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Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.

[www]

Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.

[www] [BibTex]

Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.

[www]

Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.

[www]

Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.

[www] [BibTex]

Note: article, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.