Voxel Volume Research Articles

Differences in Cortical Activation During Dorsiflexion and Plantarflexion in Chronic Ankle Instability: A Task-fMRI Study.

Chronic ankle instability is a common sports injury that often presents with increased plantarflexion and restricted dorsiflexion. The cumulative effect of peripheral injuries may induce neuroplasticity in the central nervous system. However, the relationship between dorsiflexion or plantarflexion and the central nervous system in patients with chronic ankle instability remains unknown. (1) Is there a difference in region and voxel (volume pixel) of cortical activation during plantarflexion and dorsiflexion between patients with chronic ankle instability and a control group with normal ankle function? (2) Is there a correlation between activation of sensorimotor-related brain regions and three clinical measurement scales of ankle function and disease severity in patients with chronic ankle instability? Between December 2020 and May 2022, we treated 400 patients who had chronic ankle instability. Ten percent (40 patients; mean ± standard deviation age 29 ± 7 years; 17 male patients) were randomly selected to participate in this study. We recruited 42 volunteers with normal ankle function (mean age 28 ± 5 years; 21 male participants) matched by age and education level. A total of 2.5% (1 of 40) of patients with bilateral chronic ankle instability and 30% (12 of 40) with left-sided chronic ankle injury did not meet our inclusion criteria and were excluded from the study. The control group underwent MRI with good image quality. Finally, 27 patients with chronic ankle instability (mean age 26 ± 5 years; 10 male patients) and 42 participants with normal ankle function were enrolled. Ankle function and disease severity were assessed using three clinical scales: the Cumberland Ankle Instability Tool, Karlsson-Peterson Ankle Function Score, and the American Orthopedic Foot and Ankle Society Score. A uniplanar and nonweightbearing ankle dorsiflexion-plantarflexion paradigm (a recognized model or pattern) was performed using a short-block design during the functional MRI scan. This experimental design included a series of on-off periods consisting of movement and a rest period. From 15° of plantarflexion to 15° of dorsiflexion, the manipulator allowed 30° of ankle rotation. The cerebral excitability patterns between patients with chronic ankle instability and controls were analyzed using t-tests. We retained voxels with p values less than 0.05 in a voxel-level family-wise error correction. Clusters with voxel numbers greater than 10 were retained. The Cohen d coefficient was used to calculate between-group effect sizes. Spearman analysis was performed to explore the correlation between activation regions and the three clinical assessment scales. In the patient group, cortical activation was greater during plantarflexion than during dorsiflexion, which was different from that in the control group. The between-group comparison showed that patients with chronic ankle instability had reduced activation in the ipsilateral precuneus (cluster size = 35 voxels [95% CI -0.23 to 0.07]; p < 0.001) during dorsiflexion, whereas during plantarflexion, chronic ankle instability caused increased activation in the ipsilateral superior temporal gyrus (cluster size = 90 voxels [95% CI -0.73 to -0.13]; p < 0.001), precuneus (cluster size = 18 voxels [95% CI -0.56 to -0.19]; p < 0.001), supplementary motor area (cluster size = 57 voxels [95% CI -0.31 to 0.00]; p < 0.001), superior frontal gyrus (cluster size = 43 voxels [95% CI -0.82 to -0.29]; p < 0.001), medial part of the superior frontal gyrus (cluster size = 39 voxels [95% CI 0.41 to 0.78]; p < 0.001), and contralateral postcentral gyrus (cluster size = 100 voxels [95% CI -0.32 to 0.02]; p < 0.001). Patients with chronic ankle instability showed a large effect size compared with controls (Cohen d > 0.8). During plantarflexion, the number of activated voxels in the supplementary motor area had a modest, positive correlation with the Karlsson-Peterson Ankle Function Score (r = 0.52; p = 0.01), and the number of activated voxels in the primary motor cortex (M1) and primary sensory cortex (S1) had a weak, positive correlation with the American Orthopedic Foot and Ankle Society Score in patients with chronic ankle instability (M1: r = 0.45; p = 0.02, S1: r = 0.49; p = 0.01). Compared with volunteers with normal ankle function, patients with chronic ankle instability had increased cortical activation during plantarflexion and decreased cortical activation during dorsiflexion. We analyzed the central neural mechanisms of chronic ankle instability in patients with sports injuries and provided a theoretical basis for the development of new central and peripheral interventions in the future. Because there was a positive correlation between the neural activity in sensorimotor-related regions during plantarflexion and clinical severity, clinicians might one day be able to help patients who have chronic ankle instability with neuromuscular rehabilitation by applying electrical stimulation to specific targets (such as S1M1 and the supplementary motor area) or by increasing activation of sensorimotor neurons through ankle movement.

