Proceedings of SPIE--the International Society for Optical Engineering最新文献

{"title":"Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets.","authors":"Sayantan Bhadra, Jianfei Liu, Ronald M Summers","doi":"10.1117/12.3008793","DOIUrl":"10.1117/12.3008793","url":null,"abstract":"<p><p>Volumetric assessment of edema due to anasarca can help monitor the progression of diseases such as kidney, liver or heart failure. The ability to measure edema non-invasively by automatic segmentation from abdominal CT scans may be of clinical importance. The current state-of-the-art method for edema segmentation using intensity priors is susceptible to false positives or under-segmentation errors. The application of modern supervised deep learning methods for 3D edema segmentation is limited due to challenges in manual annotation of edema. In the absence of accurate 3D annotations of edema, we propose a weakly supervised learning method that uses edema segmentations produced by intensity priors as pseudo-labels, along with pseudo-labels of muscle, subcutaneous and visceral adipose tissues for context, to produce more refined segmentations with demonstrably lower segmentation errors. The proposed method employs nnU-Nets in multiple stages to produce the final edema segmentation. The results demonstrate the potential of weakly supervised learning using edema and tissue pseudo-labels in improved quantification of edema for clinical applications.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12927 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI.","authors":"Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C-C Jay Kuo, Georges El Fakhri, Jonghye Woo","doi":"10.1117/12.3006897","DOIUrl":"https://doi.org/10.1117/12.3006897","url":null,"abstract":"<p><p>In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12927 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-source photon-counting CT: impact of residual cross-scatter on quantitative spectral results.","authors":"Leening P Liu, Edgar Salazar, Pooyan Sahbaee, Harold I Litt, Peter B Noël","doi":"10.1117/12.3006109","DOIUrl":"10.1117/12.3006109","url":null,"abstract":"<p><p>Dual-source photon-counting CT combines the high temporal resolution and high pitch of dual-source CT with the material quantification capabilities of photon-counting CT. It, however, results in cross-scatter that increases in severity with increased patient size and collimation. This cross-scatter must be corrected to ensure the removal of scatter artifacts and improve quantitative accuracy. To evaluate residual cross-scatter of a first-generation dual-source photon-counting CT and the effect of phantom size, collimation, and radiation dose, a phantom was scanned in single- and dual-source modes with and without its extension ring at three collimations and three radiation doses. Virtual monoenergetic images (VMI) at 50 keV, VMI 150 keV, and iodine density maps were reconstructed to determine variation between acquisition parameters in single- and dual-source modes. Additionally, differences relative to single-source acquisitions and to single-source and small collimation acquisitions were calculated to reflect residual cross-scatter with and without matched collimation. At VMI 50 keV, inserts exhibited accuracy and similar variation between single- and dual-source modes, averaging 5.4 ± 2.6 and 6.2 ± 2.5 HU, respectively, across phantom size, collimation, and radiation dose. Differences relative to single-source measured 5.1 ± 8.5 and 0.4 ± 4.2 HU while differences relative to single-source and small collimation acquisitions were 6.4 ± 10.8 HU and -0.5 ± 3.9 HU for VMI 50 and 150 keV, respectively. This minimal residual cross-scatter increases confidence in the quantitative accuracy of spectral results necessary for clinical applications of dual-source photon-counting CT with motion, such as cardiac imaging.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT.","authors":"Saman Sotoudeh-Paima, Fong Chi Ho, Mobina Ghojogh Nejad, Amar Kavuri, Bryan O'Sullivan-Murphy, David A Lynch, W Paul Segars, Ehsan Samei, Ehsan Abadi","doi":"10.1117/12.3006925","DOIUrl":"10.1117/12.3006925","url":null,"abstract":"<p><p>Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0-5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11090051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tuberculosis Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions.","authors":"Bradley C Lowekamp, Andrei Gabrielian, Darrell E Hurt, Alex Rosenthal, Ziv Yaniv","doi":"10.1117/12.3006848","DOIUrl":"https://doi.org/10.1117/12.3006848","url":null,"abstract":"<p><p>The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, estimated at 450,000 in 2021. This is concerning, as treatment of patients with drug resistant TB is complex and may not always be successful. The NIAID TB Portals program is an international consortium with a primary focus on patient centric data collection and analysis for drug resistant TB. The data includes images, their associated radiological findings, clinical records, and socioeconomic information. This work describes a TB Portals' Chest X-ray based image retrieval system which enables precision medicine. An input image is used to retrieve similar images and the associated patient specific information, thus facilitating inspection of outcomes and treatment regimens from comparable patients. Image similarity is defined using clinically relevant biomarkers: gender, age, body mass