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Talebi S, Gai S, Sossin A, Zhu V, Tong E, Mofrad MRK. Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics. Ann Biomed Eng 2024; 52:1568-1575. [PMID: 38402314 PMCID: PMC11082011 DOI: 10.1007/s10439-024-03471-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/06/2024] [Indexed: 02/26/2024]
Abstract
Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.
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Affiliation(s)
- Salmonn Talebi
- Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA
| | - Siyu Gai
- Departments of Electrical Engineering and Computer Science, University of California, Berkeley, California, USA
| | - Aaron Sossin
- Department of Bioinformatics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Vivian Zhu
- Department of Bioinformatics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford School of Medicine, Stanford University, 725 Welch Rd Rm 1860, Palo Alto, Stanford, CA, 94304, USA.
| | - Mohammad R K Mofrad
- Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
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Zhang L, Fan M, Li L. Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:13-24. [PMID: 38343210 DOI: 10.1007/s10278-023-00915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 03/02/2024]
Abstract
Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, China
| | - Ming Fan
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies. J Clin Med 2023; 12:jcm12113755. [PMID: 37297949 DOI: 10.3390/jcm12113755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Elyse A Kuo
- School of Medicine, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Jennifer D Sokolowski
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Ryan T Kellogg
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Min Park
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
| | - Panagiotis Mastorakos
- Department of Neurological Surgery, University of Virginia Health Sciences Center, 1215 Lee Street, Charlottesville, VA 22903, USA
- Department of Neurological Surgery, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
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Chaki J, Woźniak M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Gava UA, D'Agata F, Tartaglione E, Renzulli R, Grangetto M, Bertolino F, Santonocito A, Bennink E, Vaudano G, Boghi A, Bergui M. Neural network-derived perfusion maps: A model-free approach to computed tomography perfusion in patients with acute ischemic stroke. Front Neuroinform 2023; 17:852105. [PMID: 36970658 PMCID: PMC10034033 DOI: 10.3389/fninf.2023.852105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/24/2023] [Indexed: 03/29/2023] Open
Abstract
Objective In this study, we investigate whether a Convolutional Neural Network (CNN) can generate informative parametric maps from the pre-processed CT perfusion data in patients with acute ischemic stroke in a clinical setting. Methods The CNN training was performed on a subset of 100 pre-processed perfusion CT dataset, while 15 samples were kept for testing. All the data used for the training/testing of the network and for generating ground truth (GT) maps, using a state-of-the-art deconvolution algorithm, were previously pre-processed using a pipeline for motion correction and filtering. Threefold cross validation had been used to estimate the performance of the model on unseen data, reporting Mean Squared Error (MSE). Maps accuracy had been checked through manual segmentation of infarct core and total hypo-perfused regions on both CNN-derived and GT maps. Concordance among segmented lesions was assessed using the Dice Similarity Coefficient (DSC). Correlation and agreement among different perfusion analysis methods were evaluated using mean absolute volume differences, Pearson correlation coefficients, Bland-Altman analysis, and coefficient of repeatability across lesion volumes. Results The MSE was very low for two out of three maps, and low in the remaining map, showing good generalizability. Mean Dice scores from two different raters and the GT maps ranged from 0.80 to 0.87. Inter-rater concordance was high, and a strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98, respectively). Conclusion The agreement between our CNN-based perfusion maps and the state-of-the-art deconvolution-algorithm perfusion analysis maps, highlights the potential of machine learning methods applied to perfusion analysis. CNN approaches can reduce the volume of data required by deconvolution algorithms to estimate the ischemic core, and thus might allow the development of novel perfusion protocols with lower radiation dose deployed to the patient.
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Affiliation(s)
- Umberto A Gava
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
| | | | - Enzo Tartaglione
- Department of Computer Science, University of Turin, Turin, Italy
| | | | - Marco Grangetto
- Department of Computer Science, University of Turin, Turin, Italy
| | - Francesca Bertolino
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
| | | | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Giacomo Vaudano
- Division of Neuroradiology, San Giovanni Bosco Hospital, Turin, Italy
| | - Andrea Boghi
- Division of Neuroradiology, San Giovanni Bosco Hospital, Turin, Italy
| | - Mauro Bergui
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
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Hernandez Petzsche MR, de la Rosa E, Hanning U, Wiest R, Valenzuela W, Reyes M, Meyer M, Liew SL, Kofler F, Ezhov I, Robben D, Hutton A, Friedrich T, Zarth T, Bürkle J, Baran TA, Menze B, Broocks G, Meyer L, Zimmer C, Boeckh-Behrens T, Berndt M, Ikenberg B, Wiestler B, Kirschke JS. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data 2022; 9:762. [PMID: 36496501 PMCID: PMC9741583 DOI: 10.1038/s41597-022-01875-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
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Affiliation(s)
- Moritz R Hernandez Petzsche
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Roland Wiest
- Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Waldo Valenzuela
- Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, Univ. of Bern, Bern, Switzerland
| | | | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | | | - Alexandre Hutton
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Tassilo Friedrich
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Teresa Zarth
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Johannes Bürkle
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - The Anh Baran
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Björn Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lukas Meyer
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Boeckh-Behrens
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Maria Berndt
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benno Ikenberg
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
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