High-resolution three-dimensional dosimetry in clinically relevant volumes utilizing optically stimulated luminescence.

The continued development of new radiotherapy techniques requires dosimetry systems that satisfy increasingly rigorous requirements, such as high sensitivity, wide dose range, and high spatial resolution. An emerging requirement is the ability to read out doses in three dimensions (3D) with high precision and spatial resolution. A few dosimetry systems with 3D capabilities are available, but their application in a clinical workflow is limited for various reasons, primarily originating from their chemical nature. The search for a 3D dosimetry system with potential for clinical implementation is thusongoing. To demonstrate the capabilities of a novel optically-stimulated-luminescence (OSL)-based 3D dosimetry system capable of measuring radiation doses in clinically relevantvolumes. A laser-based readout system was used to measure dose distributions delivered by both photons and protons, utilizing the OSL from a mm YSO:Ce crystal. A homogeneous treatment plan consisting of two opposing photon fields was used to establish an inhomogeneity correction map of the crystal response and demonstrated the accuracy and precision of the system. The crystal was additionally irradiated with a photon treatment plan consisting of three overlapping mm fields delivered from different angles, and a proton treatment plan consisting of four pencil beams with energies 90 MeV ( ), 115 MeV, and 140 MeV. The system abilities were quantified by comparing the 3D-resolved measurements to Monte Carlosimulations. The dose map reproducibility of the system was found to be within 2% including both statistical and systematic errors. The measurements yielded integrated doses from a volume of mm with voxel volumes of just mm . An excellent agreement between the 3D-resolved measurements and the simulations was found for both photon- andproton-irradiation. The capabilities of the devised system for measuring clinically relevant fields of photons and proton pencil beams within a clinically relevant volume were demonstrated. The system poses as a promising candidate for clinical applications, and enables future research in the field of OSL-based tissue-equivalent 3Ddosimetry.

Study of γ-Ray Spectrum Measurement Analysis Algorithm of Radioactive Solid Waste Steel Boxes

Abstract In accordance with safety requirements for the near-surface disposal of low and medium-level radioactive solid waste, accurate identification and measurement of radionuclide types and activities within steel boxes containing such waste are necessary. Conventional sampling techniques are impractical due to nonuniform internal filling and radioactive distribution in the steel boxes used for storage. Nondestructive measurement analysis techniques offer significant advantages by providing comprehensive measurements without causing damage to the steel boxes or generating secondary waste. Therefore, nondestructive techniques are widely applicable and superior to chemical analysis methods. This study presents an algorithm for analyzing γ-ray spectrum measurements of radioactive solid waste steel boxes by combining the traditional computed tomography (CT) principle with a discrete treatment approach. The progressive split voxel measurement method is utilized to discretely handle the radioactive solid waste steel boxes during the measurement and calculation process while considering the impact of voxel interactions on detection efficiency. A detection efficiency calibration model is established using Monte Carlo (MC) simulation in conjunction with the algebraic reconstruction technique (ART). Additionally, an inversion correction of the efficiency for the physically coincident portion of the detected voxel volume is performed during the measurement process. The proposed algorithm is initially applied to the analysis platform of steel boxes that store FA-IV-type radioactive solid waste. The algorithm's performance is further investigated and validated using standard radioactive sources with known activity levels. The results indicate that the relative deviation between the reconstruction results and the nominal values of radioactive activities in the steel boxes is less than 30%. These findings align with the expected results and satisfy the measurement analysis and warehousing processing requirements for waste generated from the decommissioning of China's 101 nuclear reactor. The proposed algorithm holds great potential for widespread application in related detection work.

