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The order of magnitude of a number is the power of 10 that most closely approximates it. Thus, the order of magnitude estimates the scale (or size) of its value. To find the order of magnitude of a number, take the base-10 logarithm of the number and round it to the nearest integer. Then the order of magnitude of the number is simply the resulting power of 10.
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Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements.

Warren Foltz1,2, Brandon Driscoll1, Sangjune Laurence Lee2

  • 1Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada.

Tomography (Ann Arbor, Mich.)
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

Arterial input function (AIF) measurements using MRI phase signals (AIFPHA) were found to be more accurate and robust than those using MRI magnitude signals (AIFMAGN) when compared to CT (AIFCT). AIFPHA demonstrated superior performance across various conditions.

Keywords:
MRI phasearterial input function (AIF)dynamic contrast-enhanced MRI (DCE-MRI)permeabilityphantomquantification

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Radiology

Background:

  • Accurate patient-specific arterial input functions (AIF) are crucial for model-based analysis of vascular permeability.
  • Magnetic resonance imaging (MRI) offers potential for AIF measurement, but its accuracy is affected by various factors.

Purpose of the Study:

  • To investigate factors influencing AIF measurements from MRI magnitude (AIFMAGN) and phase (AIFPHA) signals.
  • To compare MRI-derived AIFs against computed tomography (CT) AIFs (AIFCT) under controlled conditions.

Main Methods:

  • A multimodality flow phantom was used with varying flip angles, flow rates, and contrast agent concentrations.
  • MRI 3D-FLASH signals and variable flip angle T1 profiles were measured to assess in-flow and radiofrequency biases.
  • Gd-DTPA concentrations derived from MRI magnitude and phase signals were compared to AIFCT using Pearson correlation.

Main Results:

  • AIFMAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, significantly underestimating AIFCT peak concentrations.
  • Converting AIFMAGN to Gd-DTPA concentration using orientation- and flow-matched T1 improved accuracy but remained variable above 2.5 mM.
  • AIFPHA performed equivalently to AIFCT within 1 mM across all tested conditions, demonstrating superior robustness.

Conclusions:

  • MRI phase-derived arterial input functions (AIFPHA) provide a more robust and accurate measurement compared to magnitude-derived AIFs (AIFMAGN).
  • AIFPHA demonstrates reliable performance equivalent to AIFCT across a wide range of physiological conditions.
  • AIFPHA shows significant promise for accurate patient-specific AIF quantification in clinical settings.