Small Substructures Research Articles

Measuring global urban complexity from the perspective of living structure

As urban critic Jane Jacobs conceived, a city is essentially the problem of organized complexity. What underlies the complexity refers to a structural factor, called living structure, which is defined as a mathematical structure composed of hierarchically organized substructures. Through these substructures, the complexity of cities, or equivalent to the livingness of urban space (L), can be measured by the multiplication the number of cities or substructures (S) and their scaling hierarchy (H), indicating that complexity is about both quantity of cities and how well the city is organized hierarchically. In other words, complexity emerges from a hierarchical structure where there are far more small cities or substructures than large ones across all scales, and cities are more or less similar within each individual hierarchy. In this paper, we conduct comprehensive case studies to investigate urban complexity on a global scale using multisource geospatial data. We develop an efficient approach to recursively identifying all natural cities with their inner hotspots worldwide through connected component analysis. To characterize urban complexity, urban space is initially represented as a hierarchy of recursively defined natural cities, and all the cities are then represented as a network for measuring the degree of complexity or livingness of the urban space. The results show the Earth’s surface is growing more complex from an economic perspective, and the dynamics of urban complexity are more explicit from nighttime light imagery than from population data. We further discuss the implications in city science, aiming to help create and recreate urban environments that are more resilient and livable by fostering organized complexity from the perspective of living structure.

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Research on microstructure and mechanical properties of Ti–6Al–4V ELI fabricated by hybrid directed energy deposition with in-situ rolling and annealing

To achieve superior damage tolerance and fatigue properties, it is desirable for the microstructure of Ti-6Al-4V ELI (Extra Low Interstitial) titanium alloy to consist of equiaxed grains with a homogeneous lamellar structure. However, traditional forging and single additive manufacturing techniques require extreme processes such as quasi β heat treatment and hot isostatic pressing. In this paper, a hybrid directed energy deposition method that integrates in-situ rolling with simple annealing is proposed to simultaneously achieve these microstructures. The results indicate that the initial microstructures produced by hybrid directed energy deposition consist of fine equiaxed prior-β grains, inconspicuous interpass bright bands, randomly oriented lamellar structure and numerous small substructures at the TEM scale. Due to the plastic deformation caused by in-situ rolling, the primary β grains underwent significant refinement with a reduction in grain diameter from over 2000 μm to 113.8 μm. Besides, the unique microstructures and thickness of α laths are influenced by coarsening and dissolution mechanisms that are controlled by diffusion during phase transformation. Furthermore, a uniform microstructure without bright bands and excellent comprehensive properties can be achieved through heating at 880 °C for 2 h and natural cooling. These findings validate that hybrid directed energy deposition coupled with simple annealing present a novel and cost-effective approach for the direct manufacturing of uniform titanium alloy forgings.

The comparison of densification mechanisms and microstructure evolution among TiB2 ceramics sintered under different levels of high pressure

TiB2 shows significant advantages in high-tech industry but it is hard to fulfill densification. High pressure sintering is an effective method to obtain TiB2 ceramics. This study reports the sintering condition of TiB2 ceramics under different levels of high pressure from 150 MPa to 15 GPa. The results show that the densification temperature reduced with the applied pressure. The densification mechanism of TiB2 ceramics sintered under 150 MPa during the final holding time at 1900℃ was dominated by the mixture of dislocation motion and grain boundary sliding. When the applied pressure achieves at 1.5 GPa, the densification mechanism was transformed into the plastic deformation of grains and grain boundary diffusion, a number of small angle grain boundaries, dislocation cells, substructures and straight grain boundaries were observed. Finally, the densification mechanism was transformed into plastic deformation of grains and grain boundary while the driving force increased to 15 GPa. The dislocation and substructures density increased with pressure, leading to the grain refinement. Our study compared the densification and microstructure evolution of TiB2 ceramics sintered under high pressure, proving the high pressure sintering could regulate the microstructure and refine grains.

