Shape-based grey-level image interpolation

Impact of data transfer between treatment planning systems on dosimetric parameters

PURPOSE

In radiotherapy it is often necessary to transfer a patient's DICOM (Digital Imaging and COmmunications in Medicine) dataset from one system to another for re-treatment, plan-summation or registration purposes. The aim of the study is to evaluate effects of dataset transfer between treatment planning systems.

MATERIALS AND METHODS

Twenty-five patients treated in a 0.35T MR-Linac (MRidian, ViewRay) for locally-advanced pancreatic cancer were enrolled. For each patient, a nominal dose distribution was optimized on the planning MRI. Each plan was daily re-optimized if needed to match the anatomy and exported from MRIdian-TPS (ViewRay Inc.) to Eclipse-TPS (Siemens-Varian). A comparison between the two TPSs was performed considering the PTV and OARs volumes (cc), as well as dose coverages and clinical constraints.

RESULTS

From the twenty-five enrolled patients, 139 plans were included in the data comparison. The median values of percentage PTV volume variation are 10.8 % for each fraction, while percentage differences of PTV coverage have a mean value of -1.4 %. The median values of the percentage OARs volume variation are 16.0 %, 7.0 %, 10.4 % and 8.5 % for duodenum, stomach, small and large bowel, respectively. The percentage variations of the dose constraints are 41.0 %, 52.7 % and 49.8 % for duodenum, stomach and small bowel, respectively.

CONCLUSIONS

This study has demonstrated a non-negligible variation in size and dosimetric parameters when datasets are transferred between TPSs. Such variations should be clinically considered. Investigations are focused on DICOM structure algorithm employed by the TPSs during the transfer to understand the cause of such variations.

3D Visualization of Brain Tumors Using MR Images: A Survey

BACKGROUND

Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer.

DISCUSSION

It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling.

CONCLUSION

In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.

Polygon interpolation for serial cross sections

In this paper, a new technique for contour interpolation between slices is presented. We assumed that contour interpolation is equivalent to the interpolation of a polygon that approximates the object shape. The location of each polygon vertex is characterized by a set of parameters. Polygon interpolation can be performed on these parameters. These interpolated parameters are then used to reconstruct the vertices of the new polygon. Finally, the contour is approximated from this polygon using a cubic spline interpolation. This new technique takes into account the shape, the translation, the size, and the orientation of the object's contours. A comparison with regular shape-based interpolation is made on several object contours. The preliminary results show that this new method yields a better contour and is computationally more efficient than shape-based interpolation. This technique can be applied to gray-level images too. The interpolation result of an MR image does not show artifact of intermediate substance commonly seen in a typical linear gray-level interpolation.

Morphology-based three-dimensional interpolation

In many medical applications, the number of available two-dimensional (2-D) images is always insufficient. Therefore, the three-dimensional (3-D) reconstruction must be accomplished by appropriate interpolation methods to fill gaps between available image slices. In this paper, we propose a morphology-based algorithm to interpolate the missing data. The proposed algorithm consists of several steps. First, the object or hole contours are extracted using conventional image-processing techniques. Second, the object or hole matching issue is evaluated. Prior to interpolation, the centroids of the objects are aligned. Next, we employ a dilation operator to transform digital images into distance maps and we correct the distance maps if required. Finally, we utilize an erosion operator to accomplish the interpolation. Furthermore, if multiple objects or holes are interpolated, we blend them together to complete the algorithm. We experimentally evaluate the proposed method against various synthesized cases reported in the literature. Experimental results show that the proposed method is able to handle general object interpolation effectively.