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Review of Cost Reduction Methods in Photoacoustic Computed Tomography

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arXiv 2019年

作者： Fatima, Afreen Kratkiewicz, Karl Manwar, Rayyan Zafar, Mohsin Zhang, Ruiying Huang, Bin Dadashzadesh, Neda Xia, Jun Avanaki, Mohammad Department of Biomedical Engineering Wayne State University DetroitMI United States Department of Electrical and Computer Engineering Wayne State University DetroitMI United States Edan Medical USA Sunnyvale United States 3339 Northwest Ave BellinghamWA United States R&D Fiber-Laser Department Coherent Inc. East GranbyCT United States Department of Biomedical Engineering State University of New York BuffaloNY United States Department of Neurology Wayne State University School of Medicine DetroitMI United States Molecular Imaging Program Barbara Ann Karmanos Cancer Institute Wayne State University DetroitMI United States

Photoacoustic Computed Tomography (PACT) is a major configuration of photoacoustic imaging, a hybrid noninvasive modality for both functional and molecular imaging. PACT has rapidly gained importance in the field of biomedical imaging due to superior performance as compared to conventional optical imaging counterparts. However, the overall cost of developing a PACT system is one of the challenges towards clinical translation of this novel technique. The cost of a typical commercial PACT system originates from optical source, ultrasound detector, and data acquisition unit. With growing applications of photoacoustic imaging, there is a tremendous demand towards reducing its cost. In this review article, we have discussed various approaches to reduce the overall cost of a PACT system, and provided a cost estimation to build a low-cost PACT system. Copyright © 2019, The Authors. All rights reserved.

关键词： Photoacoustic effect

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

Author Correction: Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries

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Nature communications 2021年第1期12卷 6827页

作者： Yong Suk Oh Jae-Hwan Kim Zhaoqian Xie Seokjoo Cho Hyeonseok Han Sung Woo Jeon Minsu Park Myeong Namkoong Raudel Avila Zhen Song Sung-Uk Lee Kabseok Ko Jungyup Lee Je-Sang Lee Weon Gi Min Byeong-Ju Lee Myungwoo Choi Ha Uk Chung Jongwon Kim Mengdi Han Jahyun Koo Yeon Sik Choi Sung Soo Kwak Sung Bong Kim Jeonghyun Kim Jungil Choi Chang-Mo Kang Jong Uk Kim Kyeongha Kwon Sang Min Won Janice Mihyun Baek Yujin Lee So Young Kim Wei Lu Abraham Vazquez-Guardado Hyoyoung Jeong Hanjun Ryu Geumbee Lee Kyuyoung Kim Seunghwan Kim Min Seong Kim Jungrak Choi Dong Yun Choi Quansan Yang Hangbo Zhao Wubin Bai Hokyung Jang Yongjoon Yu Jaeman Lim Xu Guo Bong Hoon Kim Seokwoo Jeon Charles Davies Anthony Banks Hyung Jin Sung Yonggang Huang Inkyu Park John A Rogers Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA. Department of Materials Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA. Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian People's Republic of China. Ningbo Institute of Dalian University of Technology Ningbo People's Republic of China. Department of Biomedical Engineering Texas A&M University College Station TX USA. Department of Mechanical Engineering Northwestern University Evanston IL USA. Advanced 3D Printing Technology Development Division Korea Atomic Energy Research Institute Daejeon Republic of Korea. Qualcomm Institute La Jolla CA USA. Sibel Health Inc Niles IL USA. Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Planning and Development Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Rehabilitation Medicine Pusan national university hospital Busan Republic of Korea. Department of Materials Science and Engineering KAIST Institute for The Nanocentury (KINC) Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Mechanical Engineering Kyung Hee University Yongin Republic of Korea. Department of Biomedical Engineering College of Future Technology Peking University Beijing People's Republic of China. School of Biomedical Engineering Korea University Seoul Republic of Korea. Interdisci

Generation of terahertz transients from Co2Fe0.4Mn0.6Si-Heusler-alloy/normal-metal nanobilayers excited by femtosecond optical pulses

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Physical Review Research 2021年第4期3卷 043025-043025页

作者： Sarah Heidtfeld Roman Adam Takahide Kubota Koki Takanashi Derang Cao Carolin Schmitz-Antoniak Daniel E. Bürgler Fangzhou Wang Christian Greb Genyu Chen Ivan Komissarov Hilde Hardtdegen Martin Mikulics Roman Sobolewski Shigemasa Suga Claus M. Schneider Research Centre Jülich Peter Grünberg Institute 52425 Jülich Germany Faculty of Physics University of Duisburg-Essen 47048 Duisburg Germany Institute for Materials Research Tohoku University Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai 980-8577 Japan College of Physics National Demonstration Center for Experimental Applied Physics Education Qingdao University Qingdao 266071 China Materials Science Graduate Program University of Rochester Rochester New York 14627 USA Laboratory for Laser Energetics University of Rochester Rochester New York 14623 USA Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Belarusian State University of Informatics and Radioelectronics Minsk 220013 Belarus Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons 52425 Jülich Germany Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka 567-0047 Japan Department of Physics University of California Davis Davis California 95616 USA