Efficient calculation of higher order time history response derivatives by substructuring method

Time history response derivatives with respect to the design variables are frequently used in optimization design, damage detection, structural control, etc. This paper proposes a substructuring method for efficient calculation of higher order time history response derivatives of large-scale structures. First, the global structure is disassembled into several small substructures and the substructural displacement is projected onto the range space of a few substructural master eigenvectors. Afterwards, the derivative of substructural master eigenvectors of a few substructures containing the design variables are assembled to form the reduced first, second, and higher order sensitivity equations with a size of the number of master eigenvectors. The equivalent eigenvector which relates the slave and master eigenvectors is derived to compensate for the inertial effect of discarded slave eigenvectors. Finally, the first, second, and higher order time history response derivatives of global large-scale structure are efficiently solved from the reduced sensitivity equations by using Newmark- β method. A numerical one-bay plane frame and a numerical highway bridge are applied to verify the accuracy and efficiency of proposed substructuring method.

Unpaired Dual-Modal Image Complementation Learning for Single-Modal Medical Image Segmentation.

Multi-modal MR/CT image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to acquire aligned multi-modal images of a patient in clinical practice due to the high cost and specific allergic reactions to contrast agents. To address these issues, a task complementation framework is proposed to enable unpaired multi-modal image complementation learning in the training stage and single-modal image segmentation in the inference stage. To fuse unpaired dual-modal images in the training stage and allow single-modal image segmentation in the inference stage, a synthesis-segmentation task complementation network is constructed to mutually facilitate cross-modal image synthesis and segmentation since the same content feature can be used to perform the image segmentation task and image synthesis task. To maintain the consistency of the target organ with varied shapes, a curvature consistency loss is proposed to align the segmentation predictions of the original image and the cross-modal synthesized image. To segment the small lesions or substructures, a regression-segmentation task complementation network is constructed to utilize the auxiliary feature of the target organ. Comprehensive experiments have been performed with an in-house dataset and a publicly available dataset. The experimental results have demonstrated the superiority of our framework over state-of-the-art methods. The proposed method can fuse dual-modal CT/MR images in the training stage and only needs single-modal CT/MR images in the inference stage. The proposed method can be used in routine clinical occasions when only single-modal CT/MR image is available for a patient.

JWST’s PEARLS: Mothra, a new kaiju star at z = 2.091 extremely magnified by MACS0416, and implications for dark matter models

We report the discovery of Mothra, an extremely magnified monster star, likely a binary system of two supergiant stars, in one of the strongly lensed galaxies behind the galaxy cluster MACS J0416.1−2403. Mothra is in a galaxy with spectroscopic redshift z = 2.091 in a portion of the galaxy that is parsecs away from the cluster caustic. The binary star is observed only on the side of the critical curve with negative parity but has been detectable for at least eight years, implying the presence of a small lensing perturber. Microlenses alone cannot explain the earlier observations of this object made with the Hubble Space Telescope. A larger perturber with a mass of at least 104 M⊙ offers a more satisfactory explanation. Based on the lack of perturbation on other nearby sources in the same arc, the maximum mass of the perturber is 2.5 × 106 M⊙, making this the smallest substructure constrained by lensing at z > 0.3. The existence of this millilens is fully consistent with expectations from standard cold dark matter cosmology. On the other hand, the existence of such a small substructure in a cluster environment has implications for other dark matter models. In particular, warm dark matter models with particle masses below 8.7 keV are excluded by our observations. Similarly, axion dark matter models are consistent with the observations only if the axion mass is in the range 0.5 × 10−22 eV < ma < 5 × 10−22 eV.

