Development and validation of a hybrid simulator for ultrasound-guided laparoscopic common bile duct exploration

BACKGROUND

Ultrasound-guided laparoscopic common bile duct exploration (LCBDE) is the surgical management of choledocholithiasis. The procedure presents significant benefits to patients but still fails to be generalised because of the complex set of skills it requires. A simulator for ultrasound-guided LCBDE would allow trainee surgeons as well as experienced surgeons who perform this surgery seldomly to practice and gain confidence.

METHODS

This article presents the development and validation of an easily reproducible hybrid simulator for ultrasound-guided LCBDE which integrates real and virtual components of the task. We first developed a physical model made of silicone. The fabrication technique is replicable and allows quick and easy production of multiple models. We then applied virtual components onto the model to create training for laparoscopic ultrasound examination. Combined with a commercially available lap-trainer and surgical equipment, the model can be used for training the fundamental steps of the surgery through the trans-cystic or trans-choledochal approaches. The simulator was evaluated through face, content, and construct validation.

RESULTS

Two novices, eight middle grades, and three experts were recruited to test the simulator. The results of the face validation showed that the surgeons found the model realistic visually and felt realistic when performing the different steps of the surgery. The content validation indicated the usefulness of having a training system to practice the choledochotomy, the choledochoscopy and stone retrieval, and the suturing. The construct validation highlighted the ability of the simulator to differentiate between surgeons with various levels of expertise.

CONCLUSIONS

The hybrid simulator presented is a low-cost yet realistic model which allows the surgeons to practice the technical skills required for trans-cystic and trans-choledochal ultrasound-guided LCBDE.

Additivity Constrained Linearisation of Camera Calibration Data

When characterising a digital camera spectrally or colourimetrically, the camera response to a generally diffusely reflecting colour chart is often employed. The recorded responses to the light incident from each colour patch are typically not linearly related to the power of the irradiance on the chart, and the irradiance varies with position on the chart. This necessitates a linearisation of the responses. We present a new single image colour chart-based estimation method of responses, that are linearly related to camera response values known as ground truth. The method estimates the spatial geometry of the irradiance incident on the chart attenuated by lens vignetting and compensates individually for volumetric and per colour channel non-linearities, including compensation for physical scene and camera properties in a pipeline of successive signal transformations between the estimated linear and the given recorded responses. The estimation is controlled by introducing a novel Additivity Principle of linear responses, which is derived from the spectral reflectances of the coloured surfaces on the colour chart, observing that linear relations of the spectral reflectances are equal to the relations of the corresponding linear responses. Crucially, the additivity principle is not subject to metamerism. The method is fundamentally solely reliant on a one-shot set of one triplet of response values sampled from each patch of a colour chart with known spectral reflectances, where rendition level, gray scale, illuminant, camera sensor curves, irradiance geometry, vignetting, moderate specular reflection, colour space, colour correction, gamut correction and noise level are unknown.

A sensor-data-based denoising framework for hyperspectral images

Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term accounts the structural proposition of a hyperspectral image cube. We evaluate the approach on a synthetic dataset that guarantees a noise-free ground truth, and the best results are achieved when the dark current is taken into account.