Real Time Volumetric MRI for MR-Guided 3D Motion Tracking via Sparse Prior-Augmented Neural Representation Learning

To reconstruct volumetric MRI from orthogonal cine acquisition aided by sparse priors of 2 static 3D MRI through implicit neural representation (NeRP) learning, with the goal of eliminating large-scale training datasets for data-driven sparse MRI reconstruction and supporting clinical workflow of real time 3D motion tracking during MR-guided radiotherapy. A multi-layer perceptron network was trained to learn the NeRP of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first learning the NeRP of 2 static 3D MRI with different breathing motion states, prior knowledge of patient breathing motion was embedded into network weights through optimization. The prior knowledge was then augmented from 2 to 31 motion states by querying the optimized network at interpolated/extrapolated motion state coordinates. Starting from the prior-augmented network as an initialization point, the network was further trained using sparse samples of 2 orthogonal cine slices. The final volumetric reconstruction was obtained by querying the trained network at desired 3D spatial locations. We evaluated the proposed method using 5-minute volumetric MRI time series with 340 ms temporal resolution collected from 7 liver carcinoma patients. The time series was acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two MRI with inhale and exhale states respectively were selected from the first 30 sec of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to 2 orthogonal slices and compared network-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Across the 7 patients evaluated, the peak signal to noise ratio between model reconstruction and ground truth was 54.66 ± 6.16 dB and the structural similarity index measure was 0.99 ± 0.01. Gross tumor volume (GTV) contours estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 1.89 ± 1.13 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all 3 orthogonal directions. Volumetric MRI from orthogonal cine acquisition with sparse priors is feasible by modeling prior knowledge through implicit neural representation learning. The model-reconstructed images showed sufficient accuracy in supporting 3D motion tracking of abdominal targets. By eliminating the need for large scale training datasets, the method promises to enable clinical implementation of 3D motion tracking for precision radiation therapy.

Dosimetric Analysis Using Automated Tools of Trigeminal Neuralgia Treatment with Stereotactic Radiosurgery

Symptoms from trigeminal neuralgia (TGN) can be significantly debilitating. For treatment of TGN with stereotactic radiosurgery (SRS), there is some data that suggests dose and location of treatment along the trigeminal nerve (TN) pathway may be associated with improved outcomes; however, recurrence rates are widely variable. Limited data exists to evaluate dosimetric parameters of SRS for TGN in relation to patient outcome. This study evaluated the hypothesis that specific characteristics of high dose volume coverage of the TN was associated with outcomes in the treatment of TGN with SRS. Patients who underwent a non-invasive stereotactic radiosurgery instrument for the treatment of TGN at a single institution from 2008-2020 were retrospectively reviewed. Patients were selected for treatment with SRS after consensus from neurosurgery and radiation oncology teams. Patient must have undergone MR FIESTA sequence imaging for treatment planning and had at least 3mo TGN follow up after SRS. Using commercially available software, an automated script was developed to extract detailed characteristics of isodose line structures (IDL) created from the delivered radiation treatment plan. Variables evaluated included voxel volume, mean/max of the HU signal, kurtosis/skewness (a measure of non-spheroidicity) of the IDL, and HU integral of the 140-195% IDL structures, all of which could serve as possible surrogates to the degree and shape of TN pathway dosimetric coverage. Patient reported pain was graded prior and post treatment using Barrow Neurologic Institute pain scale (BNI). Post SRS BNI score I-III was considered significant symptom improvement while BNI score IV-V was considered no benefit. Univariate and multivariable analysis was performed using the cox proportional hazard model. Of the 73 patients included, 52 (71%) were female and 21 (29%) were male, and median age at time of treatment was 73 years (60-79 years). Pretreatment BNI scores were BNI III in 9 (12%) patients, BNI IV in 42 (58%) patients, and BNI V in 22 (30%) patients. After SRS, 62 (85%) patients experienced significant symptom relief (BNI I-III). Median time to symptom relief was 2mo (IQR 1-4mo) with a median duration of 12mo (2-28mo). Median time to treatment failure was 12mo (IQR 2-28mo). On multivariable analysis evaluating IDL characteristics associated with achieving symptom relief, 195% IDL kurtosis was associated with increased likelihood of achieving symptom relief (HR 1.94, p<.001), while and 180% IDL voxel size was associated with reduced likelihood of achieving symptom relief (HR 0.99, p<.001). In the treatment of TGN with SRS, these initial results suggests that high dose IDL structure characteristics, specifically the size and shape features of the dose distributions, may be associated with likelihood of achieving symptom relief. Future directions include analysis of a richer set of dosimetric features in a larger cohort to further refine significant dosimetric parameters for treatment planning.

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.

Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model. The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV. The MRI-based deep learning model is an accurate and reliable tool for measuring TV.

