Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions

Morphological variation of gubernacular tracts for permanent mandibular canines in eruption: a three-dimensional analysis

OBJECTIVES

This study aims to evaluate the morphological features of gubernacular tract (GT) for erupting permanent mandibular canines at different ages from 5 to 9 years old with a three-dimensional (3D) measurement method.

METHODS

The cone-beam CT images of 50 patients were divided into five age groups. The 3D models of the GT for mandibular canines were reconstructed and analysed. The characteristics of the GT, including length, diameter, ellipticity, tortuosity, superficial area, volume, and the angle between the canine and GT, were evaluated using a centreline fitting algorithm.

RESULTS

Among the 100 GTs that were examined, the length of the GT for mandibular canines decreased between the ages of 5 and 9 years, while the diameter increased until the age of 7 years. Additionally, the ellipticity and tortuosity of the GT decreased as age advanced. The superficial area and volume exhibited a trend of initially increasing and then decreasing. The morphological variations of the GT displayed heterogeneous changes during different periods.

CONCLUSIONS

The 3D measurement method effectively portrayed the morphological attributes of the GT for mandibular canines. The morphological characteristics of the GT during the eruption process exhibited significant variations. The variations in morphological changes may indicate different stages of mandibular canine eruption.

Dose reduction and image enhancement in micro-CT using deep learning

BACKGROUND

In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. Much higher resolutions are needed to achieve scale-equivalent discriminatory capabilities in rodents as those in humans. High resolution imaging however comes at the expense of increased scan times and higher doses. Specifically, with preclinical longitudinal imaging, there are concerns that dose accumulation may affect experimental outcomes of animal models.

PURPOSE

Dose reduction efforts under the ALARA (as low as reasonably achievable) principles are thus a key point of attention. However, low dose CT acquisitions inherently induce higher noise levels which deteriorate image quality and negatively impact diagnostic performance. Many denoising techniques already exist, and deep learning (DL) has become increasingly popular for image denoising, but research has mostly focused on clinical CT with limited studies conducted on preclinical CT imaging. We investigate the potential of convolutional neural networks (CNN) for restoring high quality micro-CT images from low dose (noisy) images. The novelty of the CNN denoising frameworks presented in this work consists of utilizing image pairs with realistic CT noise present in the input as well as the target image used for the model training; a noisier image acquired with a low dose protocol is matched to a less noisy image acquired with a higher dose scan of the same mouse.

METHODS

Low and high dose ex vivo micro-CT scans of 38 mice were acquired. Two CNN models, based on a 2D and 3D four-layer U-Net, were trained with mean absolute error (30 training, 4 validation and 4 test sets). To assess denoising performance, ex vivo mice and phantom data were used. Both CNN approaches were compared to existing methods, like spatial filtering (Gaussian, Median, Wiener) and iterative total variation image reconstruction algorithm. Image quality metrics were derived from the phantom images. A first observer study (n = 23) was set-up to rank overall quality of differently denoised images. A second observer study (n = 18) estimated the dose reduction factor of the investigated 2D CNN method.

RESULTS

Visual and quantitative results show that both CNN algorithms exhibit superior performance in terms of noise suppression, structural preservation and contrast enhancement over comparator methods. The quality scoring by 23 medical imaging experts also indicates that the investigated 2D CNN approach is consistently evaluated as the best performing denoising method. Results from the second observer study and quantitative measurements suggest that CNN-based denoising could offer a 2-4× dose reduction, with an estimated dose reduction factor of about 3.2 for the considered 2D network.

CONCLUSIONS

Our results demonstrate the potential of DL in micro-CT for higher quality imaging at low dose acquisition settings. In the context of preclinical research, this offers promising future prospects for managing the cumulative severity effects of radiation in longitudinal studies.

Performance evaluation of a micro-CT system for laboratory animal imaging with iterative reconstruction capabilities

BACKGROUND

In recent years, there has been a rapid proliferation in micro-computed tomography (micro-CT) systems becoming more available for routine preclinical research, with applications in many areas including bone, lung, cancer and cardiac imaging. Micro-CT provides the means to non-invasively acquire detailed anatomical information, but high-resolution imaging comes at the cost of longer scan times and higher doses, which is not desirable given the potential risks related to x-ray radiation. To achieve dose reduction and higher throughputs without compromising image quality (noise management), fewer projections can be acquired. This is where iterative reconstruction methods can have the potential to reduce noise since these algorithms can better handle sparse projection data, compared to filtered backprojection PURPOSE: We evaluate the performance characteristics of a compact benchtop micro-CT scanner that provides iterative reconstruction capabilities with GPU-based acceleration. More specifically, we thereby investigate the potential benefit of iterative reconstruction methods for dose reduction.

METHODS

Based on a series of phantom experiments, the benchtop micro-CT system was characterized in terms of image uniformity, noise, low contrast detectability, linearity and spatial resolution. Whole-body images of a plasticized ex vivo mouse phantom were also acquired. Different acquisition protocols (general-purpose versus high-resolution, including low dose scans) and different reconstruction strategies (analytic versus iterative algorithms: FDK, ISRA, ISRA-TV) were compared.

RESULTS

Signal uniformity was maintained across the radial and axial field-of-view (no cupping effect) with an average difference in Hounsfield units (HU) between peripheral and central regions below 50. For low contrast detectability, regions with at least ∆HU of 40 to surrounding material could be discriminated (for rods of 2.5 mm diameter). A high linear correlation (R² = 0.997) was found between measured CT values and iodine concentrations (0-40 mg/ml). Modulation transfer function (MTF) calculations on a wire phantom evaluated a resolution of 10.2 lp/mm at 10% MTF that was consistent with the 8.3% MTF measured on the 50 μm bars (10 lp/mm) of a bar-pattern phantom. Noteworthy changes in signal-to-noise and contrast-to-noise values were found for different acquisition and reconstruction protocols. Our results further showed the potential of iterative reconstruction methods to deliver images with less noise and artefacts.

