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

{"title":"Demonstration of 1000 fps High-Speed Angiography (HSA) in Pre-Clinical In-vivo Rabbit Aneurysm Models During Flow-Diverter Treatment.","authors":"E A Vanderbilt, C Koenighsknecht, D Pionessa, C N Ionita, D R Bednarek, S Rudin, S V Setlur Nagesh","doi":"10.1117/12.3005678","DOIUrl":"10.1117/12.3005678","url":null,"abstract":"<p><p>High Speed Angiography (HSA) at 1000 fps is a novel interventional-imaging technique that was previously used to visualize changes in vascular flow details before and after flow-diverter treatment of cerebral aneurysms in in-vitro 3D printed models.¹ In this first pre-clinical work, we demonstrate the use of the HSA technique during flow-diverter treatment of in-vivo rabbit aneurysm models. An aneurysm was created in the right common carotid artery of each of two rabbits using previously published elastase aneurysm-creation methods.² A 5 French catheter was inserted into the femoral artery and moved to the aneurysm location under the guidance of standard-speed 10 fps Flat Panel Detector (FPD) fluoroscopy. Following this, a flow diverter stent was placed in the parent vessel covering the aneurysm neck and diverting the flow away from the aneurysm. HSA was performed before and after placement of the flow diverter using a 1000 fps CdTe photon-counting detector (Aries, Varex). The detector was mounted on a motorized changer and was used with a commercial x-ray c-arm system (Fig. 1). During these procedures Omnipaque iodinated contrast was injected into the aneurysm area using a computer-controlled injector at a steady rate of 50 ml/min or 70 ml/min depending on the rabbit to visualize blood flow detail. The contrast injection and x-ray image acquisition were synchronized manually. The x-ray image acquisition was for a duration of 1 second, from which 300 ms was used for velocity analysis during systole. Detailed differences in flow patterns in the region of interest (ROI) between pre and post flow-diverter deployment were visualized at the high frame rates. The Optical Flow (OF) method for velocity calculation was performed upon the acquired 1000 fps HSA image sequences to provide quantitative evaluation of flow.</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/PMC11559516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633815","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":"Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model.","authors":"Fanyang Yu, Rongguang Wang, Pratik Chaudhari, Christos Davatzikos","doi":"10.1117/12.3005434","DOIUrl":"10.1117/12.3005434","url":null,"abstract":"<p><p>Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.</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/PMC11034818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861819","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":"Novel high-stopping power scintillators for medical applications.","authors":"J Glodo, E van Loef, Y Wang, P Bhattacharya, L Soundara Pandian, U Shirwadkar, I Hubble, J Schott, M Muller","doi":"10.1117/12.3006480","DOIUrl":"10.1117/12.3006480","url":null,"abstract":"<p><p>Development of new scintillator materials is a continuous effort, which recently has been focused on materials with higher stopping power. Higher stopping power can be achieved if the compositions include elements such as Tl (Z=81) or Lu (Z=71), as the compounds gain higher densities and effective atomic numbers. In context of medical imaging this translates into high detection efficiency (count rates), therefore, better image quality (statistics, thinner films) or lower irradiation doses to patients in addition to lowering of cost. Many known scintillator hosts, commercial or in research stages, are alkali metal halides (Cs, K, Rb). Often these monovalent ions can be replaced with monovalent Tl. Since Tl has a higher atomic number than for example Cs (55), this increases the stopping power of modified compounds. A good example of an enhanced host is Ce doped Tl₂LaCl₅ (5.2 g/cm³), that mirrors less dense Ce doped K₂LaCl₅ (2.89 g/cm³). Tl substation also increased the luminosity to >60,000 ph/MeV, as it often leads to a reduction in the bandgap. Another example is the dual mode (gamma/neutron) Ce doped Cs₂LiYCl₆ scintillator (density 3.31 g/cm³). Substitution creates Ce doped Tl₂LiYCl₆ with density of 4.5 g/cm³, with much better stopping power and 20% higher light yield. Binary Tl-compounds are also of interest, although mostly they are semiconductors. Notable example of a scintillator is double doped TlCl with Be, I. This scintillator offers fast Cherenkov emission topped off with scintillation signal for achieving better energy resolution. Another family of interesting and dense compositions is based on Lu₂O₃ ceramics. Lu₂O₃ is one of the densest hosts (9.2 g/cm³) available offering high stopping power. Lu₂O₃ doped with Eu³⁺ is known to be a high luminosity scintillator, however, this emission is very slow (1-3 ms), which limits its utility. On the other hand, ultra-fast, 1 ns, scintillation can be achieved with the Yb³⁺ doping that can be used for timing or high count-rate applications. However, while fast, Yb³⁺ doped Lu₂O₃ has very low luminosity. Recently, we have shown a middle ground performance, with Lu₂O₃ doped with La³⁺. This composition generates scintillation with 1,000 ns decay time and up to 20,000 ph/MeV luminosity. Moreover, the material demonstrates very good energy resolution.</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/PMC11631204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815248","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":"GAN-Based Motion Artifact Correction of 3D MR Volumes Using an Image-to-Image Translation Algorithm.","authors":"Vishnu Vardhan Reddy Kanamata Reddy, Chandan Ganesh Bangalore Yogananda, Nghi C D Truong, Ananth J Madhuranthakam, Joseph A Maldjian, Baowei Fei","doi":"10.1117/12.3007743","DOIUrl":"10.1117/12.3007743","url":null,"abstract":"<p><p>The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes. Our methodology is substantiated through comprehensive experimentation involving a diverse set of motion artifact-affected MR volumes. The generated high-quality MR volumes have similar volumetric signatures comparable to motion-free MR volumes after motion correction. This underscores the significant potential of harnessing this 3D deep learning system to aid in the rectification of motion artifacts in brain MR volumes, highlighting a promising avenue for advanced clinical applications.