index (BMI), and the percentage of lung affected per sextant. The biomarkers are predicted using variations of the DenseNet169 convolutional neural network. A multi-task approach is used to predict gender, age and BMI incorporating transfer learning from an initial training on the NIH Clinical Center CXR dataset to the TB portals dataset. The resulting gender AUC, age and BMI mean absolute errors were 0.9854, 4.03years and <math><mrow><mn>1.67</mn><mfrac><mrow><mi>k</mi><mi>g</mi></mrow><mrow><msup><mi>m</mi><mn>2</mn></msup></mrow></mfrac></mrow></math>. For the percentage of sextant affected by lesions the mean absolute errors ranged between 7% to 12% with higher error values in the middle and upper sextants which exhibit more variability than the lower sextants. The retrieval system is currently available from https://rap.tbportals.niaid.nih.gov/find_similar_cxr.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12931 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11016336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nerve Detection and Visualization Using Hyperspectral Imaging for Surgical Guidance.","authors":"Minh Ha Tran, Michelle Bryarly, Ling Ma, Muhammad Saad Yousuf, Theodore J Price, Baowei Fei","doi":"10.1117/12.3008470","DOIUrl":"10.1117/12.3008470","url":null,"abstract":"<p><p>During surgery of delicate regions, differentiation between nerve and surrounding tissue is crucial. Hyperspectral imaging (HSI) techniques can enhance the contrast between types of tissue beyond what the human eye can differentiate. Whereas an RGB image captures 3 bands within the visible light range (<i>e.g.,</i> 400 nm to 700 nm), HSI can acquire many bands in wavelength increments that highlight regions of an image across a wavelength spectrum. We developed a workflow to identify nerve tissues from other similar tissues such as fat, bone, and muscle. Our workflow uses spectral angle mapper (SAM) and endmember selection. The method is robust for different types of environment and lighting conditions. We validated our workflow on two samples of human tissues. We used a compact HSI system that can image from 400 to 1700 nm to produce HSI of the samples. On these two samples, we achieved an intersection-over-union (IoU) segmentation score of 84.15% and 76.73%, respectively. We showed that our workflow identifies nerve segments that are not easily seen in RGB images. This method is fast, does not rely on special hardware, and can be applied in real time. The hyperspectral imaging and nerve detection approach may provide a powerful tool for image-guided surgery.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11070131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.","authors":"Nghi C D Truong, Chandan Ganesh Bangalore Yogananda, Benjamin C Wagner, James M Holcomb, Divya Reddy, Niloufar Saadat, Kimmo J Hatanpaa, Toral R Patel, Baowei Fei, Matthew D Lee, Rajan Jain, Richard J Bruce, Marco C Pinho, Ananth J Madhuranthakam, Joseph A Maldjian","doi":"10.1117/12.3009331","DOIUrl":"10.1117/12.3009331","url":null,"abstract":"<p><p>Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition. The framework comprises two components: a 3D autoencoder model for perceptual compression and a conditional 3D Diffusion Probabilistic Model (DPM) for generating high-quality and diverse multi-contrast brain tumor MRI samples, guided by a conditional tumor mask. Unlike existing works that focused on generating either 2D multi-contrast or 3D single-contrast MRI samples, our models generate multi-contrast 3D MRI samples. We also integrated a conditional module within the UNet backbone of the DPM to capture the semantic class-dependent data distribution driven by the provided tumor mask to generate MRI brain tumor samples based on a specific brain tumor mask. We trained our models using two brain tumor datasets: The Cancer Genome Atlas (TCGA) public dataset and an internal dataset from the University of Texas Southwestern Medical Center (UTSW). The models were able to generate high-quality 3D multi-contrast brain tumor MRI samples with the tumor location aligned by the input condition mask. The quality of the generated images was evaluated using the Fréchet Inception Distance (FID) score. This work has the potential to mitigate the scarcity of brain tumor data and improve the performance of deep learning models involving brain tumor MRI data.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12931 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11075745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140878060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of scatter suppression with 2D antiscatter grids in photon counting compact CBCT.","authors":"Ryan Sabounchi, Uttam Pyakurel, Farhang Bayat, Mohamed Eldib, Cem Altunbas","doi":"10.1117/12.3006524","DOIUrl":"10.1117/12.3006524","url":null,"abstract":"<p><p>Energy sensitive and photon counting detectors can provide improved tissue visualization and material quantification capabilities in Cone Beam Computed Tomography (CBCT) systems. However, their implementation in CBCT systems is more challenging, which is in part due to high fluence of scattered X-rays in wide cone angle CBCT geometry. Specifically, high scatter contamination in lower energy spectrum challenges reconstruction of high fidelity CBCT images by using lower energy X-rays. To address this problem, we investigated a robust scatter rejection with 2D antiscatter grids in a benchtop photon