CONCLUSIONS

In summary, the micro-CT system for laboratory animal imaging that was evaluated in the present work was shown to provide a good combination of performance characteristics between image uniformity, low contrast detectability and resolution in short scan times. With the iterative reconstruction capabilities of this micro-CT system in mind (ISRA and ISRA-TV), the adoption of such algorithms by GPU-based acceleration enables the integration of noise reduction methods which here demonstrated potential for high quality imaging at reduced doses. This article is protected by copyright. All rights reserved.

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

BACKGROUND AND OBJECTIVE

As Computed Tomography scans are an essential medical test, many techniques have been proposed to reconstruct high-quality images using a smaller amount of radiation. One approach is to employ algebraic factorization methods to reconstruct the images, using fewer views than the traditional analytical methods. However, their main drawback is the high computational cost and hence the time needed to obtain the images, which is critical in the daily clinical practice. For this reason, faster methods for solving this problem are required.

METHODS

In this paper, we propose a new reconstruction method based on the QR factorization that is very efficient on affordable equipment (standard multicore processors and standard Solid-State Drives) by using Out-Of-Core techniques.

RESULTS

Combining both affordable hardware and the new software proposed in our work, the images can be reconstructed very quickly and with high quality. We analyze the reconstructions using real Computed Tomography images selected from a dataset, comparing the QR method to the LSQR and FBP. We measure the quality of the images using the metrics Peak Signal-To-Noise Ratio and Structural Similarity Index, obtaining very high values. We also compare the efficiency of using spinning disks versus Solid-State Drives, showing how the latter performs the Input/Output operations in a significantly lower amount of time.

CONCLUSIONS

The results indicate that our proposed me thod and software are valid to efficiently solve large-scale systems and can be applied to the Computed Tomography reconstruction problem to obtain high-quality images.

Dosimetry in Micro-computed Tomography: a Review of the Measurement Methods, Impacts, and Characterization of the Quantum GX Imaging System

Purpose

X-ray micro-computed tomography (μCT) is a widely used imaging modality in preclinical research with applications in many areas including orthopedics, pulmonology, oncology, cardiology, and infectious disease. X-rays are a form of ionizing radiation and, therefore, can potentially induce damage and cause detrimental effects. Previous reviews have touched on these effects but have not comprehensively covered the possible implications on study results. Furthermore, interpreting data across these studies is difficult because there is no widely accepted dose characterization methodology for preclinical μCT. The purpose of this paper is to ensure in vivo μCT studies can be properly designed and the data can be appropriately interpreted.

Procedures

Studies from the scientific literature that investigate the biological effects of radiation doses relevant to μCT were reviewed. The different dose measurement methodologies used in the peer-reviewed literature were also reviewed. The CT dose index 100 (CTDI₁₀₀) was then measured on the Quantum GX μCT instrument. A low contrast phantom, a hydroxyapatite phantom, and a mouse were also imaged to provide examples of how the dose can affect image quality.

Results

Data in the scientific literature indicate that scenarios exist where radiation doses used in μCT imaging are high enough to potentially bias experimental results. The significance of this effect may relate to the study outcome and tissue being imaged. CTDI₁₀₀ is a reasonable metric to use for dose characterization in μCT. Dose rates in the Quantum GX vary based on the amount of material in the beam path and are a function of X-ray tube voltage. The CTDI₁₀₀ in air for a Quantum GX can be as low as 5.1 mGy for a 50 kVp scan and 9.9 mGy for a 90 kVp scan. This dose is low enough to visualize bone both in a mouse image and in a hydroxyapatite phantom, but applications requiring higher resolution in a mouse or less noise in a low-contrast phantom benefit from longer scan times with increased dose.

Conclusions

Dose management should be considered when designing μCT studies. Dose rates in the Quantum GX are compatible with longitudinal μCT imaging.

A novel weighted total difference based image reconstruction algorithm for few-view computed tomography

In practical applications of computed tomography (CT) imaging, due to the risk of high radiation dose imposed on the patients, it is desired that high quality CT images can be accurately reconstructed from limited projection data. While with limited projections, the images reconstructed often suffer severe artifacts and the edges of the objects are blurred. In recent years, the compressed sensing based reconstruction algorithm has attracted major attention for CT reconstruction from a limited number of projections. In this paper, to eliminate the streak artifacts and preserve the edge structure information of the object, we present a novel iterative reconstruction algorithm based on weighted total difference (WTD) minimization, and demonstrate the superior performance of this algorithm. The WTD measure enforces both the sparsity and the directional continuity in the gradient domain, while the conventional total difference (TD) measure simply enforces the gradient sparsity horizontally and vertically. To solve our WTD-based few-view CT reconstruction model, we use the soft-threshold filtering approach. Numerical experiments are performed to validate the efficiency and the feasibility of our algorithm. For a typical slice of FORBILD head phantom, using 40 projections in the experiments, our algorithm outperforms the TD-based algorithm with more than 60% gains in terms of the root-mean-square error (RMSE), normalized root mean square distance (NRMSD) and normalized mean absolute distance (NMAD) measures and with more than 10% gains in terms of the peak signal-to-noise ratio (PSNR) measure. While for the experiments of noisy projections, our algorithm outperforms the TD-based algorithm with more than 15% gains in terms of the RMSE, NRMSD and NMAD measures and with more than 4% gains in terms of the PSNR measure. The experimental results indicate that our algorithm achieves better performance in terms of suppressing streak artifacts and preserving the edge structure information of the object.