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

{"title":"Predicting Age from White Matter Diffusivity with Residual Learning.","authors":"Chenyu Gao, Michael E Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy R Newlin, Derek B Archer, Angela L Jefferson, Warren D Taylor, Brian D Boyd, Lori L Beason-Held, Susan M Resnick, Yuankai Huo, Katherine D Van Schaik, Kurt G Schilling, Daniel Moyer, Ivana Išgum, Bennett A Landman","doi":"10.1117/12.3006525","DOIUrl":"10.1117/12.3006525","url":null,"abstract":"<p><p>Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.</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/PMC11415267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302988","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":"Evaluation of aneurysm flow divertor (stent) treatment using multi-angled 1000 fps High-Speed Angiography (HSA) and Optical Flow (OF).","authors":"E A Vanderbilt, R White, S V Setlur Nagesh, V K Chivukula, D R Bednarek, C N Ionita, S Rudin","doi":"10.1117/12.3006922","DOIUrl":"10.1117/12.3006922","url":null,"abstract":"<p><p>Understanding detailed hemodynamics is critical in the treatment of aneurysms and other vascular diseases; however, traditional digital subtraction angiography (DSA) does not provide detailed quantitative flow information. Instead, 1000 fps High-Speed Angiography (HSA) can be used for high-temporal visualization and evaluation of detailed blood flow patterns and velocity distributions. In the treatment of aneurysms, flow diverter expansion and positioning play a critical role in affecting the hemodynamics and optimal patient outcomes. Patient-specific aneurysm phantom imaging was done with a CdTe photon-counting detector (Aries, Varex). Treatment was done with a Pipeline Flex Embolization Device on a 3D-printed fusiform aneurysm phantom. The untreated aneurysm and two treatment stent expansions and positions were imaged, and velocity calculations were generated using Optical Flow (OF). Pre- and post-treatment images were then compared between different HSA image sequences and evaluated using OF with different stent positions. Differences in flow patterns due to changes in stent placement characteristics were identified and quantified with OF velocimetry. The velocity results within the aneurysm post-treatment showed significant flow reduction. Differences in stent placement result in substantial changes in velocities. The peak velocities found in the aneurysm dome show a reduction with the widened stent placement compared to the narrowed placement and both are reduced compared to the untreated aneurysm. The stent placements were compared quantitatively with the adjusted widened stent clearly better diverting the flow away from the aneurysm with decreased velocity in the aneurysm dome compared to both the narrowed stent placement and the untreated aneurysm. Providing this information in-clinic can help improve treatment and patient outcomes.</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/PMC11533909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577167","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":"Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.","authors":"Teja Pathour, Ling Ma, Douglas W Strand, Jeffrey Gahan, Brett A Johnson, Shashank R Sirsi, Baowei Fei","doi":"10.1117/12.3008643","DOIUrl":"https://doi.org/10.1117/12.3008643","url":null,"abstract":"<p><p>Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using <i>ex-vivo</i> whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12932 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11069342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859306","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":"Lifelike <i>PixelPrint</i> phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm.","authors":"Jessica Y Im, Sandra S Halliburton, Kai Mei, Amy E Perkins, Eddy Wong, Leonid Roshkovan, Grace J Gang, Peter B Noël","doi":"10.1117/12.3006547","DOIUrl":"10.1117/12.3006547","url":null,"abstract":"<p><p>Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDI_vol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.</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/PMC11148728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249236","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":"Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.","authors":"Amar Kavuri, Fong Chi Ho, Mobina Ghojogh-Nejad, Saman Sotoudeh-Paima, Ehsan Samei, W Paul Segars, Ehsan Abadi","doi":"10.1117/12.3006833","DOIUrl":"10.1117/12.3006833","url":null,"abstract":"<p><p>Parametric response mapping (PRM) is a voxel-based quantitative CT imaging biomarker that measures the severity of chronic obstructive pulmonary disease (COPD) by analyzing both inspiratory and expiratory CT scans. Although PRM-derived measurements have been shown to predict disease severity and phenotyping, their quantitative accuracy is impacted by the variability of scanner settings and patient conditions. The aim of this study was to evaluate the variability of PRM-based measurements due to the changes in the scanner types and configurations. We developed 10 human chest models with emphysema and air-trapping at end-inspiration and end-expiration states. These models were virtually imaged using a scanner-specific CT simulator (DukeSim) to create CT images at different acquisition settings for energy-integrating and photon-counting CT systems. The CT images were used to estimate PRM maps. The quantified measurements were compared with ground truth values to evaluate the deviations in the measurements. Results showed that PRM measurements varied with scanner type and configurations. The emphysema volume was overestimated by 3 ± 9.5 % (mean ± standard deviation) of the lung volume, and the functional small airway disease (fSAD) volume was underestimated by 7.5±19 % of the lung volume. PRM measurements were more accurate and precise when the acquired settings were photon-counting CT, higher dose, smoother kernel, and larger pixel size. This study demonstrates the development and utility of virtual imaging tools for systematic assessment of a quantitative biomarker accuracy.