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110


Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction

Schramm, Georg; Filipovic, Marina; Qian, Yongxian; Alivar, Alaleh; Lui, Yvonne W; Nuyts, Johan; Boada, Fernando
PURPOSE/OBJECTIVE:Na images. METHODS:Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS:Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION/CONCLUSIONS:Na MR imaging at 3T.
PMID: 38044789
ISSN: 1522-2594
CID: 5597582

Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction

Chung, Sohae; Fieremans, Els; Novikov, Dmitry S; Lui, Yvonne W
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
PMCID:10690318
PMID: 38045398
CID: 5597642

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

Filippi, C G; Stein, J M; Wang, Z; Bakas, S; Liu, Y; Chang, P D; Lui, Y; Hess, C; Barboriak, D P; Flanders, A E; Wintermark, M; Zaharchuk, G; Wu, O
In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.
PMCID:10631523
PMID: 37652578
ISSN: 1936-959x
CID: 5590102

Thalamocortical coherence predicts persistent postconcussive symptoms

Li, Yi-Tien; Kuo, Duen-Pang; Tseng, Philip; Chen, Yung-Chieh; Cheng, Sho-Jen; Wu, Changwei W; Hsieh, Li-Chun; Chiang, Yung-Hsiao; Chung, Hsiao-Wen; Lui, Yvonne W; Chen, Cheng-Yu
The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.
PMID: 37169275
ISSN: 1873-5118
CID: 5507992

Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes

Chen, Junbo; Chung, Sohae; Li, Tianhao; Fieremans, Els; Novikov, Dmitry S; Wang, Yao; Lui, Yvonne W
PURPOSE/OBJECTIVE:Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS:36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS:Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION/CONCLUSIONS:) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.
PMID: 37212469
ISSN: 2385-1996
CID: 5543572

Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System

Pham, N; Hill, V; Rauschecker, A; Lui, Y; Niogi, S; Fillipi, C G; Chang, P; Zaharchuk, G; Wintermark, M
Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.
PMID: 37080722
ISSN: 1936-959x
CID: 5466292

Author Correction: Federated learning enables big data for rare cancer boundary detection

Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pandey, Umang; Saini, Jitender; Garrett, John; Larson, Matthew; Jeraj, Robert; Currie, Stuart; Frood, Russell; Fatania, Kavi; Huang, Raymond Y; Chang, Ken; Balaña, Carmen; Capellades, Jaume; Puig, Josep; Trenkler, Johannes; Pichler, Josef; Necker, Georg; Haunschmidt, Andreas; Meckel, Stephan; Shukla, Gaurav; Liem, Spencer; Alexander, Gregory S; Lombardo, Joseph; Palmer, Joshua D; Flanders, Adam E; Dicker, Adam P; Sair, Haris I; Jones, Craig K; Venkataraman, Archana; Jiang, Meirui; So, Tiffany Y; Chen, Cheng; Heng, Pheng Ann; Dou, Qi; Kozubek, Michal; Lux, Filip; Michálek, Jan; Matula, Petr; Keřkovský, Miloš; Kopřivová, Tereza; Dostál, Marek; Vybíhal, Václav; Vogelbaum, Michael A; Mitchell, J Ross; Farinhas, Joaquim; Maldjian, Joseph A; Yogananda, Chandan Ganesh Bangalore; Pinho, Marco C; Reddy, Divya; Holcomb, James; Wagner, Benjamin C; Ellingson, Benjamin M; Cloughesy, Timothy F; Raymond, Catalina; Oughourlian, Talia; Hagiwara, Akifumi; Wang, Chencai; To, Minh-Son; Bhardwaj, Sargam; Chong, Chee; Agzarian, Marc; Falcão, Alexandre Xavier; Martins, Samuel B; Teixeira, Bernardo C A; Sprenger, Flávia; Menotti, David; Lucio, Diego R; LaMontagne, Pamela; Marcus, Daniel; Wiestler, Benedikt; Kofler, Florian; Ezhov, Ivan; Metz, Marie; Jain, Rajan; Lee, Matthew; Lui, Yvonne W; McKinley, Richard; Slotboom, Johannes; Radojewski, Piotr; Meier, Raphael; Wiest, Roland; Murcia, Derrick; Fu, Eric; Haas, Rourke; Thompson, John; Ormond, David Ryan; Badve, Chaitra; Sloan, Andrew E; Vadmal, Vachan; Waite, Kristin; Colen, Rivka R; Pei, Linmin; Ak, Murat; Srinivasan, Ashok; Bapuraj, J Rajiv; Rao, Arvind; Wang, Nicholas; Yoshiaki, Ota; Moritani, Toshio; Turk, Sevcan; Lee, Joonsang; Prabhudesai, Snehal; Morón, Fanny; Mandel, Jacob; Kamnitsas, Konstantinos; Glocker, Ben; Dixon, Luke V M; Williams, Matthew; Zampakis, Peter; Panagiotopoulos, Vasileios; Tsiganos, Panagiotis; Alexiou, Sotiris; Haliassos, Ilias; Zacharaki, Evangelia I; Moustakas, Konstantinos; Kalogeropoulou, Christina; Kardamakis, Dimitrios M; Choi, Yoon Seong; Lee, Seung-Koo; Chang, Jong Hee; Ahn, Sung Soo; Luo, Bing; Poisson, Laila; Wen, Ning; Tiwari, Pallavi; Verma, Ruchika; Bareja, Rohan; Yadav, Ipsa; Chen, Jonathan; Kumar, Neeraj; Smits, Marion; van der Voort, Sebastian R; Alafandi, Ahmed; Incekara, Fatih; Wijnenga, Maarten M J; Kapsas, Georgios; Gahrmann, Renske; Schouten, Joost W; Dubbink, Hendrikus J; Vincent, Arnaud J P E; van den Bent, Martin J; French, Pim J; Klein, Stefan; Yuan, Yading; Sharma, Sonam; Tseng, Tzu-Chi; Adabi, Saba; Niclou, Simone P; Keunen, Olivier; Hau, Ann-Christin; Vallières, Martin; Fortin, David; Lepage, Martin; Landman, Bennett; Ramadass, Karthik; Xu, Kaiwen; Chotai, Silky; Chambless, Lola B; Mistry, Akshitkumar; Thompson, Reid C; Gusev, Yuriy; Bhuvaneshwar, Krithika; Sayah, Anousheh; Bencheqroun, Camelia; Belouali, Anas; Madhavan, Subha; Booth, Thomas C; Chelliah, Alysha; Modat, Marc; Shuaib, Haris; Dragos, Carmen; Abayazeed, Aly; Kolodziej, Kenneth; Hill, Michael; Abbassy, Ahmed; Gamal, Shady; Mekhaimar, Mahmoud; Qayati, Mohamed; Reyes, Mauricio; Park, Ji Eun; Yun, Jihye; Kim, Ho Sung; Mahajan, Abhishek; Muzi, Mark; Benson, Sean; Beets-Tan, Regina G H; Teuwen, Jonas; Herrera-Trujillo, Alejandro; Trujillo, Maria; Escobar, William; Abello, Ana; Bernal, Jose; Gómez, Jhon; Choi, Joseph; Baek, Stephen; Kim, Yusung; Ismael, Heba; Allen, Bryan; Buatti, John M; Kotrotsou, Aikaterini; Li, Hongwei; Weiss, Tobias; Weller, Michael; Bink, Andrea; Pouymayou, Bertrand; Shaykh, Hassan F; Saltz, Joel; Prasanna, Prateek; Shrestha, Sampurna; Mani, Kartik M; Payne, David; Kurc, Tahsin; Pelaez, Enrique; Franco-Maldonado, Heydy; Loayza, Francis; Quevedo, Sebastian; Guevara, Pamela; Torche, Esteban; Mendoza, Cristobal; Vera, Franco; Ríos, Elvis; López, Eduardo; Velastin, Sergio A; Ogbole, Godwin; Soneye, Mayowa; Oyekunle, Dotun; Odafe-Oyibotha, Olubunmi; Osobu, Babatunde; Shu'aibu, Mustapha; Dorcas, Adeleye; Dako, Farouk; Simpson, Amber L; Hamghalam, Mohammad; Peoples, Jacob J; Hu, Ricky; Tran, Anh; Cutler, Danielle; Moraes, Fabio Y; Boss, Michael A; Gimpel, James; Veettil, Deepak Kattil; Schmidt, Kendall; Bialecki, Brian; Marella, Sailaja; Price, Cynthia; Cimino, Lisa; Apgar, Charles; Shah, Prashant; Menze, Bjoern; Barnholtz-Sloan, Jill S; Martin, Jason; Bakas, Spyridon
PMID: 36702828
ISSN: 2041-1723
CID: 5426632

