Radiation dose reduction in CT-guided spine biopsies does not reduce diagnostic yield.

BACKGROUND AND PURPOSE: CT-guided biopsy is the most commonly used method to obtain tissue for diagnosis in suspected cases of malignancy involving the spine. The purpose of this study was to demonstrate that a low-dose CT-guided spine biopsy protocol is as effective in tissue sampling as a regular-dose protocol, without adversely affecting procedural time or complication rates. MATERIALS AND METHODS: We retrospectively reviewed all patients who underwent CT-guided spine procedures at our institution between May 2010 and October 2013. Biopsy duration, total number of scans, total volume CT dose index, total dose-length product, and diagnostic tissue yield of low-dose and regular-dose groups were compared. RESULTS: Sixty-four patients were included, of whom 31 underwent low-dose and 33 regular-dose spine biopsies. There was a statistically significant difference in total volume CT dose index and total dose-length product between the low-dose and regular-dose groups (P < .0001). There was no significant difference in the total number of scans obtained (P = .3385), duration of procedure (P = .149), or diagnostic tissue yield (P = .6017). CONCLUSIONS: Use of a low-dose CT-guided spine biopsy protocol is a practical alternative to regular-dose approaches, maintaining overall quality and efficiency at reduced ionizing radiation dose.

Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.

We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.