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• The impact of loss functions on complex MRI reconstruction with CNNs was analyzed. • The mean squared loss is biased towards magnitude underestimations with phase error. • A newly-proposed unbiased loss function called ⊥ -loss yields improved performance. • ⊥ -loss can be applied to any high-dimensional problem defined in R 2. • ⊥ -loss was applied to image registration tasks, showing superior performance. Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, e.g., to reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) acquisitions or estimate subject motion during an examination. MRI is naturally acquired in the complex domain C , obtaining magnitude and phase information in k-space. However, CNNs in complex regression tasks are almost exclusively trained to minimize the L2 loss or maximizing the magnitude structural similarity (SSIM), which are possibly not optimal as they do not take full advantage of the magnitude and phase information present in the complex domain. This work identifies that minimizing the L2 loss in the complex field has an asymmetry in the magnitude/phase loss landscape and is biased, underestimating the reconstructed magnitude. To resolve this, we propose a new loss function for regression in the complex domain called ⊥ -loss, which adds a novel phase term to established magnitude loss functions, e.g., L2 or SSIM. We show ⊥ -loss is symmetric in the magnitude/phase domain and has favourable properties when applied to regression in the complex domain. Specifically, we evaluate the ⊥ + ℓ 2 -loss and ⊥ +SSIM-loss for complex undersampled MR image reconstruction tasks and MR image registration tasks. We show that training a model to minimize the ⊥ + ℓ 2 -loss outperforms models trained to minimize the L2 loss and results in similar performance compared to models trained to maximize the magnitude SSIM while offering high-quality phase reconstruction. Moreover, ⊥ -loss is defined in R n , and we apply the loss function to the R 2 domain by learning 2D deformation vector fields for image registration. We show that a model trained to minimize the ⊥ + ℓ 2 -loss outperforms models trained to minimize the end-point error loss. [ABSTRACT FROM AUTHOR]