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2.2 Spinal Mestastasis Segmentation

3.1.6 Discussion

Image fusion has been successfully applied to radiation therapy for the purpose of delineation and enhancement of target fields (Dalah et al., 2008) or to support image-guided interventions (Mauri et al.,2015; Ne-mec et al.,2007; Sohn et al.,2009). However, in musculoskeletal radiology, especially in neuroradiology of the spine, image fusion applications have rarely been reported. Image fusion of diagnosticMRIand interventional FP-CTscans can efficiently support image-guided interventions of spinal metastases due to the valuable gain in image information.MRIis consid-ered the method of choice regarding tumour and metastasis delineation as well as for assessing compressions of spinal nerve roots and the spinal cord, due to its emphasised soft tissue contrast. However,RFAs are per-formed underFP-CTimage guidance, which besides a pronounced image contrast of cortical bone structures withholds relevant image information regarding soft tissues like metastases. Therefore, fusion of both modal-ities can provide various additional information to support applicator guidance and needle tip placement beyond the established methods of

a b c d e

Figure 3.5: Comparison of fused images using a globally rigid (a) and a multiseg-mental (b) registration approach. Shown are a mid-sagittal (top row) and lateral (bottom row) cross-section of an exemplary patient case.

Additionally, the masks of previously segmented metastases were transformed according to the resulting transformation matrices of the globally rigid (red contours) and the proposed multirigid approach (green contours). Sagittal (c), coronal (d) and axial (e) cross-sections of theFP-CTscan are shown with overlayed metastasis contours. The FREGRof this patient case was 4.58 mm and theFREMSwas 1.84 mm.

current navigation systems (Kavakebi et al., 2017; Wallach et al.,2014).

Considering this, the proposed multisegmental voxel-based (MS-VB) reg-istration approach represents a key aspect of this thesis and the intended intervention support.

Table3.2displays an overview of relevant information about the pre-sented and related state of the art works, although some of the latter did not state any quantitative results, which restricts comparability. In con-trast to the related work of spinal image fusion, the presented approach focused on interventional rather than nativeCTimaging, which meant qualitatively inferior images due to low-dose protocols. So far, either landmark-based approaches (Kaminsky et al., 2004; Karlo et al., 2010;

Sohn et al., 2009) orMIas a registration metric ( ˇCech et al.,2006; Hu and Haynor,2004; Miles et al.,2013) have been used. The former usually have the disadvantage of a time-consuming initialisation procedure, which grows proportionally with the number of used landmarks. Automatised landmark detection approaches, e.g. by edge detection, come at the cost of solving the complex correspondence problem between two sets of land-marks. In contrast to the mean computational time of 24 s per segment of the proposed MS-VB approach, the study of Zhang et al. (2020) for instance, required on average 15 min, while Hu and Haynor (2004) re-ported an overall required time of approximately 60 min per patient case, which is hardly compatible with any clinical procedures. Additionally, the registration accuracy of manual procedures is highly dependent on the

user’s precision and care, whereas especially a high workload and lack of time in clinical routine could negatively affect the results. Likewise, a preceding vertebrae segmentation to define the regions to be registered, as presented by ˇCech et al. (2006) or Hu and Haynor (2004), is yet more time-consuming, if manually performed or if automatically performed requires similar user initialisation as the proposed MS-VBapproach (Chu et al., 2015; Rak et al., 2019; Zuki´c et al., 2014). The presented MS-VB method, however, required only an approximate marking of the vertebrae to be registered, which reduced the required time for an initialisation as well as the cognitive load of radiologists and could therefore, be more easily integrated into the clinical workflow. However, this manual initial-isation can be replaced in the future by automatic vertebra and spinal metastases detection procedures like presented by Chen et al. (2015), Rak and Tönnies (2016), and Wang et al. (2017). Yet it remains to be seen, how robust vertebra detection in the immediate vicinity of metastases per-forms. After the implementation and publication of the proposedMS-VB approach, Rashad et al. (2019) presented a largely similar registration strategy using a new commercially available software. They followed the pipeline of vertebra-specific multirigid registrations and the subsequent embedding in fused images. As already mentioned in Section3.1.2, their results should be considered with reservations, since their evaluation strategy was unsuitable to unbiasedly assess the registration accuracy of their proposed method. In addition, their registration accuracy was highly dependent on the initial prealignment, which partially led toFREs over 2 cm, if the outmost segmented vertebrae were used as starting points.

However, the findings of Rashad et al. (2019) indicated the importance of a multisegmental approach to cope with spine deformations (meanFREMS of 1.54 mm vs. mean FREGR of 7.12 mm) and the impact of the spatial resolution on the registration accuracy (mean FREMS of 0.91 mm with high resolution images vs. meanFREMSof 1.77 mm with low resolution images).