Haralick texture analysis for microdosimetry: characterization of Monte Carlo generated 3D specific energy distributions

Objective. Explore the application of Haralick textural analysis to 3D distributions of specific energy (energy imparted per unit mass) scored in cell-scale targets considering varying mean specific energy (absorbed dose), target volume, and incident spectrum. Approach. Monte Carlo simulations are used to generate specific energy distributions in cell-scale water voxels ((1 μm)3–(15 μm)3) irradiated by photon sources (mean energies: 0.02–2 MeV) to varying mean specific energies (10–400 mGy). Five Haralick features (homogeneity, contrast, entropy, correlation, local homogeneity) are calculated using an implementation of Haralick analysis designed to reduce sensitivity to grey level quantization and are interpreted using fundamental radiation physics. Main results. Haralick measures quantify differences in 3D specific energy distributions observed with varying voxel volume, absorbed dose magnitude, and source spectrum. For example, specific energy distributions in small (1–3 μm) voxels with low magnitudes of absorbed dose (10 mGy) have relatively high measures of homogeneity and local homogeneity and relatively low measures of contrast and entropy (all relative to measures for larger voxels), reflecting the many voxels with zero specific energy in an otherwise sporadic distribution. With increasing target size, energy is shared across more target voxels, and trends in Haralick measures, such as decreasing homogeneity and increasing contrast and entropy, reflect characteristics of each 3D specific energy distribution. Specific energy distributions for sources of differing mean energy are characterized by Haralick measures, e.g. contrast generally decreases with increasing source energy, correlation and homogeneity are often (not always) higher for higher energy sources. Significance. Haralick texture analysis successfully quantifies spatial trends in 3D specific energy distributions characteristic of radiation source, target size, and absorbed dose magnitude, thus offering new avenues to quantify microdosimetric data beyond first order histogram features. Promising future directions include investigations of multiscale tissue models, targeted radiation therapy techniques, and biological response to radiation.

Constraining primordial magnetic fields with line-intensity mapping

Primordial magnetic fields (PMFs) offer a compelling explanation for the origin of observed magnetic fields, especially on extragalactic scales. Such PMFs give rise to excess of power in small scale matter perturbations that could strongly influence structure formation. We study the impact of the magnetically enhanced matter power spectrum on the signal that will be observed by line-intensity mapping (LIM) surveys targeting carbon monoxide (CO) emission from star-forming galaxies at high redshifts. Specifically, the voxel intensity distribution of intensity maps provides access to small-scale information, which makes it highly sensitive to signatures of PMFs on matter overdensities. We present forecasts for future LIM CO surveys, finding that they can constrain PMF strength as small as B 1Mpc ∼ 0.006–1 nG, depending on the magnetic spectral index and the targeted redshifts.

A multitracer empirically driven approach to line-intensity mapping light cones

ABSTRACT Line-intensity mapping (LIM) is an emerging technique to probe the large-scale structure of the Universe. By targeting the integrated intensity of specific spectral lines, it captures the emission from all sources and is sensitive to the astrophysical processes that drive galaxy evolution. Relating these processes to the underlying distribution of matter introduces observational and theoretical challenges, such as observational contamination and highly non-Gaussian fields, which motivate the use of simulations to better characterize the signal. In this work we present skyline , a computational framework to generate realistic mock LIM observations that include observational features and foreground contamination, as well as a variety of self-consistent tracer catalogues. We apply our framework to generate realizations of LIM maps from the multidark planck 2 simulations coupled to the universemachine galaxy formation model. We showcase the potential of our scheme by exploring the voxel intensity distribution and the power spectrum of emission lines such as 21 cm, CO, [C ii], and Lyman-α, their mutual cross-correlations, and cross-correlations with galaxy clustering. We additionally present cross-correlations between LIM and submillimetre extragalactic tracers of large-scale structure such as the cosmic infrared background and the thermal Sunyaev-Zel’dovich effect, as well as quantify the impact of galactic foregrounds, line interlopers, and instrument noise on LIM observations. These simulated products will be crucial in quantifying the true information content of LIM surveys and their cross-correlations in the coming decade, and to develop strategies to overcome the impact of contaminants and maximize the scientific return from LIM experiments.

