One aspect of ongoing research on obesity and metabolic diseases¹ is the assessment of human brown adipose tissue (BAT) using MRI^2-4. Imaging is challenging because human BAT is a heterogeneous mixture⁵ of brown and white adipocytes. Isotropic image resolutions below (1.5 mm)³ are required to distinguish between both. Additionally, BAT has been mostly observed in the cervical and mediastinal anatomic areas^6,7, which results in a high susceptibility to breathing motion during image acquisition. One option is to acquire images during breath-hold. However, breath-hold positions differ due to long measurement times requiring data registration. In this study, two deformable registration algorithms were evaluated with respect to their suitability for compensation of deviations in breath-hold positions during BAT assessment.Data from a previously published human BAT assessment study⁸ were analyzed retrospectively. The study was performed using a 3T MRI system (Biograph mMR 3.0T, Siemens, Erlangen, Germany) with five healthy volunteers. 2-point Dixon⁹ imaging (VIBE; TR=5.85ms, TE_in/TE_opp=2.46/3.69ms, α=10° matrix=416×260×64, FOV=500×312×76.8mm³, BW=710 Hz/px, GRAPPA R=2; TA=30sec) was used during breath-hold. Water and fat images were acquired with a 5-minute time resolution for 140 minutes. Registration of data sets to a target data set (D_T, water image) was done with MITK (Medical Imaging Interaction Toolkit, DKFZ, Heidelberg, Germany)¹⁰. The Insight Tookit (itk)-based¹¹ implementations of the deformable Fast Symmetric Forces Demons¹² (FSF) and Level Set Motion¹³ (LSM) algorithms were used. For each volunteer, a data set at the beginning (D_M1) and at the end of the measurement (D_M2) were chosen. Water images of D_M1 and D_M2 were registered to the target data D_T using FSF and LSM resulting in transformations T_FSF and T_LSM, respectively (Fig.1a). Registration evaluation was done using PointSet Interaction (plugin of MITK). A radiologist (5 years of experience) placed a set of five positions X_T={x_i ǀ i=1…5} at distinct anatomical landmarks on D_T. The following landmarks were chosen (Fig.1b): L₁: joint space of the right clavicle (axial), L₂: branching of the left common carotid artery from the aortic arch (coronal), L₃: left costo-vertebral joint space of the 1st thoracic vertebra (axial), L₄: processus spinosus of the 7th cervical vertebra (sagittal), L₅: left tuberculum majus humeri (sagittal). The positions X_M1={x_i,_M1 ǀ i=1…5} and X_M2={x_i,M2 ǀ i=1…5} of the same landmarks were marked on the unregistered data sets (D_M1, D_M2). Using T_FSF and T_LSM, both sets of positions X_M1 and X_M2 were mapped to the target. Deviation vectors ∆s_i = x_i,M1/2 - x_i,T for all landmarks L_i (i=1…5) for all volunteers were calculated. Mean deviations ∆s_i,mean for each landmark were compared for both algorithms.Boxplots show the distribution range of each mapped landmark for FSF and LSM (Fig.2). Median values scatter around zero in the range of M_FSF=[-0.70; 0.31]mm and M_LSM=[-2.91; 1.22]mm. For each landmark, a shift of a single voxel after (side length 1.2mm) after mapping with FSF and LSM is illustrated (Fig.3). Mean deviations ∆s_i,mean scatter around zero in the range [-0.90; 0.51]mm for FSF and [-2.49; 1.06]mm for LSM. LSM results show larger maximum deviations and larger absolute deviations compared to FSF. In general, landmark L₅ shows the maximum absolute deviation, with ∆s_5,FSF=1.61mm≙1.34pixel and ∆s_5,LSM=2.64mm≙-2.20pixel. Combining the results for all landmarks L₁-L₅ and focusing on the directions in space (x-,y-,z-components), mean deviation vectors are ∆m_FSF=(-0.18; -0.19; -0.26)mm and ∆m_LSM=(-0.21; -0.59; -0.51)mm (Fig.4,table).Neither registration algorithm (FSF and LSM) showed a systematic displacement of the landmarks. Both deviation sets ∆s_i,mean scatter around zero. FSF results show smaller mean deviations and smaller maximum absolute deviations. Hence, with the limited number of landmarks, FSF seems to be suitable for correction of BAT data. Considering the evaluated mean BAT volume of 0.42ml in a recent volunteer study⁸ and assuming a simplified cubical shape (7.4mm≙6.2pixel side length), a shift using the mean deviations ∆m_FSF and ∆m_LSM still results in an overlap of OV_mean,FSF=92% and OV_mean,LSM=83% (Fig.4a). Considering the maximum deviations of directions in space, no overlap (OV_all) exists (Fig.4b). High deviations of landmark L₅ lead to the exclusion of BAT assessment in this site. Hence, the maximum error including L₁-L₄ leads to an overlap of OV_exL5=25% for FSF mapping (Fig.4c). L₁, L₃, L₄ are close to anatomical BAT sites^1,6,7. Although the volunteer study⁸ focused on interscapular BAT, the results for FSF allow for evaluation of more BAT sites, e.g. supraclavicular BAT. Since supraclavicular BAT depot volumes are expected to be larger, an increase in overlap is expected. In conclusion, this study indicates that FSF is suitable for correction of breath-hold offsets in human data and enables improved assessment of human BAT depots.