Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.

Medical augmented reality (AR) applications where virtual renderings are aligned with the real world allow to visualize internal anatomy of the patient to a medical caregiver wearing an AR headset. Accurate alignment of virtual and real content is important for applications where the virtual rendering is used to guide the medical procedure such as a surgery. Compared to 2D AR applications, where the alignment accuracy can be directly measured on the 2D screen, 3D medical AR applications require alignment measurements using phantoms and external tracking systems. In this paper we present an approach for landmark-based alignment, validation and accuracy measurement of a 3D AR overlay of medical images on the real-world subject. This is done by performing an initial MRI of a subject’s head, an AR alignment task of the virtual rendering of the head MRI data to the subject’s real-world head using virtual fiducials, and a second MRI scan to test the accuracy of the AR alignment task. We have performed these 3D medical AR alignment measurements on seven volunteers using a MagicLeap AR head-mounted display. Across all seven volunteers we measured an alignment accuracy of $4.7 \pm 2.6$ mm. These results suggest that such an AR application can be a valuable tool for guiding non-invasive transcranial magnetic brain stimulation treatment. The presented MRI-based accuracy validation will furthermore be an important versatile tool to establish the safety of medical AR techniques.