Polarimetric second harmonic generation microscopy of partially oriented fibers II: Imaging study

Polarimetric second harmonic generation (SHG) microscopy imaging is employed to investigate the ultrastructural organization of biological and biomimetic partially oriented fibrillar structures. The linear polarization-in polarization-out SHG microscopy measurements are conducted with rat tail tendon, rabbit cornea, pig cartilage, and biomimetic meso-tetra(4-sulfonatophenyl)porphine (TPPS4) cylindrical aggregates, which represent different two- and three-dimensional (2D and 3D) configurations of C6 symmetry fibril structures in the focal volume (voxel) of the microscope. The polarization-in polarization-out imaging of rat tail tendon reveals that SHG intensity is affected by parallel/antiparallel arrangements of the fibers, and achiral (R) and chiral (C) susceptibility component ratio values change by tilting the tendon fibers out of image plane. The R ratio changes for the 2D crossing fibers observed in cornea tissue. The 3D crossing of fibers also affects R ratio in cartilage tissue. The distinctly different dependence of R on crossing and tilting of fibers is demonstrated in collagen and TPPS4 aggregates, due to the achiral molecular susceptibility ratio having values below and above 3, respectively. The polarimetric microscopy results correspond well with the analytical expressions of amplitude and R and C ratios dependence on the crossing angle of the fibers. The experimentally measured SHG intensity and R and C ratio maps are consistent with the computational modeling of various fiber configurations presented in the preceding article. The demonstrated SHG intensity and R and C ratio dependencies on fibril configurations provide the basis for interpreting polarimetric SHG microscopy images in terms of 3D ultrastructural organization of fibers in each voxel of the samples.

Polarimetric second-harmonic generation microscopy of partially oriented fibers I: Digital modeling

Second-harmonic generation (SHG) in biological tissues originates predominantly from noncentrosymmetric fibrillar structures partially oriented within a focal volume (voxel) of a multiphoton excitation microscope. This study is aimed to elucidate fibrillar organization factors influencing SHG intensity, as well as achiral, R, and chiral, C, nonlinear susceptibility tensor component ratios. SHG response is calculated for various configurations of fibrils in a voxel using the digital nonlinear microscope. The R and C ratios are calculated using linear incident and outgoing polarization states that simulate polarization-in polarization-out polarimetric measurements. The investigation shows strong SHG intensity dependence on parallel/antiparallel fiber organization. The R and C ratios are strongly influenced by the fiber chirality, tilting of the fibers out of the image plane, and crossing of the fibers. The computational modeling provides the basis for the interpretation of polarimetric SHG microscopy images in terms of the ultrastructural organization of fibers in each voxel of the samples. The modeling results are employed in the accompanying paper to investigate the ultrastructures with parallel/antiparallel fibers and two-dimensional and tree-dimensional crossing fibers in biological and biomimetic structures.

The dosimetric impact of titanium implants in spinal SBRT using four commercial treatment planning algorithms.

To evaluate the dosimetric impact of titanium implants in spine SBRT using four dose calculation algorithms. Twenty patients with titanium implants in the spine treated with SBRT without density override (DO) were selected. The clinical plan for each patient was created in Pinnacle and subsequently imported into Eclipse (AAA and AcurosXB) and Raystation (CC) for dose evaluation with and without DO to the titanium implant. We renormalized all plans such that 90% of the tumor volume received the prescription dose and subsequently evaluated the following dose metrics: (1) the maximum dose to 0.03 cc (Dmax), dose to 99% (D99%) and 90% (D90%) of the tumor volume; (2) Dmax and volumetric metrics of the spinal cord. For the same algorithm, plans with and without DO had similar dose distributions. Differences in Dmax, D99% and D90% of the tumor were on average <2% with slightly larger variations up to 5.58% in Dmax using AcurosXB. Dmax of the spinal cord for plans calculated with DO increased but the differences were clinically insignificant for all algorithms (mean: 0.36%±0.7%). Comparing to the clinical plans, the relative differences for all algorithms had an average of 1.73% (-10.36%-13.21%) for the tumor metrics and -0.93% (-9.87%-10.95%) for Dmax of the spinal cord. A few cases with small tumor and spinal cord volumes, dose differences of >10% in both D99% and Dmax of the tumor, and Dmax of the spinal cord were observed. For all algorithms, the presence of titanium implants in the spine for most patients had minimal impact on dose distributions with and without DO. For the same plan calculated with different algorithms, larger differences in volumetric metrics of >10% could be observed, impacted by dose gradient at the plan normalization volume, tumor volumes, plan complexity, and partial voxel volume interpolation.

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging.