We generated pulses of electromagnetic radiation in the terahertz (THz) frequency range by optical excitation of Co2Fe0.4Mn0.6Si (CFMS)/normal-metal (NM) bilayer structures. The CFMS is a Heusler alloy showing a band gap in one spin channel and is therefore a half metal. We compared the THz emission efficiency in a systematic manner for four different CFMS/NM bilayers, where NM was either Pt, Ta, Cr, or Al. Our measurements show that the THz intensity is highest for a Pt capping. We also demonstrate the tunability of the THz amplitude by varying the magnetic field for all four bilayers. We attribute the THz generation to the inverse spin Hall effect. In order to investigate the role of the interface in THz generation, we measured the spin mixing conductance for each CFMS/NM bilayer using a ferromagnetic resonance method. We found that the spin-orbit coupling cannot completely describe the THz generation in the bilayers and that the spin transmission efficiency of the CFMS/NM interface and the spin diffusion length, as well as the oxidation of the NM layer, play crucial roles in the THz emission process.

关键词： Inverse spin Hall effect Magnetism Spin injection Spin pumping Spintronics Ferromagnetic resonance Terahertz time-domain spectroscopy

An efficient convolutional neural network for coronary heart disease prediction

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arXiv 2019年

作者： Dutta, Aniruddha Batabyal, Tamal Basu, Meheli Acton, Scott T. Department of Pathology & Molecular Medicine Queen’s University KingstonONK7L 3N6 Canada Haas School of Business University of California BerkeleyCA94704 United States Department of Electrical & Computer Engineering University of Virginia VA22904 United States Katz Graduate School of Business University of Pittsburgh PA15260 United States Department of Biomedical Engineering University of Virginia VA22904 United States Department of Neurology School of Medicine University of Virginia VA22904 United States

This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data are curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of predicting the occurrence of Coronary Heart Disease (CHD). While the majority of the existing machine learning models that have been used on this class of data are vulnerable to class imbalance even after the adjustment of class-specific weights, our simple two-layer CNN exhibits resilience to the imbalance with fair harmony in class-specific performance. In order to obtain significant improvement in classification accuracy under supervised learning settings, it is a common practice to train a neural network architecture with a massive data and thereafter, test the resulting network on a comparatively smaller amount of data. However, given a highly imbalanced dataset, it is often challenging to achieve a high class 1 (true CHD prediction rate) accuracy as the testing data size increases. We adopt a two-step approach: first, we employ least absolute shrinkage and selection operator (LASSO) based feature weight assessment followed by majority-voting based identification of important features. Next, the important features are homogenized by using a fully connected layer, a crucial step before passing the output of the layer to successive convolutional stages. We also propose a training routine per epoch, akin to a simulated annealing process, to boost the classification accuracy. Despite a 35:1 (Non-CHD:CHD) ratio in the NHANES dataset, the investigation confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD and 81.8% the absence of CHD cases on a testing data, which is 85.70% of the total dataset. This result signifies that the proposed architecture can be generalized to other studies in healthcare with a similar order of features and imbalances. While the recall valu

关键词： Convolution

Corrigendum to: Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

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Cerebral cortex (New York, N.Y. : 1991) 2021年第8期31卷 3974页

作者： Ru Kong Jingwei Li Csaba Orban Mert R Sabuncu Hesheng Liu Alexander Schaefer Nanbo Sun Xi-Nian Zuo Avram J Holmes Simon B Eickhoff B T Thomas Yeo Department of Electrical and Computer Engineering ASTAR-NUS Clinical Imaging Research Centre Singapore Institute for Neurotechnology and Memory Networks Program National University of Singapore Singapore. School of Electrical and Computer Engineering Cornell University Ithaca NY USA. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. CAS Key Laboratory of Behavioral Sciences and Research Center for Lifespan Development of Brain and Mind (CLIMB) Institute of Psychology Beijing China. University of Chinese Academy of Sciences Beijing China. Department of Psychology Yale University New Haven CT USA. Institute for Systems Neuroscience Medical Faculty Heinrich-Heine University Düsseldorf Düsseldorf Germany. Institute of Neuroscience and Medicine Brain & Behaviour (INM-7) Research Center Jülich Jülich Germany. Centre for Cognitive Neuroscience DukeNUS Medical School Singapore. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore.

Training on the test set? An analysis of Spampinato et al.

