Stocchiero Silvia, Abdarahmane Isselmou, Poblador Rodríguez Esaú, Fröhlich Vanessa, Zeilinger Markus, Georg Dietmar
Competence Center for Preclinical Imaging and Biomedical Engineering, Faculty of Health, University of Applied Sciences, Wiener Neustadt, Austria.
Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
Med Phys. 2025 Jul;52(7):e17963. doi: 10.1002/mp.17963.
Ultra-high field (UHF) magnetic resonance (MR) systems are advancing in preclinical imaging offering the potential to enhance radiation research. However, system-dependent factors, such as magnetic field inhomogeneities ( ) and gradient non-linearity (GNL), induce geometric distortions compromising the sub-millimeter accuracy required for radiation research.
This study tackles system-dependent distortions in 15.2T MR images by prospective shimming strategies optimization and comparing two imaging methods for voxel displacement correction. The methods were evaluated on a 3D-printed grid phantom and validated on in vivo mouse brain MR images. Additionally, a phantom-based displacement map was tested for GNL correction in mouse brain images.
Phantom MR and CT images were acquired with 200 resolution. In vivo mouse brain MR and CT images had 140 and 200 resolutions, respectively. Three shimming strategies were established to assess displacements ( ) in phantom MR images. was calculated using the acquired static field maps in three volumes of interest (VOIs) via Python script. A one-step distortion correction (1SDC) method, which simultaneously corrects and GNL distortions via non-rigid registration with CT, and a two-step distortion correction (2SDC) method, which corrects separately in two consecutive steps and GNL displacements, were assessed on phantom and in vivo images. For in vivo 2SDC validation, a phantom displacement map generated by MR to CT non-rigid registration was applied to correct GNL on the mouse brain. Total displacements ( ) were quantified in phantom VOIs and the in vivo skull region by measuring landmarks' positions.
The in the phantom increased with distance from the VOI center and magnet isocenter. Shimming scenario-2 showed the lowest maximum displacement (0.26 mm) for the largest VOI but required a longer acquisition time. Distortion correction methods were necessary for large VOIs (13-25 mm, along the z-axis) in the phantom where 0.2 mm. The 2SDC method outperformed 1SDC by achieving a 0.2 mm accuracy in 100%, 92.1%, and 59.3% of the landmarks from the smallest to the largest VOI. Phantom dice scores confirmed the improvement in geometric precision after each correction step. In vivo results showed that 1SDC correction overcorrected MR images, increasing voxel displacements. The 2SDC exceeded the 1SDC, reducing by 85%, in accordance with the dice score analysis (0.97 2SDC vs. 0.84 1SDC).
At 15.2T, in vivo MR images of even small regions (e.g., mouse brain) require geometric distortion correction for radiation research. The 2SDC method outperformed the 1SDC, emphasizing the need for separate and GNL corrections. Moreover, a phantom-based displacement map shows promise for in vivo GNL correction.
超高场（UHF）磁共振（MR）系统在临床前成像领域不断发展，为加强放射学研究提供了潜力。然而，诸如磁场不均匀性（ ）和梯度非线性（GNL）等与系统相关的因素会导致几何畸变，从而影响放射学研究所需的亚毫米精度。
本研究通过前瞻性匀场策略优化以及比较两种体素位移校正成像方法，来解决15.2T MR图像中与系统相关的畸变问题。这些方法在3D打印网格体模上进行了评估，并在小鼠脑活体MR图像上进行了验证。此外，还测试了基于体模的位移图对小鼠脑图像中的GNL校正效果。
使用200 的分辨率采集体模MR和CT图像。小鼠脑活体MR和CT图像的分辨率分别为140 和200 。建立了三种匀场策略来评估体模MR图像中的 位移（ ）。通过Python脚本在三个感兴趣体积（VOI）中使用采集的静磁场图计算 。在体模和活体图像上评估了一种一步畸变校正（1SDC）方法，该方法通过与CT进行非刚性配准同时校正 和GNL畸变，以及一种两步畸变校正（2SDC）方法，该方法在两个连续步骤中分别校正 和GNL位移。对于活体2SDC验证，将通过MR到CT非刚性配准生成的体模位移图应用于校正小鼠脑上的GNL。通过测量标记点的位置，在体模VOI和活体颅骨区域中对总位移（ ）进行了量化。
体模中的 随着距VOI中心和磁等中心距离的增加而增大。匀场方案2在最大VOI时显示出最低的最大位移（0.26毫米），但需要更长的采集时间对于体模中沿z轴13 - 25毫米的大VOI（ 0.2毫米），畸变校正方法是必要的。2SDC方法优于1SDC，在从最小到最大VOI的标记点中，分别有100%（ 0.2毫米）、92.1%和59.3%达到了 0.2毫米的精度。体模骰子分数证实了每个校正步骤后几何精度的提高。活体结果表明，1SDC校正对MR图像进行了过度校正，增加了体素位移。根据骰子分数分析（2SDC为0.97，1SDC为0.84），2SDC超过了1SDC，使 减少了85%。
在15.2T时，即使是小区域（如小鼠脑）的活体MR图像也需要进行几何畸变校正以用于放射学研究。2SDC方法优于1SDC，强调了分别进行 和GNL校正的必要性。此外，基于体模的位移图在活体GNL校正方面显示出前景。