G2Retro as a two-step graph generative models for retrosynthesis prediction

Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper,we develop a generative framework G2Retro for one-step retrosynthesis prediction. G2Retro imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. G2Retro defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, G2Retro considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that G2Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Substructuring approaches possess many superiorities over traditional global approaches in damage identification because large-size global structures are replaced by small and manageable substructures. This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural autoregressive moving average with exogenous inputs (ARMAX) model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The detection of structural damage is essentially an inverse problem, and the damage in structure bears sparse properties. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector. The accuracy of the proposed method is demonstrated by the numerical simulation of a frame structure and shaking table test of a shear building structure. As the substructural properties are more sensitive to local structural damage than the global properties, the substructural ARMAX model is quite accurate and efficient to be used in the damage identification of a complex system.

An improvement of generalized Bloch mode synthesis method-based model order reduction technique for band-structure computation of periodic structures

In this paper, an improvement of generalized Bloch mode synthesis (GBMS) method-based model order reduction technique is presented for accelerating the band-structure computation of periodic structures. The improvement is implemented through employing firstly the algebraic substructuring, which enables the mass and stiffness matrices of a single unit cell partitioned into a set of small substructures automatically in the algebraic perspective without considering the physical domain. Then these substructures are reduced via the enhanced Craig-Bampton (ECB) method. The boundary sets are divided into interior boundary and exterior boundary components. The reduced interior modes and interior boundary sets are regarded as the new interior sets relative to the exterior boundary component, and its size can be further reduced via the second level ECB method. Local-level characteristic constraint (L-CC) mode reduction technique is employed for exterior boundary reduction for further reducing the computational cost. The coupling residual modes of the two-level ECB processes are compensated after imposing the Bloch boundary conditions. Two numerical examples are presented to demonstrate the potential benefits of the proposed GBMS-based model-order reduction technique in accelerating band-structure computation of periodic structures. The results show that the proposed method can effectively improve the computational efficiency and have good computational convergence compared with the generalized Bloch mode synthesis with algebraic condensation method.

Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy

Background and purposeAccurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with potential application in clinical settings and large-scale radiation-related cardiotoxicity studies. Materials and methodsA recently developed hybrid method for automatic segmentation of 18 cardiac structures, combining deep learning, multi-atlas mapping and geometric segmentation of small challenging substructures, was independently validated on 30 lung cancer cases. These included anatomical and imaging variations, such as tumour abutting heart, lung collapse and metal artefacts. Automatic segmentations were compared with manual contours of the 18 structures using quantitative metrics, including Dice similarity coefficient (DSC), mean distance to agreement (MDA) and dose comparisons. ResultsA comparison of manual and automatic contours across all cases showed a median DSC of 0.75–0.93 and a median MDA of 2.09–3.34 mm for whole heart and chambers. The median MDA for great vessels, coronary arteries, cardiac valves, sinoatrial and atrioventricular conduction nodes was 3.01–8.54 mm. For the 27 cases treated with curative intent (planned target volume dose ≥50 Gy), the median dose difference was –1.12 to 0.57 Gy (absolute difference of 1.13–3.25%) for the mean dose to heart and chambers; and –2.25 to 4.45 Gy (absolute difference of 0.94–6.79%) for the mean dose to substructures. ConclusionThe novel hybrid automatic segmentation tool reported high accuracy and consistency over a validation set with challenging anatomical and imaging variations. This has promising applications in substructure dose calculations of large-scale datasets and for future studies on long-term cardiac toxicity.

Cryogenic die-less spinning of aluminum alloy thin-walled curved components and microstructure evolution

Cryogenic die-less spinning was proposed to address wrinkling and cracking, which occur in traditional spinning, to form large thin-walled curved components. A finite element model for this technique was developed, and die-less spinning at room temperature (RT) and cryogenic temperature (CT) was conducted to determine its feasibility. The wall thickness and the hardness of the components were then measured, and the microstructure evolution was observed to evaluate the forming accuracy and quality of the components. The component could be formed entirely (with a height of 45 mm) at cryogenic temperature feeding in a single pass, but the component formed at room temperature was broken with a height of 17.2 mm. In addition, the component formed via a single-pass feed path had a large stress concentration area and poor wall thickness accuracy. Results indicate that cryogenic temperature and multi-pass feed path could improve the formability of the component. Owing to the suppression of dynamic recovery at cryogenic temperature, smaller substructures were formed, and more dislocations accumulated in the grain, causing a higher level of work hardening of the alloy. The dislocation density of the components formed via a single-pass feed path at CT, multi-pass feed path at CT, and multi-pass feed path at RT were 3.40 × 1013, 2.62 × 1013, and 1.97 × 1013, respectively. The plasticity of the alloy was significantly improved at cryogenic temperatures because of the increased critical dislocation density and the improved work hardening ability.

