[www] [BibTex]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.

[www]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.

[www] [BibTex]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.

[www]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.

[www]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.

[www] [BibTex]

Note: article, multi-contrast

Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.