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Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

Passive Filters

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Active Filters

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

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Updated: Feb 13, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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A convolutional neural network to filter artifacts in spectroscopic MRI.

Saumya S Gurbani^1,2,3, Eduard Schreibmann^1,3, Andrew A Maudsley⁴

¹Department of Radiation Oncology, Emory University, Atlanta, Georgia.

Magnetic Resonance in Medicine

|March 10, 2018

Summary

A new deep learning model accurately identifies and removes artifacts in proton magnetic resonance spectroscopic imaging (MRSI), improving data quality for clinical applications like radiation therapy planning.

Keywords:

MR spectroscopic imaging deep learning machine learning spectroscopic MRI

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Area of Science:

Medical Imaging
Artificial Intelligence
Biophysics

Background:

Proton MRSI is a noninvasive technique for mapping in vivo tissue metabolism.
It is valuable for studying neuropathologies but hindered by spectral artifacts.
Artifacts can compromise the accuracy of MRSI data.

Purpose of the Study:

To develop a deep learning model for identifying and filtering spectral artifacts in MRSI.
To improve the reliability and clinical adoption of MRSI.
To automate the assessment of spectral quality.

Main Methods:

A tiled convolutional neural network was developed to analyze frequency-domain spectra.
The model was trained to detect and filter out poor quality spectra.
A visualization scheme was implemented to interpret the model's decisions.

Main Results:

The convolutional neural network achieved high sensitivity and specificity (AUC of 0.95) compared to human experts.
The model was integrated into a real-time pipeline for whole-brain spectroscopic MRI.
The system demonstrated effective artifact detection and spectral quality assessment.

Conclusions:

An automated deep learning method enhances spectral quality assessment in MRSI.
This tool supports clinical MRSI and spectroscopic MRI studies.
Applications include adaptive radiation therapy planning and neuropathology research.