This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Echo grid integration: A novel method for preprocessing multibeam water column data to quantify underwater gas bubble emissions

AbstractWater column imaging multibeam echo sounder systems (MBESs) are a promising technology for quantitative estimates of the gas bubble volume flow within large gas seepage areas. Considerable progress has been made in recent years toward applicable calibration methods for MBESs as well as developing inversion models to convert acoustically measured backscattering cross sections to gas bubble volume flow. However, MBESs are still not commonly used for quantitative gas flow assessments. A reason for this is the absence of published processing methods that demonstrate how MBES data can be processed to quantitatively represent bubble streams. Here, we present a novel method (echo grid integration) that allows for assessing the aggregated backscattering cross section of targets within horizontal water layers. This derived value enables quantifying bubble stream gas flow rates using existing acoustic inversion methods. The presented method is based on averaging geo‐referenced volume backscattering coefficients onto a high‐resolution 3D voxel‐grid. The results are multiplied with the voxel volume to represent measurements of the total backscattering cross‐section within each voxel cell. Individual gridded values cannot be trusted because the beam pattern effects cause the values of individual targets to “smear” over multiple grid‐cells. The true aggregated backscattering cross‐section is thus estimated as the integral over the grid‐cells affected by this smearing. Numerical simulation of MBES data acquisition over known targets assesses the method's validity and quantify it's uncertainty for different, realistic scenarios. The found low measurement bias (&lt; 1%), and dispersion (&lt; 5%) are promising for application in gas flow quantification methods.

Classification of amyloid positivity in PET imaging using end‐to‐end deep learning: a multi‐cohort, multi‐tracer analysis

AbstractBackgroundPositron emission tomography (PET) imaging with amyloid‐specific tracers is the gold standard for assessing amyloid positivity in vivo. However, visual interpretation of PET images is limited by availability of clinicians and interobserver variability. Classification of amyloid status typically requires calculation of standardized uptake value ratios (SUVRs), correspondence with neuroanatomy from magnetic resonance imaging, and additional complex, computationally intensive preprocessing steps requiring specialized software. We developed a deep learning model that can automatically classify unprocessed PET images as amyloid positive or negative.MethodFlorbetapir (AV45), florbetaben (FBB), and Pittsburgh compound B (PiB) images from the Alzheimer’s Disease Neuroimaging Association (ADNI), AV45 and PiB images from the Open Access Series of Imaging Studies 3 (OASIS3), and FBB, flutametamol (FMT), and flutafuranol (NAV4694) images from the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) were used (Table 1). Each image was resized to isotropic 2 mm voxels, and voxel intensities between the 5th and 95th percentile were normalized to zero mean and unit variance. We trained and internally validated a convolutional neural network (CNN) on AV45 images from ADNI before testing it on images from an independent cohort and/or acquired with a different tracer.ResultThe model achieved an AUC of 0.98 [0.97, 1.0] on test AV45 images from ADNI and generalized well to AV45 images from OASIS3, achieving an AUC of 0.95 [0.92, 0.97] (Table 2). Critically, the model’s performance remained robust even when evaluating imaging from different tracers, including FBB, FMT, PiB, and NAV4694, scoring an AUC of at least 0.97 on each and demonstrating its ability to learn a tracer‐agnostic pattern of abnormality. Similar conclusions can be drawn from other performance metrics.ConclusionWe developed a deep learning model that can classify amyloid PET scans with a high degree of accuracy. We demonstrated that the model generalizes well to unseen images acquired from an independent cohort, under a different protocol, and/or with tracers unseen during training. Our model may usefully provide concise metrics for amyloid research as well as support clinical assessment of amyloid‐specific PET when expertise or computational resources are limited.

Assessing the quality of 3D point clouds using descriptors for color and geometry texture

