Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

507
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
507

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Learning Driven Evaluation of MR-guided Focused Ultrasound Ablation.

IEEE transactions on bio-medical engineering·2026
Same author

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same author

Uncovering memorization effect in the presence of spurious correlations.

Nature communications·2025
Same author

Radiation induces diffuse extracellular remodeling of healthy myocardium in a dose- and time-dependent manner without a dense ablative effect.

Heart rhythm·2025
Same author

Model-based self-supervised learning for quantitative assessment of myocardial oxygen extraction fraction and myocardial blood volume.

Magnetic resonance in medicine·2025
Same author

Improving ungated steady-state cardiac perfusion using transition bands.

Magnetic resonance in medicine·2025
Same journal

Age-Related Concentric Remodeling and Sex-Dependent Dimensional Variation in Left Ventricular Geometry: A Cardiac Magnetic Resonance Study.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Opportunistic Screening for Low Bone Density Using Automated Vertebral Trabecular CT Attenuation from Low-Dose CT Acquired During FDG PET/CT: A Single-Center Retrospective Study.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Relationship Between Cervical Central Canal and Neural Foraminal Dimensions in a Normative Population.

Tomography (Ann Arbor, Mich.)·2026
Same journal

AI-Based Scientific Manuscript Peer Review: Is It Ready for Adoption?

Tomography (Ann Arbor, Mich.)·2026
See all related articles

Related Experiment Video

Updated: Apr 12, 2026

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
10:21

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

8.3K

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Qi Huang1,2, Johnathan Le1,2, Sarang Joshi2

  • 1Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108, USA.

Tomography (Ann Arbor, Mich.)
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

This study improves cardiac MRI myocardial blood flow quantification by using deep learning to correct inaccurate arterial input functions (AIFs). Integrating tissue curves with AIF data significantly enhances accuracy for perfusion measurements.

Keywords:
AIF correctionAIF saturationBi-LSTMarterial input functiondeep learningmyocardial perfusion MRI

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

705

Related Experiment Videos

Last Updated: Apr 12, 2026

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
10:21

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

8.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

705

Area of Science:

  • Medical Imaging
  • Cardiovascular MRI
  • Quantitative Perfusion Imaging

Background:

  • The arterial input function (AIF) is crucial for accurate myocardial blood flow (MBF) quantification in cardiac MRI.
  • Inaccurate AIFs, often due to saturation or bias, can lead to significant errors in perfusion quantification.
  • Developing robust methods to correct AIFs is essential for reliable cardiac MRI analysis.

Purpose of the Study:

  • To investigate methods for improving arterial input function (AIF) accuracy in cardiac MRI when only saturated and biased AIFs are measured.
  • To evaluate the effectiveness of leveraging tissue curve information and optimizing deep neural network loss functions for AIF correction.
  • To enhance the precision of myocardial blood flow quantification by improving AIF estimation.

Main Methods:

  • Generated simulated cardiac MRI data using a 12-parameter AIF model and compartment models for tissue curves.
  • Employed Bloch simulations for a saturation-recovery 3D radial stack-of-stars sequence, accounting for sequence imperfections.
  • Trained a bidirectional long short-term memory (Bi-LSTM) network, comparing AIF loss only versus combined AIF and tissue/parameter loss strategies.

Main Results:

  • The bidirectional long short-term memory (Bi-LSTM) network significantly reduced AIF peak error from -23.6% to 0.2-0.3% with simulated data.
  • Ktrans error was reduced from -13.5% to approximately 0% with simulated data, demonstrating improved perfusion quantification.
  • On hybrid data (simulated training, in vivo testing), integrating tissue curves with AIF inputs reduced AIF peak error to 1.3% and ktrans error to -2.4%.

Conclusions:

  • Integrating tissue curves with arterial input function (AIF) curves as inputs to deep learning networks improves the precision of AI-driven AIF corrections.
  • The proposed method demonstrates robust performance on both simulated and hybrid datasets, including application to in vivo data.
  • This approach offers a promising strategy for enhancing the accuracy of myocardial blood flow quantification in cardiac MRI.