Comparison between radiation exposure levels using an image intensifier and a flat-panel detector-based system in image-guided central venous catheter placement in children weighing less than 10 kg.

BACKGROUND: Ultrasound-guided central venous puncture and fluoroscopic guidance during central venous catheter (CVC) positioning optimizes technical success and lowers the complication rates in children, and is therefore considered standard practice. OBJECTIVE: The purpose of this study was to compare the radiation exposure levels recorded during CVC placement in children weighing less than 10 kg in procedures performed using an image intensifier-based angiographic system (IIDS) to those performed in a flat-panel detector-based interventional suite (FPDS). MATERIALS AND METHODS: A retrospective review of 96 image-guided CVC placements, between January 2008 and October 2013, in 49 children weighing less than 10 kg was performed. Mean age was 8.2 ± 4.4 months (range: 1-22 months). Mean weight was 7.1 ± 2.7 kg (range: 2.5-9.8 kg). The procedures were classified into two categories: non-tunneled and tunneled CVC placement. RESULTS: Thirty-five procedures were performed with the IIDS (21 non-tunneled CVC, 14 tunneled CVC); 61 procedures were performed with the FPDS (47 non-tunneled CVC, 14 tunneled CVC). For non-tunneled CVC, mean DAP was 113.5 ± 126.7 cGy cm(2) with the IIDS and 15.9 ± 44.6 cGy · cm(2) with the FPDS (P < 0.001). For tunneled CVC, mean DAP was 84.6 ± 81.2 cGy · cm(2) with the IIDS and 37.1 ± 33.5 cGy cm(2) with the FPDS (P = 0.02). CONCLUSION: The use of flat-panel angiographic equipment reduces radiation exposure in small children undergoing image-guided CVC placement.

A retrospective study to reduce prostate biopsy cores by a real time interactive tool.

OBJECTIVE: Prostate carcinoma (PCa) is one of the most frequent neoplasms, with more than 110.000 new cases/year in Europe. As PCa is not clearly demonstrable at transrectal ultrasound (TRUS), guidelines on TRUS guided biopsy suggest to perform a random tissue sampling (at least 8-12 "cores" depending on gland volume). Although accuracy grows with core number, patient discomfort and adverse event probability grow as well. Thus it would be worth to aim to reduce the number of prostate biopsy cores without loss of diagnostic accuracy. MATERIALS AND METHODS: A retrospective study was performed to evaluate the feasibility of an improved version of a rtCAB tool developed at DEIS (University of Bologna) for the reduction of prostate biopsy cores. rtCAB is an innovative processing technique which enhances TRUS video stream by a live false color overlay image that helps the physician to perform the biopsy by guiding the sampling into target zones. In order to train rtCAB, a monocentric, single operator prostate gland adenocarcinoma database has been built. The database enlists 81 patients, for a total of 743 prostate byoptic (PBx) cores and 14860 ROI. For each patient we collected age, PSA levels, digital rectal examination (DRE) findings, presence or absence of focal lesions, and prostate volume. During TRUS, raw ultrasound data were acquired and associated to each PBx core. For each core we collected both the radio frequency (RF) signal and the histological outcome. RESULTS: The whole system was optimized for reducing the number of false positives while preserving an acceptable number of false negatives. Comparing to a classical PBx approach (8-12 cores), the estimated positive predictive value (PPV) of our method increased from 25% to 40%, with an overall sensitivity of 85%. CONCLUSIONS: Preliminary results show that the proposed tool can provide real-time feedback to the operator during TRUS. Sensitivity and PPV values suggest that a reduction of almost 50% the number of biopsy cores without losing in diagnostic accuracy is feasible. A prospective study is needed to further confirm these preliminary retrospective results.

A restoration framework for ultrasonic tissue characterization.

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of l(2)-norm or (1)-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and l(1)-norm deconvolution techniques attests to the superiority of the proposed algorithm.

Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer.

Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.