Volume Rendering Research Articles

Triplicated middle cerebral arteries (duplicated and ipsilateral accessory) associated with triplicated anterior cerebral arteries (accessory) diagnosed by magnetic resonance angiography.

To describe a case of duplicated middle cerebral artery (MCA) combined with ipsilateral accessory MCA, forming a triplicated MCA, associated with the accessory anterior cerebral artery (ACA), forming a triplicated A2 segment of the ACA detected incidentally on magnetic resonance (MR) angiography. A 70-year-old woman with internal carotid artery (ICA) stenosis at the origin, which was detected by ultrasound, underwent cranial MR imaging and MR angiography of the intracranial region for an evaluation of brain and cerebral arterial lesions. The MR machine was a 3-Tesla scanner. MR angiography was performed using a standard 3-dimensional time-of-flight technique. Multiple ischemic white matter lesions are observed. No significant stenotic lesions were observed in intracranial arteries. The right duplicated MCA was originated from right distal ICA. And main MCA was originated from right ICA bifurcation. Right accessory MCA was arisen from the A2 segment of the right ACA. Thus, the right MCA was triplicated. There was also an accessory ACA forming a triplicated ACA at its A2 segment. These findings were clearly identified on partial volume-rendering (VR) images. We herein report a case of triplicated MCA associated with triplicated ACA. MCA variations are relatively rare, and this is the third case of triplicated MCA reported in relevant English-language literature. To identify multiple cerebral arterial variations, creating partial VR images using MR angiographic source images is useful.

FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces

3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts---photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It runs natively on commodity graphics soft- and hardware, and allows for a graceful trade-off between quality and efficiency. Our method utilizes recent advances in neural rendering, particularly learning discrete radiance manifolds to sparsely sample the scene to model volumetric effects. We achieve efficient modeling by learning a single set of manifolds for the entire dynamic sequence, while implicitly modeling appearance changes as temporal canonical texture. We export a single layered mesh and view-independent RGBA texture video that is compatible with legacy graphics renderers without additional ML integration. We demonstrate our method by rendering dynamic face captures of real actors in a game engine, at comparable photorealism to state-of-the-art neural rendering techniques at previously unseen frame rates.

MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models

BackgroundRadiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy.MethodsMice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors).ResultsThe RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both).ConclusionsWe have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

Distinguishing Laterality in Brain Injury in Rabbit Fetal MRI Using Novel Volume Rendering Techniques.

Our laboratory has been exploring the MRI detection of fetal brain injury, which previously provided a prognostic biomarker for newborn hypertonia in an animal model of cerebral palsy (CP). The biomarker relies on distinct patterns of diffusion-weighted imaging-defined apparent diffusion coefficient (ADC) in fetal brains during uterine ischemia (H-I). Despite the challenges posed by small brains and tissue acquisition, our objective was to differentiate between left and right brain ADC changes. A novel aspect involved utilizing three-dimensional rendering techniques to refine ADC measurements within spheroids encompassing fetal brain tissue. 25-day gestation rabbit fetuses underwent global hypoxia due to maternal uterine ischemia. Successful differentiation of left and right brain regions was achieved in 28% of the fetal brains. Ordinal analysis revealed predominantly higher ADC on the left side compared to the right at baseline and across the entire time series. During H-I and reperfusion-reoxygenation, the right side exhibited a favored percentage change. Among these fetal brains, 73% exhibited the ADC pattern predictive of hypertonia. No significant differences between left and right sides were observed in patterns predicting hypertonia, except for one time point during hypoxia-ischemia. This study also highlights a balance between left-sided and right-sided alterations within the population. This study emphasizes the importance of investigating laterality and asymmetric hemispheric lesions for early diagnosis of brain injury leading to CP. The technological limitations in obtaining a clear picture of the entire fetal brain for every fetus mirror the challenges encountered in human studies.

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.

A Transformer-Guided Cross-Modality Adaptive Feature Fusion Framework for Esophageal Gross Tumor Volume Segmentation

Background and ObjectiveAccurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. MethodsSpecifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. ResultsThrough extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. ConclusionsThe devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images.

Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning (DL) networks as convolutional neural network (CNN) and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption.
Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume.
Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% vs 76.5%), smaller needle tip position (1.28mm vs 1.87mm) and length (1.78mm vs 2.19mm) errors on the kidney dataset as well as DSC (78.5% vs 76.9%), needle tip position (0.86mm vs 1.12mm) and length (1.01mm vs 1.26mm) errors on the prostate dataset.
Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.&#xD.

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum.

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host-pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum-Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral (x-y) pixel resolution of 10 nm and an axial (z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum-Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Determinants and effects of microvascular obstruction on serial change in left ventricular diastolic function after reperfused acute myocardial infarction

BackgroundAlthough left ventricular (LV) diastolic dysfunction is more related to functional capacity after acute myocardial infarction (AMI), the determinants of LV diastolic functional change after reperfused AMI remain unknown. This study aimed to investigate the effects of microvascular obstruction (MVO) on mid-term changes in LV diastolic function after reperfused AMI.MethodsIn a cohort of 72 AMI patients who underwent successful revascularization, echocardiography and cardiovascular magnetic resonance imaging were repeated at 9-month intervals. The late gadolinium enhancement (LGE) amount, segmental extracellular volume fraction, global LV, and left atrial (LA) phasic functions, along with mitral inflow and tissue Doppler measurements, were repeated.ResultsAmong the included patients, 31 (43%) patients had MVO. During the 9-month interval, LV ejection fraction (EF) and LV global longitudinal strain (GLS) were significantly improved in accordance with a decrease in LGE amount (from 18.2 to 10.3 g, p < 0.001) and LV mass. The deceleration time (DT) of early mitral inflow (188.6 ms–226.3 ms, p < 0.001) and LV elastance index (Ed; 0.133 1/ml–0.127 1/ml, p = 0.049) were significantly improved, but not in conventional diastolic functional indexes. Their improvements occurred in both groups; however, the degree was less prominent in patients with MVO. The degree of decrease in LGE amount and increase in LVEF was significantly correlated with improvement in LV-Ed or LA phasic function, but not with conventional diastolic functional indexes.ConclusionsIn patients with reperfused AMI, DT of early mitral inflow, phasic LA function, and LV-Ed were more sensitive diastolic functional indexes. The degree of their improvement was less prominent in patients with MVO.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD. A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale. Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse. The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.

Severe Bronchopulmonary Dysplasia Adversely Affects Brain Growth in Preterm Infants

Introduction: Bronchopulmonary dysplasia (BPD) is associated with neurodevelopmental outcomes of preterm infants, but its effect on brain growth in preterm infants after the neonatal period is unknown. This study aimed to evaluate the effect of severe BPD on brain growth of preterm infants from term to 18 months of corrected age (CA). Methods: Sixty-three preterm infants (42 with severe BPD and 21 without severe BPD) who underwent magnetic resonance imaging at term equivalent age (TEA) and 18 months of CA were studied by using the Infant Brain Extraction and Analysis Toolbox (iBEAT). We measured segmented brain volumes and compared brain volume and brain growth velocity between the severe BPD group and the non-severe BPD group. Results: There was no significant difference in brain volumes at TEA between the groups. However, the brain volumes of the total brain and cerebral white matter in the severe BPD group were significantly smaller than those in the non-severe BPD group at 18 months of CA. The brain growth velocities from TEA to 18 months of CA in the total brain, cerebral cortex, and cerebral white matter in the severe BPD group were lower than those in the non-severe BPD group. Conclusion: Brain growth in preterm infants with severe BPD from TEA age to 18 months of CA is less than that in preterm infants without severe BPD.

Effects of Partial-Body, Continuous/Pulse Irradiation at Dose Rates from Flash to Conventional Rates on The Level of Surviving Blood Lymphocytes: Modeling Approach II. Two- and Multiple-Pulse Irradiation.