counting and compact CBCT system. The benchtop system employs a 35 cm wide CdTe photon counting detector with two energy thresholds. To reject scatter, a dedicated 2D antiscatter grid (2D grid) prototype made from tungsten was developed and mounted directly on the detector. To correct residual scatter not stopped by the 2D grid, a measurement-based scatter correction method, referred to as Grid-based Scatter Sampling (GSS), was utilized. Without 2D grid, scatter to primary ratio (SPR) reached 2.3 in the 15-40 keV energy bin. SPR was factor of 3 higher in the lowest energy bin when compared to the highest energy bin (90-120 keV). With the 2D grid, SPR was reduced below 0.14, and SPR values were more homogenous across the energy spectrum. CT number nonuniformity was factor of 3 lower in both low and high energy bin CBCT reconstructions. Improvement in contrast to noise ratio and contrast was more pronounced in the low energy bin CBCT images. This work indicates that 2D grids can significantly reduce spectral contamination caused by scatter in photon counting compact CBCT, and potentially enable higher fidelity CBCT image reconstructions.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11045167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Onward to Better Surgery - the Critical Need for Improved Ex Vivo Testing and Training Methods.","authors":"Eric R Henderson, Ryan Halter, Keith D Paulsen, Brian W Pogue, Jonathan Elliott, Ethan LaRochelle, Alberto Ruiz, Shudong Jiang, Samuel S Streeter, Kimberley S Samkoe, Summer Gibbs","doi":"10.1117/12.3010121","DOIUrl":"10.1117/12.3010121","url":null,"abstract":"<p><p>Guided surgery has demonstrated significant improvements in patient outcomes in some disease processes. Interest in this field has led to substantial growth in the technologies under investigation. Most likely no single technology will prove to be \"best,\" and combinations of macro- and microscale guidance-using radiological imaging navigation, probes (activatable, perfusion, and molecular-targeted; large- and small-molecule), autofluorescence, tissue intrinsic optical properties, bioimpedance, and other characteristics-will offer patients and surgeons the greatest opportunity for high-success/low-morbidity medical interventions. Problems are arising, however, from the lack of valid testing formats; surgical training simulators suffer the same problems. Small animal models do not accurately recreate human anatomy, especially in terms of tissue volume. Large animal models are expensive and have difficulty replicating many pathological states, particularly when molecular specificity for individual cancers is required. Furthermore, the sheer number of technologies and the potential for synergistic combination leads to exponential growth of testing requirements that is unrealistic for in vivo testing. Therefore, critical need exists to expand the ex vivo/in vitro testing platforms available to investigators and, once validated, a need to increase the acceptance of these methods for funding and regulatory endpoints. Herein is a review of the available ex vivo/in vitro testing formats for guided surgery, a review of their advantages/disadvantages, and consideration for how our field may safely and more swiftly move forward through stronger adoption of these testing and validation methods.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12825 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-photon excitation two-dimensional fluorescence spectroscopy (2PE-2DFS) of the fluorescent nucleobase 6-MI.","authors":"Claire S Albrecht, Lawrence F Scatena, Peter H von Hippel, Andrew H Marcus","doi":"10.1117/12.3001802","DOIUrl":"10.1117/12.3001802","url":null,"abstract":"<p><p>Base stacking is fundamentally important to the stability of double-stranded DNA. However, few experiments can directly probe the local conformations and conformational fluctuations of the DNA bases. Here we report a new spectroscopic approach to study the local conformations of DNA bases using the UV-absorbing fluorescent guanine analogue, 6-methyl isoxanthopterin (6-MI), which can be used as a site-specific probe to label DNA. In these experiments, we apply a two-photon excitation (2PE) approach to two-dimensional fluorescence spectroscopy (2DFS), which is a fluorescence-detected nonlinear Fourier transform spectroscopy. In 2DFS, a repeating sequence of four collinear laser pulses (with center wavelength ~ 675 nm and relative phases swept at radio frequencies) is used to excite the lowest energy electronic-vibrational (vibronic) transitions of 6-MI (with center wavelength ~ 340 nm). The ensuing low flux fluorescence is phase-synchronously detected at the level of individual photons and as a function of inter-pulse delay. The 2PE transition pathways that give rise to electronically excited state populations include optical coherences between electronic ground and excited states and non-resonant (one-photon-excited) virtual states. Our results indicate that 2PE-2DFS experiments can provide information about the electronic-vibrational spectrum of the 6-MI monomer, in addition to the conformation-dependent exciton coupling between adjacent 6-MI monomers within a (6-MI)₂ dimer. In principle, this approach can be used to determine the local base-stacking conformations of (6-MI)₂ dimer-substituted DNA constructs.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12863 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}