Previous studies have shown that image fusion is a powerful methodology to enhance diagnostic and follow up imaging in cancer patients [30
]. Different registration algorithms have been used successfully for non-interventional applications in the past [29
], and were successfully applied in this study to RFA for liver and kidney tumors. Registration and fusion may be useful during interventional procedures and may assist before, during, and after RFA [21
]. Often morphologic and functional imaging studies provide separate and complimentary information. Registration of pre- and post-RFA images may provide another window into the often-subtle spatial relationships between tumor and post-RFA thermal lesion. Conventional interpretation uses mental registration [9
]; however computer processing may provide a more objective and exact view [36
]. Image fusion has matured predominately for rigid structures (brain and bone), and many technical details have already been refined, however, several major problems remain while performing image fusion after RFA.
The problem of respiratory motion is inherent to the imaging itself. Image fusion is easily performed on the brain [2
] because the skull is a rigid structure that prohibits significant movements. Unlike the brain, the abdominal cavity is not stationary, and organs can significantly alter their shape and location. These changes can be due to breathing, the position of the patient on the table, organ shift, change in organ shape, hydration status, stomach contents, and the RFA procedure itself etc. Not surprisingly, this caused mis-registration and hampered the fusion process in the kidney and liver in some cases.
Organ shift and shrinkage were encountered, as thermal lesions tend to shrink after RFA. This is problematic for retrospective fusion of post-RFA images to pre-RFA images to assess for adequacy of treatment margin (see ). If weeks to months are allowed to pass before post-RFA imaging, then registration may show the now-shrunken thermal lesion to be smaller than the tumor, giving the false impression of inadequate treatment. This occurred repeatedly when we compared 2-month post-RFA images to pre-RFA images in kidney tumor patients, who did not suffer subsequent recurrence years later (see ).
The size of the safety margin may influence the utility of this technique [37
]. In the liver, a 5–10 mm margin of normal tissue burned may be easier to mentally co-register than a patient with a familial renal cell carcinoma, where only a several mm margin is desirable, to preserve normal kidney function given the predisposition for synchronous and metachronous tumor development over a lifetime. For the latter, this technique may be more useful.
If a tumor only presents during arterial phase imaging, co-registration may enable using the spatial information of that brief arterial phase for localization during a procedure. Image registration lets the physician use off-line prior imaging in the procedure room. Any imaging dataset can be registered to CT space, which can then be used to guide robotic needle placements for point and click tumor destruction [38
]. This is a powerful tool that may gain importance in the future as tumor-specific and cell-specific contrast agents are developed. Fusion may also enable biopsy of metabolically active regions of a tumor, which could facilitate more accurate biopsy and improved information on the temporal and spatial evolution of a tumor genomic or proteomic profile. This in turn could help tailor patient-specific drug regimens.
Region cropping proved to be a rapid and simple method of scaling down the large imaging datasets into a computationally workable size. While future optimizations are planned and processing speeds are improving, the registration process took 3–5 min, which makes this technique clinically relevant for intra-procedural monitoring and navigation. Monitoring and navigation during RFA currently suffers from imaging limitations. Ultrasound gas shadows the burn, CT contrast increases risk of renal toxicity, CT fluoroscopy has potentially high radiation doses to the user, and MR thermometry is not widely available, is costly, and requires a RF switch box or alternating imaging Rf-signals with treatment currents. Electromagnetic tracking during RFA may register pre-procedural imaging to the patient for use during needle manipulation and is being further investigated as an alternate method allowing use of pre-procedural imaging during interventions [39
Versatile fusion software with multiple available methods of rigid and elastic registration may improve chances for optimal fusion for a given patient [40
], as each method has own inherent strengths and weaknesses. However, fusion can facilitate interventions in select scenarios. Further validation is indicated before these techniques can be routinely utilized or applied to navigation systems or treatment planning software.
Current methods of monitoring treatment during RFA are inadequate and may represent the largest technical limitation of RFA today. Early detection of the tumor activity could potentially improve outcomes by allowing for early repeat intervention before regrowth results in a geometrically-unfavorable configuration.
In addition, method 1 (least squares) generates a rigid transformation, which involves 6 degrees of freedom (3 rotations and 3 translations). Method 2 (thin plate splines) is non-linear and can provide a richer registration than method 1 since this method can address non-linear registration problems (e. g., breathing artifacts, organ shift, and organ deformation). However, the accuracy of these two landmark registration methods is sensitive to user training and expertise in choosing landmarks. In addition, it can be time consuming and difficult to find enough landmarks to produce and acceptable registration.
However, the affine voxel-similarity automatic method is invariant to the user and often provides an acceptable result. This method has up to 12 degrees of freedom (3 rotations, 3 translations, 3 scale and 3 skew). While this may yield useful results, this method does not address breathing artifacts very well. Voxel similarity methods use statistics based on comparisons of voxel intensities between two datasets. Correlation ratio and cross-correlation measures are typically used to register intra-modality datasets. Normalized mutual information is typically used for inter-modality registration. Correlation ratio and normalized mutual information cost functions were used in this study for method 3 for intra and inter modality registration, respectively.
To fully visualize two fused images, it is important to be able to adjust the colorization or lookup tables, brightness, and contrast of each image independently. In addition, it is also important to be able to adjust the amount of blending between the two fused images. Having the ability to modify these image attributes greatly improves the visualization of lesions, vessels, and necrotic tissue. Such visualization is vital to the accurate assessment of RFA safety margins (see and ). The most subjectively effective color schemes were saved, which allowed the further automation of routine post-processing steps. In addition, surface-rendering techniques allowed for localization and the visual quantification of both pre-treatment lesion and post-treatment ablation volumes. Image processing and multimodality fusion are mature diagnostic tools that should be further evaluated for potential utility during interventional radiology procedures.