A photon recycling approach to the denoising of ultra-low dose X-ray sequences

Robust learning-based X-ray image denoising - potential pitfalls, their analysis and solutions

PURPOSE

Since guidance based on X-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the X-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.

METHOD

We present a robust learning-based denoising strategy involving model- based simulations of low-dose X-ray images during the training phase. The method also utilizes a data-driven normalization step - based on an X-ray imaging model - to stabilize the mixed signal-dependent noise associated with X-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose X-ray images on the denoising results.

RESULTS

A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band co efficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy.

CONCLUSION

The proposed learning-based denoising strategy provides scope for significant X-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training ph.

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images

PURPOSE

Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient.

MATERIALS AND METHODS

We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels.

RESULTS

Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained.

CONCLUSION

The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.

Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

BACKGROUND

Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient's dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too.

RESULTS

For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter.

CONCLUSIONS

The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient's X-ray dose.

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system

PURPOSE

2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients' kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality.

MATERIALS AND METHODS

To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used.

RESULTS

Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best.

CONCLUSION

Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.

An analytical approach for the simulation of realistic low-dose fluoroscopic images

PURPOSE

The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.

METHOD

We introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images.

RESULTS

We have compared several corresponding regions of the associated real and simulated low-dose images-obtained from two different imaging systems-visually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regions-from 80 pairs of real and simulated low-dose regions-belonging to the same distribution has been accepted in 81.43% of the cases.

CONCLUSION

The results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.

Robust navigation support in lowest dose image setting

PURPOSE

Clinical cardiac electrophysiology (EP) is concerned with diagnosis and treatment of cardiac arrhythmia describing abnormality or perturbation in the normal activation sequence of the myocardium. With the recent introduction of lowest dose X-ray imaging protocol for EP procedures, interventional image enhancement has gained crucial importance for the well-being of patients as well as medical staff.

METHODS

In this paper, we introduce a novel method to detect and track different EP catheter electrodes in lowest dose fluoroscopic sequences based on [Formula: see text]-sparse coding and online robust PCA (ORPCA). Besides being able to work on real lowest dose sequences, the underlying methodology achieves simultaneous detection and tracking of three main EP catheters used during ablation procedures.

RESULTS

We have validated our algorithm on 16 lowest dose fluoroscopic sequences acquired during real cardiac ablation procedures. In addition to expert labels for 2 sequences, we have employed a crowdsourcing strategy to obtain ground truth labels for the remaining 14 sequences. In order to validate the effect of different training data, we have employed a leave-one-out cross-validation scheme yielding an average detection rate of [Formula: see text].

CONCLUSION

Besides these promising quantitative results, our medical partners also expressed their high satisfaction. Being based on [Formula: see text]-sparse coding and online robust PCA (ORPCA), our method advances previous approaches by being able to detect and track electrodes attached to multiple different catheters.