Similar to the findings of Rashad et al. (2019) regarding the impact of spine deformations, the averageFREMS with 2.35 mm achieved with the proposed multisegmental approach was significantly lower than the averageFREGR of 3.82 mm using a global transformation (see Figure3.5).

The latter would not have met the clinical objectives. This was also evident considering the individual patient cases (see Table A.4), with 9 out of the total 19 patient cases not meeting the accuracy criteria if registered globally. Applying theMS-VBapproach in contrast, resulted only in two cases with an insufficient accuracy (one of them achieved 3.12 mm instead of the required 3 mm). The worst patient case resulted in an FREMS of 4.43 mm averaged over 9 pairs of corresponding landmarks, although the mFRE, i.e. the mean error of a manual registration of this case, was among the largest with 2.37 mm (1.70 mm overall patient cases). Accordingly, the actual image registration could have been sufficiently precise, but the considerably highmFRE indicated difficulties in accurately identifying corresponding landmarks and thus also had a negative impact on the measured registration accuracy of the proposed approach. In general, the

interventionalphase

Table 3.2: Related work and the presentedMS-VBapproach in comparison w.r.t. the used image modalities (MRI,CT,FP-CT), the number of patient cases NP, the usage of interventional imaging data (IInt), the registration metric (MI- mutual information,NGF normalised gradient fields, LMB -landmark-based), the used transformation type, as well as the achieved average fiducial registration errorFREand the required computational time per datasett(t- average time required per vertebra). The results of Rashad et al. (2019) were questionable w.r.t. to their evaluation strategy.

Works Image Data NP IInt Metric Transformation FRE [mm] t Hu and Haynor,2004 MRI / CT 1 - MI multirigid n.s. >60 min Kaminsky et al.,2004 MRI / CT 1 - LMB multirigid 1.53 >8 min

Cech et al.,ˇ 2006 MRI / CT 3 - MI multirigid n.s. n.s.

Sohn et al.,2009 MRI / CT 20 - LMB globally rigid n.s. n.s.

Karlo et al.,2010 MRI / CT 10 - LMB globally rigid 1.46 ∼2 min Miles et al.,2013 MRI / CT 20 - MI globally rigid 1.90 n.s.

Rashad et al.,2019 MRI / CT 10 - MI multirigid (1.54) n.s.

Zhang et al.,2020 MRI / CT 22 - n.s. globally rigid 1.6 ∼15 min MS-VB MRI/FP-CT 19 + NGF multirigid 2.35 24 s

magnitude of themFREcan be explained in particular by the voxel size of theMRIscans, which had a diagonal of approximately 3.5 mm. Besides the spatial resolution component, it is not trivial to identify correspond-ing anatomical landmarks in images of different modalities, since the representation and display of the same tissue types differ considerably.

Hence, the meanmFREof 1.70 mm demonstrated the challenging task of landmark localisation in two image volumes even for field experts.

Furthermore, intra- and inter-reader variability regarding theFLEwere investigated. As expected, average intra-reader variability was signifi-cantly lower than the inter-reader variability (1.04 mm vs. 1.32 mm), since various readers subjectively interpret the given landmark positioning guidelines slightly different. Even though, the anatomical landmark posi-tions were clearly defined in theory, inaccuracies occurred, because, e.g.

the vertebral rim was several voxels wide and the sagittal plane of symme-try was partially difficult to determine objectively and reproducibly. For instance, the choice of the respective sagittal cross-section could already lead to significant discrepancies between different readers. TheFLEs of landmarks placed in theMR images were slightly higher than those in the interventionalFP-CT, which was due to the lower spatial resolution of theMRI. With 0.8±0.4 mm, a similarFLEof the intra-reader variability inMRI(spatial resolution of 0.3×0.3×3 mm) was found by Miles et al.

(2013).

Considering the usedMRIsequences, slightly higherFREs have been observed when usingT2- instead ofT1-weightedMR images. This could partly be attributed to the somewhat better contrast of theT1-weighted images at the transition between the dorsal vertebral rim and the spinal canal, while the image intensities of both, vertebral bone marrow and cerebrospinal fluid were roughly similar in the T2-weightedMRimages.

In terms of the used registration metric,NGFproved superior toMI (av-erageFREMSof 2.35 mm vs. 2.87 mm). This could most likely be attributed to the overall reduced tissue contrast andSNRof low-dose interventional FP-CT imaging. In most FP-CT scans only the cortical bone of the ver-tebra emitted sufficient imaging signals and high image contrast, while surrounding tissues or the cancellous bone was hardly visible and thus contributed scarcely to the MIcriterion.