Dosimetric Impact of Interfractional Bladder and Trigone Deformations: A Post-Hoc Analysis of a Phase III Randomized Trial of MRI-Guided vs. CT-Guided Stereotactic Body Radiotherapy

<h3>Purpose/Objective(s)</h3> Emerging data suggest that dosimetry of the trigone may be more associated with post-SBRT urinary toxicity than whole bladder dosimetry. Herein, we quantify the dosimetric impact of interfractional deformations during SBRT for the whole bladder and trigone using on-board 0.35T MRI images. <h3>Materials/Methods</h3> Twenty consecutive patients treated with MRI-guided prostate SBRT (40 Gy/5 fractions) on the MRI arm of a phase III single-center randomized trial were included. All patients were encouraged to have a full bladder for treatment. Bladder and trigone structures were contoured on images obtained from a 0.35T simulation MRI and five on-board pre-treatment MRIs. Dice Similarity Coefficients (DSCs) and changes in volume, Dmax, and Dmean for the bladder and trigone were calculated. <h3>Results</h3> Overall, the trigone accounted for only 2% of the bladder volume. Median DSC for bladder was 0.83 (0.60-0.97) while the median DSC of trigone was only 0.39 (0-0.84) (p<0.001). No statistically significant changes in bladder or trigone volume were seen, though the trigone volume was significantly associated with bladder volume (p<0.0001). Quantitatively, the Dmax values for the bladder and trigone were relatively consistent, with only 2% relative change. In contrast, the Dmean values for the bladder and the trigone exhibited more variability (p<0.0001). <h3>Conclusion</h3> The trigone is an extremely small urinary substructure (∼2% of the bladder volume) that exhibits significant interfractional deformation. Minimal changes in Dmax for both structures are consistent with expectations given their high proximity to the target volume and the use of bladder preparation. However, constraints for the trigone are unexplored, and the very low DSC suggests that the relative volume of this substructure receiving higher doses (∼75-100% of the prescription dose) could vary substantially. A rigorous exploration of this concept for the full cohort of trial patients who completed MRI-guided SBRT is planned.

In vivo assessment of tissue-specific radiological parameters with intra- and inter-patient variation using dual-energy computed tomography.

Experimental in vivo determination of radiological tissue parameters of organs in the head and pelvis within a large patient cohort, expanding on the current standard human tissue database summarized in ICRU46. Relative electron density (RED), effective atomic number (EAN) and stopping-power ratio (SPR) were obtained from clinical dual-energy CT scans using a clinically validated DirectSPR implementation and organ segmentations of 107 brain-tumor (brain, brainstem, spinal cord, chiasm, optical nerve, lens) and 120 pelvic cancer patients (prostate, kidney, liver, bladder). The impact of contamination by surrounding tissues on the tissue parameters was reduced with a dedicated contour adaption routine. Tissue parameters were characterized regarding the cohort mean value as well as the variation within each patient (2σintra) and between patients (2σinter). For the brain, age-dependent differences were determined. For 10 organs, including 4 structures not listed in ICRU46, the mean RED, EAN and SPR as well as their respective intra- and inter-patient variation were determined. SPR intra-patient variation was higher than 1.3% (1.3-4.6%) in all organs and always exceeded the inter-patient variation of the organ mean SPR (0.6-2.1%). For the brain, a significant SPR variation between pediatric and non-pediatric patients was determined. Radiological tissue parameters in the head and pelvis were characterized in vivo for a large patient cohort using dual-energy CT. This reassesses parts of the current standard database defined in ICRU46, furthermore complementing the data described in literature by smaller substructures in the brain as well as by the quantification of organ-specific inter- and intra-patient variation.