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arXiv 2018年

作者： Li, Ren Johansen, Jared S. Ahmed, Hamad Ilyevsky, Thomas V. Wilbur, Ronnie B. Bharadwaj, Hari M. Siskind, Jeffrey Mark Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering Department of Speech Language and Hearing Sciences and the Linguistics Program Weldon School of Biomedical Engineering and the Department of Speech Language and Hearing Sciences

A recent paper [31] claims to classify brain processing evoked in subjects watching ImageNet stimuli as measured with EEG and to employ a representation derived from this processing to construct a novel object classifier. That paper, together with a series of subsequent papers [8, 15, 17, 20, 21, 30, 35], claims to revolutionize the field by achieving extremely successful results on a wide variety of computer-vision tasks, including object classification, transfer learning, and generation of images depicting human perception and thought using brain-derived representations measured through EEG. Our novel experiments and analyses demonstrate that their results crucially depend on the block design that they employ, where all stimuli of a given class are presented together, and fail with a rapid-event design, where stimuli of different classes are randomly intermixed. The block design leads to classification of arbitrary brain states based on block-level temporal correlations that tend to exist in all EEG data, rather than stimulus-related activity. Because every trial in their test sets comes from the same block as many trials in the corresponding training sets, their block design thus leads to surreptitiously training on the test set. This invalidates all subsequent analyses performed on this data in multiple published papers and calls into question all of the purported results. We further show that a novel object classifier constructed with a random codebook performs as well as or better than a novel object classifier constructed with the representation extracted from EEG data, suggesting that the performance of their classifier constructed with a representation extracted from EEG data does not benefit at all from the brain-derived representation. Our results calibrate the underlying difficulty of the tasks involved and caution against sensational and overly optimistic, but false, claims to the contrary. Copyright © 2018, The Authors. All rights reserved.

关键词： Neuroimaging

Characterization of the mitogenome ofCynoglossus Senegalensis(Pleuronectiformes: Cynoglossidae)

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Mitochondrial DNA Part B 2018年第2期3卷 1191-1192页

作者： Fantong Zealous Gietbong Nack-Keun Kim Sapto Andriyono Hyun-Woo Kim Department of Marine Biology Pukyong National University Busan Republic of KoreaKOICA-PKNU International Graduate Program of Fisheries Science Graduate School of Global Fisheries Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaDepartment of Marine Fisheries and Marine Faculty Universitas Airlangga C Campus Jl. Mulyorejo Surabaya East Java Indonesia Department of Marine Biology Pukyong National University Busan Republic of KoreaInterdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaKOICA-PKNU International Graduate Program of Fisheries Science Graduate School of Global Fisheries Pukyong National University Busan Republic of Korea

The complete mitogenome of theSenegalese tonguesole,Cynoglossus senegalensiswas determined by Illumina MiSeq platform. The complete mitochondrial genome ofC. senegalensiswas 16,519 bp in length. The mitochondrial genome ofC. senegalensisshowed a cynoglosidae-characteristic gene organization, in which translocation of control region to the position between ND1 and tRNA-Gln gene, and also inversions in tRNA-Gln gene from L-strand position to H-strand position. Phylogenetic analysis showed thatC. senegalensisis most closely related toC. ***, which supports the previous result that genus Cynoglossidae is evolutionary paraphyletic.

关键词： Mitochondrial genome Cynoglossus senegalensis sole Cameroon Africa

Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path planning

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Multimodal connectivity based eloquence score computation an...

作者： Bakhshmand, Saeed M. Eagleson, Roy De Ribaupierre, Sandrine Biomedical Engineering Graduate Program University of Western Ontario LondonON Canada Department of Electrical and Computer Engineering University of Western Ontario LondonON Canada Department of Clinical Neurological Sciences University of Western Ontario LondonON Canada

Non-invasive assessment of cognitive importance has been a major challenge for planning of neurosurgical procedures. In the past decade, in vivo brain imaging modalities have been considered for estimating the 'eloquence' of brain areas. In order to estimate the impact of damage caused by an access path towards a target region inside of the skull, multi-modal metrics are introduced in this paper. Accordingly, this estimated damage is obtained by combining multi-modal metrics. In other words, this damage is an aggregate of intervened grey matter volume and axonal fibre numbers, weighted by their importance within the assigned anatomical and functional networks. To validate these metrics, an exhaustive search algorithm is implemented for characterising the solution space and visually representing connectional cost associated with a path initiated from underlying points. In this presentation, brain networks are built from resting state functional magnetic resonance imaging (fMRI) and deterministic tractography. their results demonstrate that the proposed approach is capable of refining traditional heuristics, such as choosing the minimal distance from the lesion, by supplementing connectional importance of the resected tissue. This provides complementary information to help the surgeon in avoiding important functional hubs and their anatomical linkages;which are derived from neuroimaging modalities and incorporated to the related anatomical landmarks.

关键词： Motion planning