Non-FFP-Based Magnetic Particle Imaging (NFMPI) with an Open-Type RF Coil System: A Feasibility Study

Abstract

Active drug delivery systems for cancer therapy are gaining attention for their biocompatibility and enhanced efficacy compared to conventional chemotherapy and surgery. To improve precision in targeted drug delivery (TDD), actuating devices using external magnetic fields are employed. However, a key challenge is the inability to visually track magnetic drug carriers in blood vessels, complicating navigation to the target. Magnetic particle imaging (MPI) systems can localize magnetic carriers (MCs) but rely on bulky electromagnetic coils to generate a static magnetic field gradient, creating a field-free point (FFP) within the field of view (FOV). Also, additional coils are required to move the FFP across the FOV, limiting flexibility and increasing the system size. To address these issues, we propose a non-FFP-based, open-type RF coil system with a simplified structure composed of a Tx/Rx coil and a permanent magnet at the coil center, eliminating the need for an FFP. Furthermore, integrating a robotic arm for coil assembly enables easy adjustment of the FOV size and location. Finally, imaging tests with magnetic nanoparticles (MNPs) confirmed the system's ability to detect and localize a minimum mass of 0.3 mg (Fe) in 80 × 80 mm².

Keywords: magnetic particle imaging; non-FFP-based method; open-type MPI scanner; targeted drug delivery.

Figure 1

Configuration diagram of a non-FFP-based MPI system (NFMPI). NFMPI consists of three coils (one Tx coil and two Rx coils) and a cylindrical permanent magnet. Based on the center magnet, the Tx coil is placed between the Rx coils to create an alternating magnetic field (AMF), while the Rx coils are placed at the top and bottom of the Tx coil to detect the MNP signal (Rx_col) and to reduce the residual signal of the RF system (Rx_cali), respectively.

Figure 2

Conceptual diagram of the non-FFP-based magnetic particle imaging. (a) Scanning trajectory on the ROI plane. (b) Continuous magnetic particle signal induced by the receiving coil from A to E when the MNP group is at C.

Figure 3

A three-dimensional visualization of the received magnetic particle signal on the XY plane. The received magnetic particle signal is distributed in the form of a mountain basin according to the change in particle magnetization caused by the static magnetic field of the permanent magnet.

Figure 4

A gradient descent method to find a valley point on the measured data image. (a) A 2D image on the XY plane, including a red star displaying a starting point as a peak point of image, a black star displaying a final valley point, and pink stars displaying calculated locations by the gradient descent method between a starting point and a valley point. (b) a Gradient value graph during the iteration process of (a). (c) Finding a valley point from a starting point manually picked by an operator. (d) A gradient value graph during the iteration process of (c).

Figure 4

A gradient descent method to find a valley point on the measured data image. (a) A 2D image on the XY plane, including a red star displaying a starting point as a peak point of image, a black star displaying a final valley point, and pink stars displaying calculated locations by the gradient descent method between a starting point and a valley point. (b) a Gradient value graph during the iteration process of (a). (c) Finding a valley point from a starting point manually picked by an operator. (d) A gradient value graph during the iteration process of (c).

Figure 5

Experimental setup of the NFMPI system.

Figure 6

Circuit diagram of the NFMPI system.

Figure 7

1D analysis of received signals depending on various amounts of MNP.

Figure 8

Two-dimensional magnetic particle image of MNPs (0.3 mg) on the XY plane (ROI of 80 × 80 mm²). (a) A 1 mm grid plate phantom; (b) a 2D image of MNPs located at A(−12, 12); (c) a 2D image of MNPs located at B(0,0); (d) a 2D image of MNPs located at C(12, −5). In (b–d), the red stars describe the predicted location of MNPs.