Federated learning enables big data for rare cancer boundary detection

Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pandey, Umang; Saini, Jitender; Garrett, John; Larson, Matthew; Jeraj, Robert; Currie, Stuart; Frood, Russell; Fatania, Kavi; Huang, Raymond Y; Chang, Ken; Quintero, Carmen Balaña; Capellades, Jaume; Puig, Josep; Trenkler, Johannes; Pichler, Josef; Necker, Georg; Haunschmidt, Andreas; Meckel, Stephan; Shukla, Gaurav; Liem, Spencer; Alexander, Gregory S; Lombardo, Joseph; Palmer, Joshua D; Flanders, Adam E; Dicker, Adam P; Sair, Haris I; Jones, Craig K; Venkataraman, Archana; Jiang, Meirui; So, Tiffany Y; Chen, Cheng; Heng, Pheng Ann; Dou, Qi; Kozubek, Michal; Lux, Filip; Michálek, Jan; Matula, Petr; Keřkovský, Miloš; Kopřivová, Tereza; Dostál, Marek; Vybíhal, Václav; Vogelbaum, Michael A; Mitchell, J Ross; Farinhas, Joaquim; Maldjian, Joseph A; Yogananda, Chandan Ganesh Bangalore; Pinho, Marco C; Reddy, Divya; Holcomb, James; Wagner, Benjamin C; Ellingson, Benjamin M; Cloughesy, Timothy F; Raymond, Catalina; Oughourlian, Talia; Hagiwara, Akifumi; Wang, Chencai; To, Minh-Son; Bhardwaj, Sargam; Chong, Chee; Agzarian, Marc; Falcão, Alexandre Xavier; Martins, Samuel B; Teixeira, Bernardo C A; Sprenger, Flávia; Menotti, David; Lucio, Diego R; LaMontagne, Pamela; Marcus, Daniel; Wiestler, Benedikt; Kofler, Florian; Ezhov, Ivan; Metz, Marie; Jain, Rajan; Lee, Matthew; Lui, Yvonne W; McKinley, Richard; Slotboom, Johannes; Radojewski, Piotr; Meier, Raphael; Wiest, Roland; Murcia, Derrick; Fu, Eric; Haas, Rourke; Thompson, John; Ormond, David Ryan; Badve, Chaitra; Sloan, Andrew E; Vadmal, Vachan; Waite, Kristin; Colen, Rivka R; Pei, Linmin; Ak, Murat; Srinivasan, Ashok; Bapuraj, J Rajiv; Rao, Arvind; Wang, Nicholas; Yoshiaki, Ota; Moritani, Toshio; Turk, Sevcan; Lee, Joonsang; Prabhudesai, Snehal; Morón, Fanny; Mandel, Jacob; Kamnitsas, Konstantinos; Glocker, Ben; Dixon, Luke V M; Williams, Matthew; Zampakis, Peter; Panagiotopoulos, Vasileios; Tsiganos, Panagiotis; Alexiou, Sotiris; Haliassos, Ilias; Zacharaki, Evangelia I; Moustakas, Konstantinos; Kalogeropoulou, Christina; Kardamakis, Dimitrios M; Choi, Yoon Seong; Lee, Seung-Koo; Chang, Jong Hee; Ahn, Sung Soo; Luo, Bing; Poisson, Laila; Wen, Ning; Tiwari, Pallavi; Verma, Ruchika; Bareja, Rohan; Yadav, Ipsa; Chen, Jonathan; Kumar, Neeraj; Smits, Marion; van der Voort, Sebastian R; Alafandi, Ahmed; Incekara, Fatih; Wijnenga, Maarten M J; Kapsas, Georgios; Gahrmann, Renske; Schouten, Joost W; Dubbink, Hendrikus J; Vincent, Arnaud J P E; van den Bent, Martin J; French, Pim J; Klein, Stefan; Yuan, Yading; Sharma, Sonam; Tseng, Tzu-Chi; Adabi, Saba; Niclou, Simone P; Keunen, Olivier; Hau, Ann-Christin; Vallières, Martin; Fortin, David; Lepage, Martin; Landman, Bennett; Ramadass, Karthik; Xu, Kaiwen; Chotai, Silky; Chambless, Lola