[TOPIC][TEAM MEMBERS][MENTOR][IMPORTANCE][METHODS][DEPENDENCIES]Validation plan600:446 Computer Integrated Surgery Proposal Sheng Xu [email protected][TOPIC] Volumetric Reconstruction from Projected Images[TEAM MEMBERS]Sheng Xu [MENTOR]Gabor Fichtinger[DELIVERABLES]1. MICCAI paper 2. A new algorithm for Volumetric Reconstruction from Projected Images3. A CAS Application for the treatment of AVM4. Test results of phantom data and patient image data5. Documentation of algorithm and codes.[IMPORTANCE]Contrast enhanced angiography has been default modality in AVM radiosurgery and itis also an emerging modality in minimally invasive interventional management ofvascularly hyperactive targets. A major obstacle in volumetric planning of local therapiesof such lesions is to determine the three dimensional shape and volume of the target fromits projective X-ray images.The problem of approximate volumetric reconstruction has not been researched duringlast half decade since the fusion of angiograpy, CT and MRI images are largely availablefor radiosurgery treatment planning, like in the popular BrainLAB or Radionics systems.Typical radiosurgery systems back-project the silhouettes onto reconstructed CT or MRIslices, but do not provide volumetric appreciation of the lesion itself. Less sophisticatedsystem approximate the target volume to an ellipsoid whose primary axes coincide withthe three largest diameters of the lesion. This technique requires generous safety marginaround the target, which is not acceptable in many cases. Elliptical volume estimation isalso frequently applied in X-ray guided brachytherapy planning, especially outside theUnited States. Our interest has been further redeemed in this reconstruction problem dueto the raising potential of mono-modal fluoroscopic planning and execution of roboticallyassisted thermal ablations of hyperactive liver lesions. In general, it is impossible to reconstruct exactly an object in 3D from its projectiveimages. Worse yet, in angiography images the target does not present well definedboundaries and the doctor must depend on subjective clinical judgment when outliningthe target. The problem is mathematically over-determined, because there is no such threedimensional object that produces the exact same projections as outlined by the physician.This phenomenon becomes prominent when more than two projections are used and itbecomes prohibitive for many known algorithms when the X-ray source is allowed torotate around multiple axes. Several reconstruction methods are known that placelimitations on the number of projections and the rotational freedom of the X-ray source,the most recent one was published by Foroni in 1996 [3].In 1996, during the peak of interest in AVM radiosurgery Yeung et al. [2] proposed amethod based on pure back-projection from angiograpic silhouettes. A fundamental1600:446 Computer Integrated Surgery Proposal Sheng Xu [email protected] disregarded by the authors is that back-projection alone produces only a cloud ofdisjoint voxels that has to be solidified after the fact, in order to receive a solid 3D object.Yeung also failed to address the issue that the shadows obtained by a forward projectionof a reconstructed object will not match the previously drawn silhouettes, because thesilhouettes are always drawn inconsistently. (Again, the reason is that soft tissue targetsand especially AVMs do not have well defined boundaries.) Back-projection alone doesnot allow for quantitative assessment of the consistency of silhouettes. On the other hand,this feature is highly desirable when the silhouettes are subject to intra-operative clinicaljudgment.Parallel to medical applications, a family of shape recovery methods have emerged inthe fields of pattern recognition and computer vision. These algorithms, besides beingrather complex and difficult to implement, also assume near exact silhouettes of theobjects, therefore, they are not suitable for our purpose.All problems considered, we critically need a volumetric and shape estimation methodthat (A) uses no prior information of the shape and volume to be reconstructed, (B) doesnot require exact silhouettes, (C) accepts arbitrary number of images, (D) acceptsarbitrary projection angles.The method is universally applicable in many X-ray guided volumetric treatments.Initial applications are being advocated for stereotactic radiosurgery of arteriovenousmalformations (AVMs) and radio frequency ablation of liver lesions.[METHODS]After the silhouettes are drawn in each 2D image, the contours are digitized, filled, andpixelized. When the system goes operational or real patients, the silhouettes will bedrawn by the physicians. The resulting binary image contains pixels of value 0 thatcorrespond to the background and pixels of value 1 that correspond to the inside of thesilhouette. Fluoroscopic images are typically used at their original resolution, but onemay consider re-sampling when speed becomes a critical issue in intra-operativetreatment planning. For simplicity, we reconstruct one object at a time.We use a bi-value discrete 3D function to describe the object in a voxelized 3Dvolume, T(x,y,z)=1 if the voxel at (x,y,z) belongs to the object and T(x,y,z)=0 if the voxeldoes not belong to the object. Voxel size is a free parameter trading off betweenperformance and resolution: if the voxels are too small, then the overall performancedeclines; but if the voxels are too large, then spatial resolution worsens. Typically, 1 mmcubic voxels work adequately in most clinical situations.Ideally, the extent and location of the encompassing voxelized volume should be thesmallest volume that guarantees to contain the object being reconstructed. Using back-projection, we determine the smallest bounding box whose silhouette would cover theoutlined target in each image, then the box is voxelized in Cartesian coordinates. The useof a tightly bounding box effectively reduces the extent of voxel volume, which is acritical factor in performance. (For highly irregular and/or eccentric objects we caneasily create a set of overlapping boxes or spheres instead of one, using a relativelystraightforward linear programming approach. This feature, however, is
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