, clinical manifestations, and prognosis of individuals with WD of bilateral MCPs.

Integration of Myrica rubra-based N-doped carbon dots with Fe3S4 as excellent peroxidase mimics for colorimetric assay and smartphone-based intelligent sensing of p-aminophenol in waters.

To realize the reutilization of waste Myrica rubra in the analytical field, we synthesized Myrica rubra-based N-doped carbon dots (MN-CDs) and further anchored them onto the surface of Fe3S4 to fabricate Fe3S4@MN-CD nanocomposites. The as-fabricated nanocomposites possessed higher peroxidase-mimetic activity than its two precursors, resulting from the synergistic effect between them, and could catalyze colorless 3,3',5,5'-tetramethylbenzidine (TMB) into deep blue oxTMB with a strong 652-nm absorption. Under optimized conditions (initial solution pH, 3.5; incubation temperature, 35 ℃; Fe3S4@MN-CD concentration, 50µg mL-1, and 652-nm absorption), Fe3S4@MN-CDs were employed for colorimetric assay of p-aminophenol (p-AP) with wide linear range (LR, 2.9-100 µM), low detection limit (LOD, 0.87 µM), and satisfactory recoveries (86.3-105%) in environmental waters. Encouragingly, this colorimetric assay provided the relative accuracy of 97.0-99.4% as compared withconventional HPLC-UV detection. A portable smartphone-based colorimetric application was developed by combining the Fe3S4@MN-CD-based visually chromogenic reaction with a "Thing Identify" APP software. Besides, we engineered an image-capturing device feasible for field use, in which the internal-compact sealing prevented external light source from entering photography chamber, thereby reducing light interference, and also the bottom light source enhanced the intensity of blue imaging. This colorimetric platform exhibited satisfactory LR (1-500 µM), low LOD (0.3 µM), and fortification recoveries (86.6-99.6%). In the chromogenic reaction catalyzed by Fe3S4@MN-CDs, ·O2- played a key role in concomitant with the participation of •OH and h+. Both the colorimetric assay and smartphone-based intelligent sensing show great promising in on-site monitoring of p-AP under field conditions.

Pulmonary hypertension across the spectrum of left heart and lung disease.

Patients with pulmonary hypertension (PH) are grouped based upon clinical and haemodynamic characteristics. Groups 2 (G2, left heart disease [LHD]) and 3 (G3, lung disease or hypoxaemia) are most common. Many patients display overlapping characteristics of heart and lung disease (G2-3), but this group is not well-characterized. Patients with PH enrolled in the prospective, NHLBI-sponsored PVDOMICS network underwent intensive clinical, biomarker, imaging, gas exchange and exercise phenotyping. Patients with pure G2, pure G3, or overlapping G2-3 PH were compared across multiple phenotypic domains. Of all patients with predominant G2 (n = 136), 66 (49%) were deemed to have secondary lung disease/hypoxaemia contributors (G2/3), and of all patients categorized as predominant G3 (n = 172), 41 (24%) were judged to have a component of secondary LHD (G3/2), such that 107 had G2-3 (combined G2/3 and G3/2). As compared with G3, patients with G2 and G2-3 were more obese and had greater prevalence of hypertension, atrial fibrillation, and coronary disease. Patients with G2 and G2-3 were more anaemic, with poorer kidney function, more cardiac dysfunction, and higher N-terminal pro-B-type natriuretic peptide than G3. Lung diffusion was more impaired in G3 and G2-3, but commonly abnormal even in G2. Exercise capacity was severely and similarly impaired across all groups, with no differences in 6-min walk distance or peak oxygen consumption, and pulmonary vasoreactivity to nitric oxide did not differ. In a multivariable Cox regression model, patients with G2 had lower risk of death or transplant compared with G3 (hazard ratio [HR] 0.51, 95% confidence interval [CI] 0.30-0.86), and patients with G2-3 also displayed lower risk compared with G3 (HR 0.57, 95% CI 0.38-0.86). Overlap is common in patients with a pulmonary or cardiac basis for PH. While lung structure/function is clearly more impaired in G3 and G2-3 than G2, pulmonary abnormalities are common in G2, even when clinically judged as isolated LHD. Further study is required to identify optimal systematic evaluations to guide therapeutic innovation for PH associated with combined heart and lung disease. ClinicalTrials.gov NCT02980887.

