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

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.4K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.4K
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

642
Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
642
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.1K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.1K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

5.3K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
5.3K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.0K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Submillimeter postmortem and in vivo diffusion and susceptibility magnetic resonance imaging to characterize cortical micro- and meso-structures.

Research square·2026
Same author

Correction to "Cell-Penetrating and Enzyme-Responsive Peptides for Targeted Cancer Therapy: Role of Arginine Residue Length on Cell Penetration and In Vivo Systemic Toxicity".

ACS applied materials & interfaces·2026
Same author

High mannose content of mesenchymal glioblastoma correlates with hydroxyl proton transfer-weighted (HPTw) MRI as noninvasive biomarker of tumor aggressiveness.

Science advances·2026
Same author

Whole-body in vivo MPI cytometry reveals injection route-, dose-, cell size-, and disease-dependent differences in organ distribution.

Science advances·2026
Same author

Stepwise Purification of Superparamagnetic Iron Oxide Nanoparticles Improves Magnetic Particle Imaging Performance.

Nano letters·2026
Same author

Vascular smooth muscle cell loss, but not neuroinflammation, drives cerebrovascular reactivity impairment in Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Related Experiment Video

Updated: Sep 17, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K

Spillover can limit accurate signal quantification in MPI.

Ali Shakeri-Zadeh1,2, Shreyas Kuddannaya1,2, Adnan Bibic1,3

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Npj Imaging
|July 3, 2025
PubMed
Summary

Accurate magnetic particle imaging (MPI) quantification in vivo is challenging. Adjusting target-to-fiducial distance and iron concentration ratios significantly reduces signal spillover, improving MPI accuracy for magnetic tracer quantification.

More Related Videos

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.6K
A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS
11:18

A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS

Published on: July 11, 2017

10.9K

Related Experiment Videos

Last Updated: Sep 17, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.6K
A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS
11:18

A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS

Published on: July 11, 2017

10.9K

Area of Science:

  • Biomedical Engineering
  • Medical Imaging

Background:

  • Accurate quantification of magnetic particle imaging (MPI) signals in vivo is crucial but technically challenging.
  • The spillover effect, signal leakage from adjacent regions, complicates in vivo MPI quantification, especially with multiple signal sources (hot spots).

Purpose of the Study:

  • To assess and mitigate the spillover effect in MPI for improved in vivo magnetic tracer quantification.
  • To determine the impact of fiducial positioning, iron content, and concentration ratios on MPI signal accuracy.

Main Methods:

  • Utilized custom-designed phantoms and in vivo mouse studies.
  • Investigated the influence of target-to-fiducial distance (TFD) and target-to-fiducial Fe concentration ratios (TFCR).
  • Evaluated four different MPI scan modes for signal quantification accuracy.

Main Results:

  • Adjusting TFD and TFCR significantly reduced the spillover effect in MPI.
  • Optimized fiducial positioning and concentration ratios enhance signal quantification accuracy.
  • The findings are applicable to scenarios with multiple hot spots in vivo.

Conclusions:

  • Implementing strategies to adjust TFD and TFCR effectively mitigates spillover effects in MPI.
  • This mitigation strategy increases the accuracy of in vivo magnetic tracer quantification using MPI.
  • The study provides a pathway for more reliable MPI applications in biological research and clinical settings.