Mathematical models, which describe effects of partial-body, two- and multiple-pulse irradiation at high total doses D and at average dose rates N from FLASH to conventional rates on the level of surviving blood lymphocytes in humans and mice, have been developed originating in the previously proposed approach. These models predict that levels of surviving blood lymphocytes in humans and mice increase with increasing the dose rate from N = D / TR (TR is the time of the blood flowing into or out of the irradiated segment of the blood circulatory system) to FLASH rates and approach an upper limiting level equal to (1- vR), where vR is the fraction of blood volume in the irradiated segment of the blood circulatory system. Levels of surviving blood lymphocytes computed at total doses D of 10-40 Gy and at average of dose rates N, which are equal to or exceed 40 Gy/s for humans and 400 Gy/s for mice, are nearly indistinguishable from the upper limiting level. These results can be interpreted as the models reproducing the optimal blood lymphocyte sparing in these mammals after such exposures. With decreasing the dose rate from N = D/ TR to conventional rates, at multiple-pulse irradiation the levels of surviving blood lymphocytes in humans and mice decrease to lower limiting levels, whereas at two-pulse irradiation they change cyclically and do not fall below their values for the delivery time equal to TR. Additionally, effects of two- and multiple-pulse irradiation of the whole abdomen in mice on the level of surviving blood lymphocytes are simulated within the developed models. Regimens of two- and multiple-pulse irradiation are taken the same as those reported in experiments, where effects of such exposures on the level of surviving crypts in mice were studied. Juxtaposing the modeling results with the experimental data reveals that the level of surviving blood lymphocytes in mice after two- and multiple-pulse irradiation of the abdomen at average dose rates N from FLASH to conventional rates modulates the level of surviving crypts in these animals after such exposures. A hypothesis is proposed to explain this phenomenon.

Vein of Galen aneurysmal malformation: does size affect outcome?

To validate a semiautomated method for segmenting vein of Galen aneurysmal malformations (VGAM) and to assess the relationship between VGAM volume and other angioarchitectural features, cardiological findings, and outcomes. In this retrospective study, we selected all subjects with VGAM admitted to theGaslini Children's Hospital between 2009 and 2022. Clinical data were retrieved from electronic charts. We compared 3D-Slicer segmented VGAM volumes obtained by two independent observers using phase-contrast MR venography to those obtained with manual measurements performed on T2-weighted images. The relationship between VGAM volumes and clinical and neuroimaging features was then explored. Forty-three subjects with VGAM (22 males, mean age 6.56days) were included in the study. Manual and semiautomated VGAM volumes were well correlated for both readers (r = 0.86 and 0.82, respectively). Regarding reproducibility, the inter-rater interclass correlation coefficients were 0.885 for the manual method and 0.992 for the semiautomated method (p < 0.001). The standard error for repeated measures was lower for the semiautomated method (0.04 versus 0.40 of manual method). Higher VGAM volume was associated with superior sagittal sinus narrowing, jugular bulb stenosis, and aqueductal stenosis (p < 0.05). A weak correlation was found between VGAM volume and straight sinus dilatation (r = 0.331) and superior sagittal sinus index (r =  - 0.325). No significant associations were found with cardiac findings, post-embolization complications, and outcome (p > 0.05). Semiautomated VGAM volumetry is feasible and reliable with improved reproducibility compared to the manual method. VGAM volume is not a prognostic factor for clinical outcome, but it is related to other venous findings with potential hemodynamic effects.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

Abstract. X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid–rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep-learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep-learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine-learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep-learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a graphic processing unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented computed tomography images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep-learning approaches in elevating the quality and reproducibility of research in the geosciences.

Can Automated 3-Dimensional Dixon-Based Methods Be Used in Patients With Liver Iron Overload?