Improving the Feasibility of Mitigating Phase Errors in 4DCT caused by a Random Reference Breathing Pattern in the Quasar Phantom and the Role of Slice Thickness.

The study aimed to validate a method for minimizing phase errors by combining full-length lung 4DCT (f4DCT) scans with shorter tumor-restricted 4DCT (s4DCT) scans. It assessed the feasibility of integrating two scans one covering the entire phantom length and the other focused on the tumor area. The study also evaluated the impact of Maximum Intensity Projection (MIP) volume and imaging dose for different slice thicknesses (2.5mm and 1.25mm) in both full-length and short target-restricted 4DCT scans. The study utilized the Quasar Programmable Respiratory Motion Phantom, simulating tumor motion with a variable lung insert. The setup included a tumor replica and a six-dot IR reflector marker on the breathing platform. The objective was to analyze volume differences in fMIP_2.5mm compared to sMIP_1.25mm within their respective 4D_MIP CT series. This involved varying breathing periods (2.5s, 3.0s, 4.0s, and 5.0s) and longitudinal tumor sizes (6mm, 8mm, and 10mm). The study also assessed exposure time and expected CTDIvol of s4D_2.5mm and s4D_1.25mm for different breathing periods (5.0s to 2.0s) in the sinusoidal wave motion of the six-dot marker on the breathing platform. Conducting two consecutive 4DCT scans is viable for patients with challenging breathing patterns or when the initial lung tumor scan is in close proximity to the tumor location, eliminating the need for an additional full-length 4DCT. The analysis involves assessing MIP volume, imaging dose (CTDIvol), and exposure time. Longitudinal tumor shifts for 6mm are [16.6-17.2] in fMIP_2.5mm and [16.8-17.5] in sMIP_1.25mm, for 8mm [17.2-18.3] in fMIP_2.5mm and [17.8-18.4] in sMIP_1.25mm, and for 10mm [19-19.9] in fMIP_2.5mm and [19.4-20] in sMIP_1.25mm (p≥ 0.005), respectively. The Quasar Programmable Respiratory Motion Phantom accurately replicated varied breathing patterns and tumor motions. Comprehensive analysis was facilitated through detailed manual segmentation of Internal Target Volumes and Internal Gross Target Volumes.

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Objective. Spinal cord stimulation (SCS) is a well-established treatment for managing certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity to restore lost autonomic or sensory-motor function. Personalized modeling and treatment planning are critical aspects of safe and effective SCS (Rowald and Amft 2022 Front. Neurorobotics 16 983072, Wagner et al 2018 Nature 563 65–71). However, the generation of spine models at the required level of detail and accuracy requires time and labor intensive manual image segmentation by human experts. This study aims to develop a maximally automated segmentation routine capable of producing high-quality anatomical models, even with limited data, to facilitate safe and effective personalized SCS treatment planning. Approach. We developed an automated image segmentation and model generation pipeline based on a novel convolutional neural network (CNN) architecture trained on feline spinal cord magnetic resonance imaging data. The pipeline includes steps for image preprocessing, data augmentation, transfer learning, and cleanup. To assess the relative importance of each step in the pipeline and our choice of CNN architecture, we systematically dropped steps or substituted architectures, quantifying the downstream effects in terms of tissue segmentation quality (Jaccard index and Hausdorff distance) and predicted nerve recruitment (estimated axonal depolarization). Main results. The leave-one-out analysis demonstrated that each pipeline step contributed a small but measurable increment to mean segmentation quality. Surprisingly, minor differences in segmentation accuracy translated to significant deviations (ranging between 4% and 13% for each pipeline step) in predicted nerve recruitment, highlighting the importance of careful workflow design. Additionally, transfer learning techniques enhanced segmentation metric consistency and allowed generalization to a completely different spine region with minimal additional training data. Significance. To our knowledge, this work is the first to assess the downstream impacts of segmentation quality differences on neurostimulation predictions. It highlights the role of each step in the pipeline and paves the way towards fully automated, personalized SCS treatment planning in clinical settings.