Toward a realistic in silico abdominal phantom for QSM

Abstract

Purpose: QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms.

Methods: Synthetic susceptibility and $R_2^{*}$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility and $R_2^{*}$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM and $R_2^{*}$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms.

Results: QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms.

Conclusion: The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility, $R_2^{*}$ and TEs, and thus creates different abdominal scenarios.

Keywords: MRI simulation; abdomen; digital phantom; liver QSM; quantitative susceptibility mapping; water-fat separation.

FIGURE 1

(Left) Pipeline employed in the phantom generation process. (Right) A magnified view shows the detailed workflow of the data processing and tissue segmentation step

FIGURE 2

Representative axial and sagittal slices of the susceptibility, local field, total field, and ${\mathrm{R}}_2^{*}$ maps of the experimental data and the three simulated phantoms: HS, PL, and IO

FIGURE 3

Axial views of the experimental and simulated acquired signals for phantoms HS, PL, and IO with protocols P1 and P2. The three pairs of columns show magnitude and the unwrapped phase at the first, third, and last TE (sixth echo for P1 or fifth for P2), respectively. To ease visualization, magnitude images were normalized between 0 and 1.

FIGURE 4

(A) Two axial examples of the total fieldmap estimations of phantoms HS, PL, and IO, for the corresponding acquisition protocols P1 and P2. Estimation errors from IGC can be seen as hyperintense regions around the subcutaneous fat (black arrows). (B) Axial slices of the local field reconstructions for phantoms HS, PL, and IO, and their difference with ground truth. Left and right columns show the results for P1 and P2 protocols, respectively. (C) Unwrapped phase of the HS phantom with the one‐peak‐based in‐phase protocol (P2) and its discrepancies with a perfect in‐phase simulation at every TE

FIGURE 5

(Left) Axial, coronal, and sagittal slices of QSM reconstructions for the HS phantom with P1 (top) and P2 (bottom). (Right) Axial, coronal, and sagittal slices with the difference between the reconstructed QSM image and the ground truth

FIGURE 6

(Left) Axial, coronal, and sagittal slices of QSM reconstructions for the PL phantom with P1 (top) and P2 (bottom). (Right) Axial, coronal, and sagittal slices with the difference between the reconstructed QSM image and the ground truth

FIGURE 7

(Left) Axial, coronal, and sagittal slices of QSM reconstructions for the IO phantom with P1 (top) and P2 (bottom). (Right) Axial, coronal, and sagittal slices with the difference between the reconstructed QSM image and the ground truth