Accurate quantification of liver iron concentration (LIC) can be achieved via magnetic resonance imaging (MRI). Maps of liver T2*/R2* are provided by commercially available, vendor-provided, 3-dimensional (3D) multiecho Dixon sequences and allow automated, inline postprocessing, which removes the need for manual curve fitting associated with conventional 2-dimensional (2D) gradient echo (GRE)-based postprocessing. The main goal of our study was to investigate the relationship among LIC estimates generated by 3D multiecho Dixon sequence to values generated by 2D GRE-based R2* relaxometry as the reference standard. A retrospective review of patients who had undergone MRI scans for estimation of LIC with conventional T2* relaxometry and 3D multiecho Dixon sequences was performed. A 1.5 T scanner was used to acquire the magnetic resonance studies. Acquisition of standard multislice multiecho T2*-based sequences was performed, and R2* values with corresponding LIC were estimated. The comparison between R2* and corresponding LIC estimates obtained by the 2 methods was analyzed via the correlation coefficients and Bland-Altman difference plots. This study included 104 patients (51 male and 53 female patients) with 158 MRI scans. The mean age of the patients at the time of scan was 15.2 (SD, 8.8) years. There was a very strong correlation between the 2 LIC estimation methods for LIC values up to 3.2 mg/g (LIC quantitative multiecho Dixon [qDixon; from region of interest R2*] vs LIC GRE [in-house]: r = 0.83, P < 0.01; LIC qDixon [from segmentation volume R2*] vs LIC GRE [in-house]: r = 0.92, P < 0.01); and very weak correlation between the 2 methods at liver iron levels >7 mg/g. Three-dimensional-based multiecho Dixon technique can accurately measure LIC up to 7 mg/g and has the potential to replace 2D GRE-based relaxometry methods.

Validation of Dynamic 3D MRI for Urodynamics Assessment Using an Anatomically Realistic In Vitro Model of the Bladder.

Lowery urinary tract symptoms (LUTS) affect a large majority of the aging population. 3D Dynamic MRI shows promise as a noninvasive diagnostic tool that can assess bladder anatomy and function (urodynamics) while overcoming challenges associated with current urodynamic assessment methods. However, validation of this technique remains an unmet need. In this study, an anatomically realistic, bladder-mimicking in vitro flow model was created and used to systematically benchmark 3D dynamic MRI performance using a highly controllable syringe pump. Time-resolved volumes of the synthetic bladder model were obtained during simulated filling and voiding events and used to calculate volumetric flowrate. During MRI acquisitions, pressure during each event was recorded and used to create PV loops for work assessment. Error between control and MRI-derived volume for voiding and filling events exhibited 3.36% and 4.66% differences, respectively. A slight increase in average error was observed for MRI-derived flowrate when compared to the control flowrate (4.90% and 7.67% for voiding and filling, respectively). Overall, average error in segmented volumes increased with decreasing volume flowrate. Pressure drops were observed during voiding. Pressure increased during filling. Enhanced validation of novel 3D MRI urodynamics is achieved by using high-resolution PIV for visualizing and quantifying velocity inside the bladder model, which is not currently possible with 3D Dynamic MRI.

NeUDF: Learning Neural Unsigned Distance Fields With Volume Rendering.

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in the neural implicit rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF, that can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. To gain the flexibility of representing arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as surface representation. While a naive extension of SDF-based neural renderer cannot scale to UDF, we formalize the rules of neural volume rendering for open surface reconstruction (e.g., self-consistent, unbiased, occlusion-aware), and derive a dedicated rendering weight function specially tailored for UDF. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity. We extensively evaluate our method over a number of challenging datasets, including two typical open surface datasets MGN (Bhatnagar et al., 2019) and Deep Fashion 3D (Zhu et al., 2020). Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art methods in the task of multi-view surface reconstruction, especially for the complex shapes with open boundaries.

Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer

Background and PurposeAutomatic segmentation methods have greatly changed the radiotherapy workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features. Methods and MaterialsWe trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient (DSC)<T and DSC⩾T. Two distinct cohorts of T2W pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing. ResultsSegmentation by 2D-SegResNet achieved the best DSC, Surface Dice Similarity Coefficient (SDSC3mm), and 95th Hausdorff Distance (95HD): DSC=0.72±0.16, SDSC3mm=0.66±0.17, and 95HD=14.6±9.0 mm without missing segmentation (M=0) on the test cohort. Failure detection generated precision (P=0.88), recall (R=0.75), F1-score (F=0.81), and accuracy (A=0.86) using Logistic Regression (LR) classifier on the test cohort with a threshold T=0.67 on DSC values. ConclusionsOur study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.