多通道磁粒子造影系統可用於生醫方面進行藥物標靶以及腫瘤追蹤。傳統磁粒子造影系統的造影方式為掃描式，且須外加高強度的梯度磁場，由於外加梯度磁場產生的零磁點範圍狹小，因此能造影的面積也將受限。
本研究旨在開發大面積的陣列式多通道磁粒子造影系統，利用測得的即時樣品訊號能進行動態影像造影，造影面積由線圈陣列的面積而定，且不需要高強度的梯度磁場，因此能大幅降低系統的成本。
本系統可造影的面積約為直徑17 cm之圓形面積，影像的空間解析度將隨著樣品接近接收線圈而提升，最靠近接收線圈時的空間解析度約為10 mm，系統可測得的最小含鐵量為0.4 μg，多通道系統中訊噪比最佳達到513。
目前所使用的樣品訊號演算法為最小範數估計演算法(Minimum-Norm Estimation, MNE)，未來可嘗試多種的樣品演算法取得最佳的樣品反演算結果。

The magnetic particle imaging system can be used in drug targeting and tumor tracking in biomedical aspects. The traditional imaging method of magnetic particle imaging system was scanning the sample by using the pick-up coil with gradient magnetic field of high intensity. Since the field-free point generated by the gradient magnetic field is limited, the imaging area is restrained.
The motivation of this research is developing the magnetic particle imaging system with wide imaging area under the array type pick-up coil. The dynamic imaging can be generated by the immediacy signal acquisition from the pick-up coil. Since the imaging area is determined by the area of the pick-up coil array and the high gradient magnetic field is not needed, the cost is lower than the scanning type magnetic particle imaging system.
The imaging area of the magnetic particle imaging system is about the circular area of 17 cm diameter. The resolution of the image increases when the samples approach the pick-up coil. The minimum image resolution of the system is about 10 mm when the sample was closest to the pick-up coil. The threshold of iron detected by the system is 0.4 μg, and the best signal to noise ratio of all channel is up to 513.
The algorithm used at present is Minimum-Norm Estimation. To get the better results of the reconstructed image, finding the new algorithm is necessary in the future studies.

1. Gleich, B. and R. Weizenecker, Tomographic imaging using the nonlinear response of magnetic particles. Nature, 2005. 435(7046): p. 1214-1217.
2. Lin, F.H., et al., Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage, 2006. 31(1): p. 160-171.
3. Hauk, O., Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data. Neuroimage, 2004. 21(4): p. 1612-1621.
4. Wang, J.Z., S.J. Williamson, and L. Kaufman, MAGNETIC SOURCE IMAGES DETERMINED BY A LEAD-FIELD ANALYSIS - THE UNIQUE MINIMUM-NORM LEAST-SQUARES ESTIMATION. Ieee Transactions on Biomedical Engineering, 1992. 39(7): p. 665-675.