PurposeAn augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid.ApproachThe HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy (n = 56), and needle targeting accuracy (n = 24) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method (n = 4) and free-hand guidance (n = 4) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum.ResultsThe image overlay error was 1.29 ± 0.57 mm, and needle targeting error was 2.13 ± 0.52 mm. The planned-path guidance placements showed similar error compared to the free-hand guidance (4.14 ± 1.08 mm versus 4.20 ± 1.08 mm, p = 0.90). The markers were successfully implanted either into or in close proximity to the target lesion.ConclusionsThe HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.
Purpose: This study aims to investigate correlation of speed of sound (SoS) map with T2-weighted (T2w) MRI and pathology in an ex vivo human prostate tissue with cancer, as an early proof of concept towards cost effective augmented ultrasound diagnosis of prostate cancer. Method: A commercial breast full angle ultrasound tomography scanner was used to generate US tomography images. Prostate-specific Echolucent mold was fabricated to allow MRI and UST to be spatially correlated. Similarly, a 3D printed mold was developed to align the histology slices with the UST and MRI. The resulting slices of prostate tissue were H and E stained. A radiologist with 10 years of experience in using multi parametric MRI for prostate cancer diagnosis labeled and contoured the suspicious ROIs in both MRI and UST. For all tissue blocks (N=10 slices with 6 mm thickness), H and E slides were prepared and labeled by an expert pathologist. Results: The radiologist found two slices with prominent cancer in each modality (i.e. MR and UST) in the peripheral zone. These two pairs of slices correlated with each other and with slices #5 and #7 in pathology. The cancer ROIs were found at similar locations in all modalities, although MR and UST underestimated the size of lesions (Sørensen–Dice coefficients, with respect to pathology, for T2w and UST were 0.11 and 0.20 respectively for first ROI, and 0.33 and 0.27 for second ROI). The SoS was 1580.4±17.7 m/s and 1571.4±9.2 m/s for normal and cancer tissues in first ROI, and 1577.7±17.7 m/s and 1574.5±10.1 m/s for second ROI. Conclusions: SoS map can correlate with MRI and pathology findings in prostate cancer. Further ex vivo validation with fresh prostate tissue is warranted.
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