Since the mid-20th century, the use of digital formats for visual content has revolutionized communication in society. The Internet and digital broadcasting systems, which became widely available in the 1990s, led to an incredible expansion of multimedia consumption among the public. As a result, telecommunication networks and providers were pushed to their limits to meet the growing demand for multimedia content. Traditional electronic imaging systems, such as TV broadcasting systems, were designed based on subjective quality analysis for defining parameters like the number of lines in a video. However, with the emergence of new digital visual content services that require faster and more affordable methods of evaluating human perceived quality, there has been a need for automatic quality assessment. In the past few decades, numerous visual quality models based on algorithms running on digital computers have been proposed. While these existing models are highly advanced for 2D digital imagery, a new generation of immersive media has emerged with different data structures that are not applicable to traditional 2D methods and require novel quality assessment metrics. These emerging immersive media formats provide a 3D visual representation of real objects and scenes. In this new visual format, objects can be captured, compressed, transmitted, an visualized in real-time as 3D content, allowing consumers to select free-viewpoints to view such media. One of the most popular formats for immersive media is Point Cloud (PC), which is composed of points with 3D geometry coordinates and color information, and sometimes other attributes such as reflectance and transparency. This research focuses on the quality assessment of 3D Point Clouds using novel color and geometric texture statistics. Since distortions in both color and geometry attributes of 3D visual content can affect the perceived visual quality, this work proposes using both color-based and geometry-based texture descriptors for Point Clouds to obtain visual degradation information through their statistics. Four novel Point Cloud texture descriptors are introduced in this work, three of which are color-based and one which is geometry-based. Additionally, a new voxelization method is proposed, which converts points to voxels (volume elements), and improves the performance of the color-based texture descriptors. The proposed Point Cloud quality assessment method demonstrates high performance, comparable to the state-of-the-art methods, while also being flexible and extensible to adapt to different types of distortions.

Subspace Culling for Ray-Box Intersection

Ray tracing is an essential operation for realistic image synthesis. The acceleration of ray tracing has been studied for a long period of time because algorithms such as light transport simulations require a large amount of ray tracing. One of the major approaches to accelerate the intersections is to use bounding volumes for early pruning for primitives in the volume. The axis-aligned bounding box is a popular bounding volume for ray tracing because of its simplicity and efficiency. However, the conservative bounding volume may produce extra empty space in addition to its content. Especially, primitives that are thin and diagonal to the axis give false-positive hits on the box volume due to the extra space. Although more complex bounding volumes such as oriented bounding boxes may reduce more false-positive hits, they are computationally expensive. In this paper, we propose a novel culling approach to reduce false-positive hits for the bounding box by embedding a binary voxel data structure to the volume. As a ray is represented as a conservative voxel volume as well in our approach, the ray--voxel intersection is cheaply done by bitwise AND operations. Our method is applicable to hierarchical data structures such as bounding volume hierarchy (BVH). It reduces false-positive hits due to the ray--box test and reduces the number of intersections during the traversal of BVH in ray tracing. We evaluate the reduction of intersections with several scenes and show the possibility of performance improvement despite the culling overhead. We also introduce a compression approach with a lookup table for our voxel data. We show that our compressed voxel data achieves significant false-positive reductions with a small amount of memory.

Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy

To perform setup procedures including both positional and dosimetric information, we developed a CT–CT rigid image registration algorithm utilizing water equivalent pathlength (WEPL)-based image registration and compared the resulting dose distribution with those of two other algorithms, intensity-based image registration and target-based image registration, in prostate cancer radiotherapy using the carbon-ion pencil beam scanning technique. We used the data of the carbon ion therapy planning CT and the four-weekly treatment CTs of 19 prostate cancer cases. Three CT–CT registration algorithms were used to register the treatment CTs to the planning CT. Intensity-based image registration uses CT voxel intensity information. Target-based image registration uses target position on the treatment CTs to register it to that on the planning CT. WEPL-based image registration registers the treatment CTs to the planning CT using WEPL values. Initial dose distributions were calculated using the planning CT with the lateral beam angles. The treatment plan parameters were optimized to administer the prescribed dose to the PTV on the planning CT. Weekly dose distributions using the three different algorithms were calculated by applying the treatment plan parameters to the weekly CT data. Dosimetry, including the dose received by 95% of the clinical target volume (CTV-D95), rectal volumes receiving > 20 Gy (RBE) (V20), > 30 Gy (RBE) (V30), and > 40 Gy (RBE) (V40), were calculated. Statistical significance was assessed using the Wilcoxon signed-rank test. Interfractional CTV displacement over all patients was 6.0 ± 2.7 mm (19.3 mm maximum standard amount). WEPL differences between the planning CT and the treatment CT were 1.2 ± 0.6 mm-H2O (< 3.9 mm-H2O), 1.7 ± 0.9 mm-H2O (< 5.7 mm-H2O) and 1.5 ± 0.7 mm-H2O (< 3.6 mm-H2O maxima) with the intensity-based image registration, target-based image registration, and WEPL-based image registration, respectively. For CTV coverage, the D95 values on the planning CT were > 95% of the prescribed dose in all cases. The mean CTV-D95 values were 95.8 ± 11.5% and 98.8 ± 1.7% with the intensity-based image registration and target-based image registration, respectively. The WEPL-based image registration was CTV-D95 to 99.0 ± 0.4% and rectal Dmax to 51.9 ± 1.9 Gy (RBE) compared to 49.4 ± 9.1 Gy (RBE) with intensity-based image registration and 52.2 ± 1.8 Gy (RBE) with target-based image registration. The WEPL-based image registration algorithm improved the target coverage from the other algorithms and reduced rectal dose from the target-based image registration, even though the magnitude of the interfractional variation was increased.