</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/PMC11262355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749931","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":"End-to-end Deep Learning Restoration of GLCM Features from blurred and noisy images.","authors":"Yijie Yuan, J Webster Stayman, Grace J Gang","doi":"10.1117/12.3006205","DOIUrl":"10.1117/12.3006205","url":null,"abstract":"<p><p>Radiomics involves the quantitative analysis of medical images to provide useful information for a range of clinical applications including disease diagnosis, treatment assessment, etc. However, the generalizability of radiomics model is often challenged by undesirable variability in radiomics feature values introduced by different scanners and imaging conditions. To address this issue, we developed a novel dual-domain deep learning algorithm to recover ground truth feature values given known blur and noise in the image. The network consists of two U-Nets connected by a differentiable GLCM estimator. The first U-Net restores the image, and the second restores the GLCM. We evaluated the performance of the network on lung CT image patches in terms of both closeness of recovered feature values to the ground truth and accuracy of classification between normal and COVID lungs. Performance was compared with an image restoration-only method and an analytical method developed in previous work. The proposed network outperforms both methods, achieving GLCM with the lowest mean-absolute-error from ground truth. Recovered GLCM feature values from the proposed method, on average, is within 2.19% error to the ground truth. Classification performance using recovered features from the network closely matches the \"best case\" performance achieved using ground truth feature values. The deep learning method has been shown to be a promising tool for radiomics standardization, paving the way for more reliable and repeatable radiomics models.</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/PMC11377019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141966","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":"Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks.","authors":"Zhenyao Yan, Li Zhang, Quanzheng Li, Dufan Wu","doi":"10.1117/12.3005368","DOIUrl":"https://doi.org/10.1117/12.3005368","url":null,"abstract":"<p><p>We proposed a deep learning-based method for single-heartbeat 4D cardiac CT reconstruction, where a single cardiac cycle was split into multiple phases for reconstruction. First, we pre-reconstruct each phase using the projection data from itself and the neighboring phases. The pre-reconstructions are fed into a supervised registration network to generate the deformation fields between different phases. The deformation fields are trained so that it can match the ground truth images from the corresponding phases. The deformation fields are then used in the FBP-and-wrap method for motion-compensated reconstruction, where a subsequent network is used to remove residual artifacts. The proposed method was validated with simulation data from 40 4D cardiac CT scans and demonstrated improved RMSE and SSIM and less blurring compared to FBP and PICCS.</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/PMC11555688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633813","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":"Modeling human observer detection for varying data acquisition in undersampled MRI for two-alternative forced choice (2-AFC) and forced localization tasks.","authors":"Rehan Mehta, Tetsuya A Kawakita, Angel R Pineda","doi":"10.1117/12.3005839","DOIUrl":"10.1117/12.3005839","url":null,"abstract":"<p><p>Undersampling in the frequency domain (k-space) in MRI enables faster data acquisition. In this study, we used a fixed 1D undersampling factor of 5x with only 20% of the k-space collected. The fraction of fully acquired low k-space frequencies were varied from 0% (all aliasing) to 20% (all blurring). The images were reconstructed using a multi-coil SENSE algorithm. We used two-alternative forced choice (2-AFC) and the forced localization tasks with a subtle signal to estimate the human observer performance. The 2-AFC average human observer performance remained fairly constant across all imaging conditions. The forced localization task performance improved from the 0% condition to the 2.5% condition and remained fairly constant for the remaining conditions, suggesting that there was a decrease in task performance only in the pure aliasing situation. We modeled the average human performance using a sparse-difference of Gaussians (SDOG) Hotelling observer model. Because the blurring in the undersampling direction makes the mean signal asymmetric, we explored an adaptation for irregular signals that made the SDOG template asymmetric. To improve the observer performance, we also varied the number of SDOG channels from 3 to 4. We found that despite the asymmetry in the mean signal, both the symmetric and asymmetric models reasonably predicted the human performance in the 2-AFC experiments. However, the symmetric model performed slightly better. We also found that a symmetric SDOG model with 4 channels implemented using a spatial domain convolution and constrained to the possible signal locations reasonably modeled the forced localization human observer results.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12929 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11128320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155281","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":"Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography.","authors":"Kathleen Baur, Xin Xiong, Erickson Torio, Rose Du, Parikshit Juvekar, Reuben Dorent, Alexandra Golby, Sarah Frisken, Nazim Haouchine","doi":"10.1117/12.3006740","DOIUrl":"10.1117/12.3006740","url":null,"abstract":"<p><p>Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified. The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001526","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":"Adapting SAM to Histopathology Images for Tumor Bud Segmentation in Colorectal Cancer.","authors":"Ziyu Su, Wei Chen, Sony Annem, Usama Sajjad, Mostafa Rezapour, Wendy L Frankel, Metin N Gurcan, M Khalid Khan Niazi","doi":"10.1117/12.3006517","DOIUrl":"10.1117/12.3006517","url":null,"abstract":"<p><p>Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist's TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12933 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11099868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066462","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":"Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.","authors":"Olivia F Sandvold, Roland Proksa, Heiner Daerr, Amy E Perkins, Kevin M Brown, Nadav Shapira, Thomas Koehler, J Webster Stayman, Grace J Gang, Ravindra M Manjeshwar, Peter B Noël","doi":"10.1117/12.3006451","DOIUrl":"10.1117/12.3006451","url":null,"abstract":"<p><p>Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.</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/PMC11129556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141157581","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}