The Morpho-kinematic Architecture of Super Star Clusters in the Center of NGC 253

The center of the nearby galaxy NGC 253 hosts a population of more than a dozen super star clusters (SSCs) that are still in the process of forming. The majority of the star formation of the burst is concentrated in these SSCs, and the starburst is powering a multiphase outflow from the galaxy. In this work, we measure the 350 GHz dust continuum emission toward the center of NGC 253 at 47 mas (0.8 pc) resolution using data from the Atacama Large Millimeter/submillimeter Array. We report the detection of 350 GHz (dust) continuum emission in the outflow for the first time, associated with the prominent South-West streamer. In this feature, the dust emission has a width of ≈8 pc, is located at the outer edge of the CO emission, and corresponds to a molecular gas mass of ∼(8–17)×106 M ⊙. In the starburst nucleus, we measure the resolved radial profiles, sizes, and molecular gas masses of the SSCs. Compared to previous work at the somewhat lower spatial resolution, the SSCs here break apart into smaller substructures with radii 0.4–0.7 pc. In projection, the SSCs, dust, and dense molecular gas appear to be arranged as a thin, almost linear, structure roughly 155 pc in length. The morphology and kinematics of this structure can be well explained as gas following x 2 orbits at the center of a barred potential. We constrain the morpho-kinematic arrangement of the SSCs themselves, finding that an elliptical, angular-momentum-conserving ring is a good description of both the morphology and kinematics of the SSCs.

Morphologies of intermetallic compound phases in Sn-Cu and Sn-Co peritectic alloys during directional solidification

The morphologies of intermetallic phases (IMCs) during directional solidification of the Sn-Cu (L+Cu3Sn→Cu6Sn5) and Sn-Co (L+CoSn→CoSn2) peritectic systems were analyzed. The primary Cu3Sn and peritectic Cu6Sn5 phases in Sn-Cu alloy are IMCs whose solubility ranges are narrow, while both the primary CoSn and peritectic CoSn2 phases in Sn-Co alloy are IMCs whose solubility ranges are nil in equilibrium condition. The experimental results before acid corrosion shows that the dendritic morphology of both the Cu6Sn5 and CoSn2 phases can be observed. The investigation on the local dendritic morphology after deep acid corrosion shows that these dendrites are composed of small sub-structures with faceted feature. Faceted growth of the primary Cu3Sn and CoSn phases is also confirmed, and a faceted to non-faceted transition in their morphologies is observed with increasing growth velocities. Further analysis shows that the dendritic morphology is formed in the solidified phases whose solubility range is larger during peritectic solidification.

Mutual enhancing learning-based automatic segmentation of CT cardiac substructure

