Aims: Proof of concept and feasibility study for preoperative diagnostic use of mixed reality (MR) holograms of individual 3D heart models from standard cardiac computed tomography angiograms (CTA) images. Optimal repair for complex congenital heart disease poses high demands on 3D anatomical imagination. Three-dimensional printed heart models are increasingly used for improved morphological understanding during surgical and interventional planning. Holograms are a dynamic and interactive alternative, probably with wider applications. Methods and results: A 3D heart model was segmented from CTA images in a patient with double outlet right ventricle and transposition of the great arteries (DORV-TGA). The hologram was visualized in the wearable MR platform HoloLens® for 36 paediatric heart team members who filled out a diagnostic and quality rating questionnaire. Morphological and diagnostic output from the hologram was assessed and the 3D experience was evaluated. Locally developed app tools such as hologram rotation, scaling, and cutting were rated. Anatomy identification and diagnostic output was high as well as rating of 3D experience. Younger and female users rated the app tools higher. Conclusion: This preliminary study demonstrates that MR holograms as surgical planning tool for congenital heart disease may have a high diagnostic value and contribute to understanding complex morphology. The first users experience of the hologram presentation was found to be very positive, with a preference among the female and the younger users. There is potential for improvement of the hologram manipulation tools.

Introduction: In liver surgery, medical images from pre-operative computed tomography and magnetic resonance imaging are the basis for the decision-making process. These images are used in surgery planning and guidance, especially for parenchyma-sparing hepatectomies. Though medical images are commonly visualized in two dimensions (2D), surgeons need to mentally reconstruct this information in three dimensions (3D) for a spatial understanding of the anatomy. The aim of this work is to investigate whether the use of a 3D model visualized in mixed reality with Microsoft HoloLens increases the spatial understanding of the liver, compared to the conventional way of using 2D images. Material and methods: In this study, clinicians had to identify liver segments associated to lesions. Results: Twenty-eight clinicians with varying medical experience were recruited for the study. From a total of 150 lesions, 89 were correctly assigned without significant difference between the modalities. The median time for correct identification was 23.5 [4–138] s using the magnetic resonance imaging images and 6.00 [1–35] s using HoloLens (p < 0.001). Conclusions: The use of 3D liver models in mixed reality significantly decreases the time for tasks requiring a spatial understanding of the organ. This may significantly decrease operating time and improve use of resources.

Conventional surgical navigation systems rely on preoperative imaging to provide guidance. In laparoscopic liver surgery, insufflation of the abdomen (pneumoperitoneum) can cause deformations on the liver, introducing inaccuracies in the correspondence between the preoperative images and the intraoperative reality. This study evaluates the improvements provided by intraoperative imaging for laparoscopic liver surgical navigation, when displayed as augmented reality (AR). Significant differences were found in terms of accuracy of the AR, in favor of intraoperative imaging. In addition, results showed an effect of user-induced error: image-to-patient registration based on annotations performed by clinicians caused 33% more inaccuracy as compared to image-to-patient registration algorithms that do not depend on user annotations. Hence, to achieve accurate surgical navigation for laparoscopic liver surgery, intraoperative imaging is recommendable to compensate for deformation. Moreover, user annotation errors may lead to inaccuracies in registration processes.

In laparoscopic liver resection, surgeons conventionally rely on anatomical landmarks detected through a laparoscope, preoperative volumetric images and laparoscopic ultrasound to compensate for the challenges of minimally invasive access. Image guidance using optical tracking and registration procedures is a promising tool, although often undermined by its inaccuracy. This study evaluates a novel surgical navigation solution that can compensate for liver deformations using an accurate and effective registration method. The proposed solution relies on a robotic C-arm to perform registration to preoperative CT/MRI image data and allows for intraoperative updates during resection using fluoroscopic images. Navigation is offered both as a 3D liver model with real-time instrument visualization, as well as an augmented reality overlay on the laparoscope camera view. Testing was conducted through a pre-clinical trial which included four porcine models. Accuracy of the navigation system was measured through two evaluation methods: liver surface fiducials reprojection and a comparison between planned and navigated resection margins. Target Registration Error with the fiducials evaluation shows that the accuracy in the vicinity of the lesion was 3.78±1.89 mm. Resection margin evaluations resulted in an overall median accuracy of 4.44 mm with a maximum error of 9.75 mm over the four subjects. The presented solution is accurate enough to be potentially clinically beneficial for surgical guidance in laparoscopic liver surgery.

Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.

Laparoscopic liver resection (LLR) of colorectal liver metastases (CLM) is increasingly performed in specialized centers. While there is a trend towards a parenchyma-sparing strategy in multimodal treatment for CLM, its role is yet unclear. In this study we present short- and long-term outcomes of laparoscopic parenchyma-sparing liver resection (LPSLR) at a single center. LLR were performed in 951 procedures between August 1998 and March 2017 at Oslo University Hospital, Oslo, Norway. Patients who primarily underwent LPSLR for CLM were included in the study. LPSLR was defined as non-anatomic hence the patients who underwent hemihepatectomy and sectionectomy were excluded. Perioperative and oncologic outcomes were analyzed. The Accordion classification was used to grade postoperative complications. The median follow-up was 40 months. 296 patients underwent primary LPSLR for CLM. A single specimen was resected in 204 cases, multiple resections were performed in 92 cases. 5 laparoscopic operations were converted to open. The median operative time was 134 minutes, blood loss was 200 ml and hospital stay was 3 days. There was no 90-day mortality in this study. The postoperative complication rate was 14.5%. 189 patients developed disease recurrence. Recurrence in the liver occurred in 146 patients (49%), of whom 85 patients underwent repeated surgical treatment (liver resection [n = 69], ablation [n = 14] and liver transplantation [n = 2]). Five-year overall survival was 48%, median overall survival was 56 months. LPSLR of CLM can be performed safely with the good surgical and oncological results. The technique facilitates repeated surgical treatment, which may improve survival for patients with CLM.