Blood-oxygen-level-dependent (BOLD) time series demonstrate signal changes in placentae and fetal organs during maternal hyperoxia.^1,2 Accurate quantification of signal changes is difficult due to complex fetal and maternal movements. Recent studies proposing different motion correction pipelines highlighted the importance of this problem.^3,4 Here, we propose a robust preprocessing pipeline compensating rigid and elastic motion within a volume and between volumes for better regional analysis of placentae and fetal organs on BOLD MRI during maternal hyperoxia.In this IRB approved study, six twin pregnancies with gestational age range 26 to 34 weeks were recruited. BOLD imaging of the placentae and fetal bodies was performed on a 3T Skyra scanner (Siemens Healthcare, Erlangen, Germany) with a maternal oxygenation protocol, designed as three consecutive 10-minute episodes: initial normoxic episode (room air, 21% O2), hyperoxic episode (15 l/min), and a final normoxic episode. Data was acquired with single shot GRE-EPI: TR=5.8-8s, TE=32-38ms, FA=90°, isotropic voxels of 3×3×3mm³, and, total acquisition time 30 minutes. Preprocessing pipeline: 1) Signal non-uniformity correction: 3D-N4⁵ bias correction implementation in ANTS registration suite was used with a mask covering whole uterus. To improve robustness, a single bias field was estimated from the average of selected volumes collected in first 10 minutes and was applied to each frame. 2) Motion correction within each volume: Fetal and placental volumes were acquired with an interleaved slice acquisition resulting in severe motion artifacts between even and odd slices. To compensate these artifacts, first even and odd slice components were separated into two sub-volumes with doubled slice thickness. Then, a group-wise B-spline transformation approach was applied as proposed by Guyader et al.⁶ to align even and odd slice volumes at each time point. 3) Motion correction between volumes: Mean Square Error (MSE) difference of these motion corrected volumes were computed and the volume with the least MSE difference was chosen as a reference volume. To correct maternal bulk motion, an initial rigid body transformation was created as a mapping from the reference volume to the remaining volumes. Next, a B-spline transformation with mutual information was used for motion correction of the placentae and fetal livers while using the previous rigid transformation as an initialization component. B-spline transformation parameters were adjusted in a subset of volumes to prevent undesired regional volume increase or decrease (e.g. keeping voxel-wise change <50%). In order to avoid unrealistic deformations of the fetal head, a rigid body transformation was applied to the fetal head. Note that Elastix⁷ toolbox was used for motion correction steps in the pipeline. 4) BOLD Signal Change Visualization: Regions of interest (i.e. placenta, fetal liver and brain) were manually delineated in ITK-SNAP using the reference frame under the supervision of an experienced radiologist. Averaged signal intensities were calculated in these regions of each registered volume. 5) Outlier rejection: A two-step outlier rejection approach was followed. As a first step, after motion correction within a volume, volumes including voxels with negative determinants of the Jacobian of the B-spline transformation were rejected. A second outlier rejection step was performed after the motion correction between volumes. The difference between the intensities of each voxel within a ROI at time t and the next time point t+1 were compared to the mean signal intensity change in the entire ROI at time t. When the difference was larger than this mean value, the voxel at time t+1 was marked as an outlier and not used in the mean signal calculation. However, its value was replaced with the value at time t for the evaluation at the next time point. Volumes with > 5% outlier voxels in the ROI were rejected. Figure 1 and 2 demonstrate the results of each step in the pipeline for a single data set. Intensity-time plots in Figure 2 illustrate that not only rejecting volumes with outlier voxels but also updating ROIs after rejecting outlier voxels within the ROI decreased the motion related signal variance between time frames. In order to quantify the alignment accuracy, ROIs for placentae, livers and brains were manually delineated on several time points (excluding outlier volumes) and Dice similarity coefficients were computed with respect to the initial masks and the updated masks after the outlier voxel rejection. For 6 datasets, dice coefficients and the percentage of the outlier volumes were given in Table 1. Updating ROIs by rejecting outlier voxels significantly improved the similarity (p<0.001).It is shown that the proposed preprocessing pipeline can decrease the signal non-uniformity and motion related signal variance between time frames in placental and fetal BOLD MRI.