Objective. Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can be a very time-intensive process, especially due to their different volume sizes and anatomical variations amongst different patients. In this work, a new deep learning (DL)-based mutual enhancing strategy is introduced for accurate and automatic segmentation, especially of smaller substructures such as coronary arteries. Approach. Our proposed method consists of three subnetworks: retina U-net, classification module, and segmentation module. Retina U-net is used as a backbone network architecture that aims to learn deep features from the whole heart. Whole heart feature maps from retina U-net are then transferred to four different sets of classification modules to generate classification localization maps of coronary arteries, great vessels, chambers of the heart, and valves of the heart. Each classification module is in sync with its corresponding subsequent segmentation module in a bootstrapping manner, allowing them to share their encoding paths to generate a mutual enhancing strategy. We evaluated our method on three different datasets: institutional CT datasets (55 subjects) 2) publicly available Multi-Modality Whole Heart Segmentation (MM-WHS) challenge datasets (120 subjects), and Automated Cardiac Diagnosis Challenge (ACDC) datasets (100 subjects). For institutional datasets, we performed five-fold cross-validation on training data (45 subjects) and performed inference on separate hold-out data (10 subjects). For each subject, 15 cardiac substructures were manually contoured by a resident physician and evaluated by an attending radiation oncologist. For the MM-WHS dataset, we trained the network on 100 datasets and performed an inference on a separate hold-out dataset with 20 subjects, each with 7 cardiac substructures. For ACDC datasets, we performed five-fold cross-validation on 100 datasets, each with 3 cardiac substructures. We compared the proposed method against four different network architectures: 3D U-net, mask R-CNN, mask scoring R-CNN, and proposed network without classification module. Segmentation accuracies were statistically compared through dice similarity coefficient, Jaccard, 95% Hausdorff distance, mean surface distance, root mean square distance, center of mass distance, and volume difference. Main results. The proposed method generated cardiac substructure segmentations with significantly higher accuracy (P < 0.05) for small substructures, especially for coronary arteries such as left anterior descending artery (CA-LADA) and right coronary artery (CA-RCA) in comparison to four competing methods. For large substructures (i.e. chambers of the heart), our method yielded comparable results to mask scoring R-CNN method, resulting in significantly (P < 0.05) improved segmentation accuracy in comparison to 3D U-net and mask R-CNN. Significance. A new DL-based mutual enhancing strategy was introduced for automatic segmentation of cardiac substructures. Overall results of this work demonstrate the ability of the proposed method to improve segmentation accuracies of smaller substructures such as coronary arteries without largely compromising the segmentation accuracies of larger substructures. Fast and accurate segmentations of up to 15 substructures can possibly be used as a tool to rapidly generate substructure segmentations followed by physicians’ reviews to improve clinical workflow.

Future Prospects of Positron Emission Tomography-Magnetic Resonance Imaging Hybrid Systems and Applications in Psychiatric Disorders.

A positron emission tomography (PET)–magnetic resonance imaging (MRI) hybrid system has been developed to improve the accuracy of molecular imaging with structural imaging. However, the mismatch in spatial resolution between the two systems hinders the use of the hybrid system. As the magnetic field of the MRI increased up to 7.0 tesla in the commercial system, the performance of the MRI system largely improved. Several technical attempts in terms of the detector and the software used with the PET were made to improve the performance. As a result, the high resolution of the PET–MRI fusion system enables quantitation of metabolism and molecular information in the small substructures of the brainstem, hippocampus, and thalamus. Many studies on psychiatric disorders, which are difficult to diagnose with medical imaging, have been accomplished using various radioligands, but only a few studies have been conducted using the PET–MRI fusion system. To increase the clinical usefulness of medical imaging in psychiatric disorders, a high-resolution PET–MRI fusion system can play a key role by providing important information on both molecular and structural aspects in the fine structures of the brain. The development of high-resolution PET–MR systems and their potential roles in clinical studies of psychiatric disorders were reviewed as prospective views in future diagnostics.