Magnetic resonance imaging of peripheral venous distension with cyclooxygenase inhibition in healthy humans

Background: Peripheral venous distension, induced by a volume infusion into the veins in an occluded limb, evokes a reflex pressor and sympathoexcitatory response in humans (venous distension reflex, VDR). However, to what extent the procedure alters the size of superficial and deep veins remains unclear. Our data showed that local cyclooxygenase (COX) inhibition could attenuate the muscle sympathetic nerve activity (MSNA) and blood pressure (BP) responses to venous distension. It is unclear if the COX blockade would alter the magnitudes of the venous distension. The purpose of this study was to evaluate the changes in superficial and deep vein sizes by the venous distension procedure in healthy subjects under local COX blockade and control conditions. This study is associated with a registered clinical trial (NCT03513770). Hypothesis: We hypothesized that the voxel volume of both superficial and deep veins would increase following peripheral venous distension. We further hypothesized that COX blockade would not alter the distribution of infused volume. Methods: The study had a randomized, double-blind, placebo-controlled, and crossover design with two separate visits. Thirteen healthy volunteers underwent peripheral venous distension by which a volume of solution (saline alone or 9 mg ketorolac tromethamine in saline) was infused into the median cubital fossa of an arterially occluded forearm. Each trial (saline or COX blockade) was separated by at least 24 hours. The total infused volume (i.e., 5% of the forearm volume) was held constant across both saline and COX blockade trails. The 3D proton density-weighted magnitude and phase images of the treated arm were obtained before and after the venous distension procedure via a 3T magnetic resonance imaging scanner (Siemens PrismaFit). The average vein voxel volume of 10 forearm veins (six superficial and four deep) was calculated from several adjacent slices of the images. Results: Vein voxel volume was significantly increased by venous distension in both superficial (both P &lt; .001) and deep (both P &lt; 0.01) veins in both trials. However, COX blockade did not influence this increase in voxel volume in either superficial (NSS: Δ2.34 ± 3.05 vs. COX blockade: Δ1.99 ± 2.27 mm3, P = 0.64) or deep (NSS: Δ1.02 ± 1.2 vs. COX blockade: Δ1.06 ± 1.42 mm3, P = 0.94) veins. Conclusion: These data suggest that both superficial and deep veins may contribute to the VDR. Importantly, the results suggest that any attenuation in the MSNA and BP responses to venous distension with COX blockade is not a result of altered magnitudes of the venous distension during the procedure. This study was supported by National Institutes of Health Grants R01 HL144781 (to J.C. and L.I.S). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Volumetric flame reconstructions in an optical engine cylinder involving refraction and blockage

Practical applications of computed tomography (CT) in optical engines require an advanced algorithm that can correct the light refraction via optical windows and reconstruct the 3D signal field partially blocked by structural obstacles. In this work, an advanced CT algorithm is designed for optical engines to simultaneously eliminate the imaging distortion by refraction and diminish the reconstruction errors using partial signal blocking. By combining the pinhole model and Snell’s law, the ray tracings from discretized 3D voxels in the measurement domain to 2D pixels in the imaging planes are accurately calculated, thus restoring the distortion in recorded projections. Besides, by deciding the locations and numbers of voxels that actually participate in iterative CT calculation, the iterative update process of voxel intensity becomes independent of the blocked rays, reducing the reconstruction errors. The algorithm is then numerically validated by reconstructing a simulated signal phantom inside an optical cylinder with a lightproof obstacle between the phantom and a recording camera, which imitates the refraction and blocking conditions in practical optical engines. Moreover, experimental demonstration is performed by reconstructing practical premixed flames inside optical engines. Both the simulation and the experiment present significantly enhanced flame chemiluminescence reconstruction by applying the optimized CT algorithm compared to the original algorithm utilized in open space applications.