</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/PMC11100024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066491","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":"Improving Stenosis Assessment in Energy Integrating Detector CT via Learned Monoenergetic Imaging Capability.","authors":"Shaojie Chang, Emily K Koons, Hao Gong, Jamison E Thorne, Cynthia H McCollough, Shuai Leng","doi":"10.1117/12.3006468","DOIUrl":"https://doi.org/10.1117/12.3006468","url":null,"abstract":"<p><p>Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EID-CT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely with ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.</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/PMC11014427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874090","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":"Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.","authors":"Hanliang Xu, Nancy R Newlin, Michael E Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind R Krishnan, Lucas W Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy J Hohman, Angela L Jefferson, Ivana Isgum, Yuankai Huo, Daniel Moyer, Kurt G Schilling, Bennett A Landman","doi":"10.1117/12.3005563","DOIUrl":"10.1117/12.3005563","url":null,"abstract":"<p><p>Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.</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/PMC11415266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302985","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":"Assessment of Subject Head Motion in Diffusion MRI.","authors":"Ema Topolnjak, Chenyu Gao, Lori L Beason-Held, Susan M Resnick, Kurt G Schilling, Bennett A Landman","doi":"10.1117/12.3006633","DOIUrl":"10.1117/12.3006633","url":null,"abstract":"<p><p>Subject head motion during the acquisition of diffusion-weighted imaging (DWI) of the brain induces artifacts and affects image quality. Information about the frequency and extent of motion could reveal which aspects of motion correction are most necessary. Therefore, we investigate the extent of translation and rotation among participants, and how the motion changes during the scan acquisition. We analyze 5,380 DWI scans from 1,034 participants. We measure the rotations and translations in the sagittal, coronal and transverse planes needed to align the volumes to the first and previous volumes, as well as the displacement. The different types of motion are compared with each other and compared over time. The largest rotation (per minute) is around the right - left axis (median 0.378 °/min, range 0.000 - 11.466°) and the largest translation (per minute) is along the anterior - posterior axis (median 1.867 mm/min, range 0.000 - 10.944 mm). We additionally observe that spikes in movement occur at the beginning of the scan, particularly in anterior - posterior translation. The results show that all scans are affected by subtle head motion, which may impact subsequent image analysis.</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/PMC11364405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115749","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":"First In-Vivo demonstration of 1000fps High Speed Coronary Angiography (HSCA) in a swine animal model.","authors":"S V Setlur Nagesh, E Vanderbilt, C Koenigsknecht, D Pionessa, V K Chivukula, C N Ionita, David M Zlotnick, D R Bednarek, S Rudin","doi":"10.1117/12.3006858","DOIUrl":"10.1117/12.3006858","url":null,"abstract":"<p><p>High-speed-angiography (HSA) 1000 fps imaging was successfully used previously to visualize contrast media/blood flow in neurovascular anatomies. In this work we explore its usage in cardiovascular anatomies in a swine animal model. A 5 French catheter was guided into the right coronary artery of a swine, followed by the injection of iodine contrast through a computer-controlled injector at a controlled rate of 40 (ml/min). The injection process was captured using high-speed angiography at a rate of 1000 fps. The noise in the images was reduced using a custom built machine-learning model consisting of Long Short-term memory networks. From the noise reduced images, velocity profiles of contrast/blood flow through the artery was calculated using Horn-Schunck optical flow (OF) method. From the high-speed coronary angiography (HSCA) images, the bolus of contrast could be visually tracked with ease as it traversed from the catheter tip through the artery. The imaging technique's high temporal resolution effectively minimized motion artifacts resulting from the heart's activity. The OF results of the contrast injection show velocities in the artery ranging from 20 - 40 cm/s. The results demonstrate the potential of 1000 fps HSCA in cardiovascular imaging. The combined high spatial and temporal resolution offered by this technique allows for the derivation of velocity profiles throughout the artery's structure, including regions distal and proximal to stenoses. This information can potentially be used to determine the need for stenoses treatment. Further investigations are warranted to expand our understanding of the applications of HSCA in cardiovascular research and clinical practice.</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/PMC11492795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482677","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":"Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.","authors":"Emily K Koons, Hao Gong, Andrew Missert, Shaojie Chang, Tim Winfree, Zhongxing Zhou, Cynthia H McCollough, Shuai Leng","doi":"10.1117/12.3006463","DOIUrl":"https://doi.org/10.1117/12.3006463","url":null,"abstract":"<p><p>Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (<b>I</b>mproved <b>LUMEN</b> visualization through <b>A</b>rtificial super-resolu<b>T</b>ion imag<b>E</b>s), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.</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/PMC11008336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866975","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}