D.J. Hawkes’s research while affiliated with University College London and other places

Background Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations. Methods Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format. Results Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8–15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes. Conclusions Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard.

Background The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration.Methods Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes.ResultsThe primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound.Conclusion The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

Motion-including dose reconstruction (MIDR) aims at reconstructing the actually delivered dose to the moving anatomy during radiotherapy. Patient-specific motion models (PSMM) can be used to determine the time-resolved anatomy during treatment delivery on an MR-linac for MIDR. In this study, PSMM-based MIDR was validated for MR-guided lung SBRT. The digital XCAT phantom was used to generate a ground truth moving anatomy (GT-XCAT) based on in-vivo measured motion. Using the first 10 minutes of the motion trace, GT-XCAT volumes were subsampled to simulate pre-treatment interleaved sagittal/coronal MR acquisition with a sagittal navigator slice for breathing signal extraction. A PSMM was fitted and a motion-compensated super-resolution image (MCSRI) was reconstructed simultaneously. An MR-linac treatment plan for 3-fraction lung-SBRT was designed on a reference GT-XCAT. GT-XCATs were generated for the remainder of the motion trace. The intra-treatment time-resolved anatomy was estimated via MCSRI deformation using the PSMM and the breathing signals extracted from navigator slices sub-sampled from GT-XCATs. Treatment delivery was simulated in our in-house emulator. The treatment fluence was discretized into sub-beams, each associated with the GT or deformed-MCSRI anatomy that it was delivered to. The dose was accumulated onto the reference anatomy. For comparison, shift-MIDR was calculated emulating tumour motion as sub-beam isocenter shifts on the static reference GT-XCAT anatomy. For the plan dose, GT-MIDR, PSMM-MIDR and shift-MIDR respectively: GTV-D98% was 70.8Gy, 67.7Gy, 69.0Gy and 67.4Gy; GTV-D50% was 77.7Gy, 775.2Gy, 75.5Gy and 76.0Gy; heart-V30Gy was 48.4cc, 55.6cc, 53.0cc and 64.7cc; Oesophagus-V2% was 22.6Gy, 21.7Gy, 21.7Gy and 23.1Gy. Evaluated against GT-MIDR, PSMM-MIDR was more accurate than shift-MIDR for organ at risk (OAR) dose estimation and similar for target dose estimation. The MR-based PSMM was shown to be suitable for MIDR of the target and OAR. Shift-MIDR is not intended to correctly estimate OAR dose but may be used for target dose estimation.

Purpose or Objective Motion-including dose reconstruction (MIDR) aims at reconstructing the actually delivered dose to the moving anatomy during radiotherapy. However, the time-resolved patient anatomy during treatment is generally unknown and commonly estimated using 3D or 4D pre-treatment images. In this study, we reconstructed the delivered dose on a ground-truth, fully time-resolved anatomy (GT-MIDR) and used this to evaluate the accuracy of MIDR based on 3D and 4D CT images. Material and Methods The digital XCAT phantom was used to generate three regularly breathing thorax phantoms, each with a lung tumour moving according to its location (Fig 1). A 4DCT was generated and treatment plans were created for either a mid-ventilation approach (midVent) or treatment delivery with dynamic MLC tracking (tracking) (9-beam step-and-shoot IMRT, RTOG 1021). Treatment delivery under regular motion or regular motion and continuous drift was simulated in our in-house software. For MIDR, the treatment fluence is discretized into subbeams; each sub-beam is associated with the anatomy instance that it was delivered to and shifted to account for residual tumour position difference between the estimated anatomy and the actual target position if any. I.e. motion is modelled by a sub-beam isocenters shift, to emulate the actual relative target/beam position. The dose for each instance is then calculated in a research treatment planning system (TPS) and accumulated on the reference anatomy via deformable registration. For GT-MIDR, ground-truth anatomy instances were generated from the XCAT. For 3D-MIDR, only one anatomy instance, the midVent 4DCT phase, was used and motion was accounted for by sub-beam isocenter shifts only. For 4D-MIDR, anatomy instances were chosen as the 4DCT phase where the tumour is closest to the actual tumour position and sub-beam isocenter shifts accounted for residual position differences. Results Differences between GT-MIDR and planning (Fig 2) show the effect of motion (midVent) or motion and mitigation (tracking) on dose delivery. Target underdosage was highest for tumour A. Tracking resulted in higher dose to the spinal cord and heart (tumour A, B) or aorta (tumour C). Differences between 3D or 4D and GT-MIDR (Fig 3) show the accuracy of the respective methods. Reconstructed target dose errors above 1Gy were observed for 3D-MIDR. For tumour A, reconstructed dose to the organs-at-risks (OAR) were underestimated by 3D-MIDR. For tumour B, dose to the oesophagus and aorta was overestimated by 3D-MIDR. For tumour C, dose to the spinal cord and the oesophagus were overestimated by 4D-MIDR and dose to the aorta was underestimated by 3D and 4D-MIDR. Conclusion In the first demonstration of GT-MIDR, we calculated the delivered dose to target and OAR for a range of lung tumour locations. We showed that MIDR based on planning data does not accurately resolve the delivered dose even in the case of regular motion. Our method may be used to validate MIDR for other motion models and treatment sites.

Accurate registration of images depicting respiratory motion, e.g. 4DCT or 4DMR, can be challenging due to sliding motion that occurs between the chest wall and organs within the pleural sac (lungs, mediastinum, liver). In this paper we propose a methodology that (1) segments one of the images to be registered (the source or floating/moving image) into two distinct regions by fitting a statistical motion mask, and (2) registers the image with a modified B-spline registration algorithm that can account for sliding motion between the regions. This registration requires the segmentation of the regions in the source image domain as a signed distance map. Two underlying transformations allow the regions to deform independently, while a constraint term based on the transformed distance maps penalises gaps and overlaps between the regions. Although implemented in a B-spline algorithm, the required modifications are not specific to the transformation type and thus can be applied to parametric and non-parametric frameworks alike. The registration accuracy is evaluated using the landmark registration error on the basis of the publicly available DIR-Lab dataset. The overall average landmark error after registration is 1.21 mm and the average gap and overlap volumes are 26.4 cm³ and 34.5 cm³ respectively. The fitted statistical motion masks are compared to previously proposed motion masks and the corresponding mean Dice coefficient is 0.96.