B; Mistry, Akshitkumar; Thompson, Reid C; Gusev, Yuriy; Bhuvaneshwar, Krithika; Sayah, Anousheh; Bencheqroun, Camelia; Belouali, Anas; Madhavan, Subha; Booth, Thomas C; Chelliah, Alysha; Modat, Marc; Shuaib, Haris; Dragos, Carmen; Abayazeed, Aly; Kolodziej, Kenneth; Hill, Michael; Abbassy, Ahmed; Gamal, Shady; Mekhaimar, Mahmoud; Qayati, Mohamed; Reyes, Mauricio; Park, Ji Eun; Yun, Jihye; Kim, Ho Sung; Mahajan, Abhishek; Muzi, Mark; Benson, Sean; Beets-Tan, Regina G H; Teuwen, Jonas; Herrera-Trujillo, Alejandro; Trujillo, Maria; Escobar, William; Abello, Ana; Bernal, Jose; Gómez, Jhon; Choi, Joseph; Baek, Stephen; Kim, Yusung; Ismael, Heba; Allen, Bryan; Buatti, John M; Kotrotsou, Aikaterini; Li, Hongwei; Weiss, Tobias; Weller, Michael; Bink, Andrea; Pouymayou, Bertrand; Shaykh, Hassan F; Saltz, Joel; Prasanna, Prateek; Shrestha, Sampurna; Mani, Kartik M; Payne, David; Kurc, Tahsin; Pelaez, Enrique; Franco-Maldonado, Heydy; Loayza, Francis; Quevedo, Sebastian; Guevara, Pamela; Torche, Esteban; Mendoza, Cristobal; Vera, Franco; Ríos, Elvis; López, Eduardo; Velastin, Sergio A; Ogbole, Godwin; Soneye, Mayowa; Oyekunle, Dotun; Odafe-Oyibotha, Olubunmi; Osobu, Babatunde; Shu'aibu, Mustapha; Dorcas, Adeleye; Dako, Farouk; Simpson, Amber L; Hamghalam, Mohammad; Peoples, Jacob J; Hu, Ricky; Tran, Anh; Cutler, Danielle; Moraes, Fabio Y; Boss, Michael A; Gimpel, James; Veettil, Deepak Kattil; Schmidt, Kendall; Bialecki, Brian; Marella, Sailaja; Price, Cynthia; Cimino, Lisa; Apgar, Charles; Shah, Prashant; Menze, Bjoern; Barnholtz-Sloan, Jill S; Martin, Jason; Bakas, Spyridon
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
PMCID:9722782
PMID: 36470898
ISSN: 2041-1723
CID: 5381682

Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRI

Radmanesh, Alireza; Muckley, Matthew J; Murrell, Tullie; Lindsey, Emma; Sriram, Anuroop; Knoll, Florian; Sodickson, Daniel K; Lui, Yvonne W
PURPOSE/UNASSIGNED:To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. MATERIALS AND METHODS/UNASSIGNED:potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. RESULTS/UNASSIGNED:Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model's ability to produce images substantially distinct from the training set, even at 100× acceleration. CONCLUSION/UNASSIGNED:© RSNA, 2022.
PMCID:9745443
PMID: 36523647
ISSN: 2638-6100
CID: 5382452

Deep multi-task learning and random forest for series classification by pulse sequence type and orientation

Kasmanoff, Noah; Lee, Matthew D; Razavian, Narges; Lui, Yvonne W
PURPOSE/OBJECTIVE:Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation. METHODS:Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series. RESULTS:The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets. CONCLUSION/CONCLUSIONS:The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification.
PMID: 35906437
ISSN: 1432-1920
CID: 5277082