Development of Simplified Auto-Segmentable Functional Cardiac Atlas

PurposeThere is increasing evidence that radiation doses to cardiac substructures are associated with cardiac adverse events. Manual delineation of cardiac substructures is time-consuming, and auto-segmentation of cardiac substructure atlases has consequently been evaluated. However, proper automatic delineation of small substructures, such as the left anterior descending coronary artery, is challenging, and auto-segmentation of cardiac conduction system substructures has never been evaluated, despite multiple reports of radiation-induced arrhythmia after thoracic irradiations. The aim of this study was to propose and evaluate a simplified auto-segmentable functional cardiac atlas. Methods and MaterialsWe created a cardiac substructure atlas based on 20 computed tomography scans from patients with breast cancer comprising the 4 cardiac cavities, a high-risk cardiac zone as a left anterior descending coronary artery surrogate, and the 2 cardiac conduction nodes. Automatic delineation of this atlas by an atlas-based auto-segmentation algorithm was evaluated on a validation data set, consisting of 20 additional computed tomography scans. Dice similarity coefficients were used to evaluate the concordance level between the manual and the automatic contours; a dosimetric comparison between mean and maximum doses to the manual and to the auto-segmented substructures was additionally performed, based on intensity modulated radiation therapy treatment plans of the patients of the validation set. ResultsAverage dice similarity coefficient values were 0.78 for the 4 cardiac cavities, 0.65 for the high-risk cardiac zones, 0.56 for the sinoatrial node, and 0.15 for the atrioventricular node. Compared with manual contours, auto-segmented substructures were slightly smaller but the dosimetric parameters were similar. ConclusionsWe proposed a simplified functional cardiac atlas that included the cardiac conduction system and circumvented coronary delineation difficulties by using a surrogate high-risk cardiac zone. Most cardiac substructures were associated with acceptable atlas-based auto-segmentation properties. Such an atlas could be used for epidemiologic studies and for clinical practice.

Abstract P3-19-14: Development of simplified auto-segmentable functional cardiac atlas

Abstract Objective: There are increasing evidences that radiation doses to specific cardiac substructures are associated with cardiac adverse events. Manual delineation of cardiac substructures is time consuming, and auto-segmentation of cardiac substructure atlases has consequently been evaluated. However, proper delineation of small substructures, such as the left anterior descending coronary artery (LADCA), is challenging and conduction system substructures have never been considered, despite frequent reports of radiation-induced arrhythmias for thoracic irradiation. The aim of this study was to propose and evaluate a simplified auto-segmentable functional cardiac atlas. Methods: We created a cardiac substructure atlas from 20 breast cancer patients’ CT scans consisting of the four cardiac cavities, of a high-risk cardiac zone (HRCZ) as a LADCA surrogate and of the two cardiac conduction nodes. Performance evaluation of atlas-based auto-segmentation (ABAS) was evaluated on a validation data set consisting of 20 additional CT scans. Dice similarity coefficients (DSC) were used to evaluate the concordance level between the manual and the automatic segmentations. Results: The average duration of manual segmentation of the proposed cardiac atlas ranged between fifteen to twenty minutes, while ABAS lasted two minutes. The median DSC for the delineated cardiac substructures was 0.718. The highest similarity between manual and automatic segmentation was observed for the left ventricle with a median DSC of 0.87, while the lowest similarity between manual and automatic segmentation was observed for the NAV with a median DSC of 0.15. Regardless of the considered cardiac substructure, auto-segmentation tended to result into smaller volumes than manual segmentation. While the volumes were not statistically different for the left ventricle, the volumes were statistically different for the other substructures. While smaller, the auto-segmented NAV and SAN were systematically localized within the manual contours. The auto-segmented NAV could be approximated by a 1.6-cm sphere and the auto-segmented SAN by a 1.0-cm sphere. Conclusion: We proposed a simplified functional cardiac atlas, for which coronary delineation difficulties were circumvent using a surrogate high risk cardiac zone and cardiac conduction system was considered. Most cardiac substructures were associated with acceptable ABAS properties. Such atlas could be potentially evaluated for epidemiological studies and clinical practice. Table 1.performance of atlas-based auto-segmentation of the proposed simplified cardiac atlasCardiac substructureDSC Overlap index [range]Cardiac cavitiesLeft ventricle0.87 [0.57-0.92]Right ventricle0.76 [0.45-0.85]Left atria0.77 [0.61-0.84]Right atria0.76 [0.54-0.85]High risk cardiac zone0.65 [0.38-0.79]Cardiac conduction systemSinoatrial node0.56 [0.27-0.67]Atrio-ventricular node0.15 [0.00-0.35] Citation Format: Pierre Loap, Ludovic De Marzi, Krassen Kirov, Vincent Servois, Alain Fourquet, Abdelhafidh Khoubeyb, Youlia Kirova. Development of simplified auto-segmentable functional cardiac atlas [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-19-14.