The point spread function of spiral CT

Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

Purpose

The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters.

Approach

We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods.

Results

The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553 ± 0.0031 .

Conclusions

We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

Self-supervised denoising of Nyquist-sampled volumetric images via deep learning

Purpose

Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling provides guarantees about the maximum difference between adjacent slices in a volumetric image, which allows denoising to be performed without access to clean images. We aim to show that our method is more broadly applicable and more effective than other self-supervised denoising algorithms on real biomedical images, and provides comparable performance to algorithms that need clean images during training.

Approach

We first provide a theoretical analysis of noise2Nyquist and an upper bound for denoising error based on sampling rate. We go on to demonstrate its effectiveness in denoising in a simulated example as well as real fluorescence confocal microscopy, computed tomography, and optical coherence tomography images.

Results

We find that our method has better denoising performance than existing self-supervised methods and is applicable to datasets where clean versions are not available. Our method resulted in peak signal to noise ratio (PSNR) within 1 dB and structural similarity (SSIM) index within 0.02 of supervised methods. On medical images, it outperforms existing self-supervised methods by an average of 3 dB in PSNR and 0.1 in SSIM.

Conclusion

noise2Nyquist can be used to denoise any volumetric dataset sampled at at least the Nyquist rate making it useful for a wide variety of existing datasets.

Harmonization of in-plane resolution in CT using multiple reconstructions from single acquisitions

PURPOSE

To providea methodology that removes the spatial variability of in-plane resolution using different CT reconstructions. The methodology does not require any training, sinogram, or specific reconstruction method.

METHODS

The methodology is formulated as a reconstruction problem. The desired sharp image is modeled as an unobservable variable to be estimated from an arbitrary number of observations with spatially variant resolution. The methodology comprises three steps: (1) density harmonization, which removes the density variability across reconstructions; (2) point spread function (PSF) estimation, which estimates a spatially variant PSF with arbitrary shape; (3) deconvolution, which is formulated as a regularized least squares problem. The assessment was performed with CT scans of phantoms acquired with three different Siemens scanners (Definition AS, Definition AS+, Drive). Four low-dose acquisitions reconstructed with backprojection and iterative methods were used for the resolution harmonization. A sharp, high-dose (HD) reconstruction was used as a validation reference. The different factors affecting the in-plane resolution (radial, angular, and longitudinal) were studied with regression analysis of the edge decay (between 10% and 90% of the edge spread function (ESF) amplitude).

RESULTS

Results showed that the in-plane resolution improves remarkably and the spatial variability is substantially reduced without compromising the noise characteristics. The modulated transfer function (MTF) also confirmed a pronounced increase in resolution. The resolution improvement was also tested by measuring the wall thickness of tubes simulating airways. In all scanners, the resolution harmonization obtained better performance than the HD, sharp reconstruction used as a reference (up to 50 percentage points). The methodology was also evaluated in clinical scans achieving a noise reduction and a clear improvement in thin-layered structures. The estimated ESF and MTF confirmed the resolution improvement.

CONCLUSION

We propose a versatile methodology to reduce the spatial variability of in-plane resolution in CT scans by leveraging different reconstructions available in clinical studies. The methodology does not require any sinogram, training, or specific reconstruction, and it is not limited to a fixed number of input images. Therefore, it can be easily adopted in multicenter studies and clinical practice. The results obtained with our resolution harmonization methodology evidence its suitability to reduce the spatially variant in-plane resolution in clinical CT scans without compromising the reconstruction's noise characteristics. We believe that the resolution increase achieved by our methodology may contribute in more accurate and reliable measurements of small structures such as vasculature, airways, and wall thickness.

Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets

Methods for reliable femur segmentation enable the execution of quality retrospective studies and building of robust screening tools for bone and joint disease. An enhance-and-segment pipeline is proposed for proximal femur segmentation from computed tomography datasets. The filter is based on a scale-space model of cortical bone with properties including edge localization, invariance to density calibration, rotation invariance, and stability to noise. The filter is integrated with a graph cut segmentation technique guided through user provided sparse labels for rapid segmentation. Analysis is performed on 20 independent femurs. Rater proximal femur segmentation agreement was 0.21 mm (average surface distance), 0.98 (Dice similarity coefficient), and 2.34 mm (Hausdorff distance). Manual segmentation added considerable variability to measured failure load and volume (CV_RMS > 5%) but not density. The proposed algorithm considerably improved inter-rater reproducibility for all three outcomes (CV_RMS < 0.5%). The algorithm localized the periosteal surface accurately compared to manual segmentation but with a slight bias towards a smaller volume. Hessian-based filtering and graph cut segmentation localizes the periosteal surface of the proximal femur with comparable accuracy and improved precision compared to manual segmentation.

Generative-based airway and vessel morphology quantification on chest CT images

Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstructive pulmonary disease (COPD), while accurately sizing vessels can help identify arterial and venous changes in lung regions that may determine future disorders. However, traditional methods are often limited due to image resolution and artifacts. We propose a Convolutional Neural Regressor (CNR) that provides cross-sectional measurement of airway lumen, airway wall thickness, and vessel radius. CNR is trained with data created by a generative model of synthetic structures which is used in combination with Simulated and Unsupervised Generative Adversarial Network (SimGAN) to create simulated and refined airways and vessels with known ground-truth. For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods. Then, in-vivo validation is performed by analyzing the association between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter, two well-known measures of lung function and airway disease, for airways. For vessels, we assess the correlation between our estimate of the small-vessel blood volume and the lungs' diffusing capacity for carbon monoxide (DLCO). The results demonstrate that Convolutional Neural Networks (CNNs) provide a promising direction for accurately measuring vessels and airways on chest CT images with physiological correlates.

Bronchial Cartilage Assessment with Model-Based GAN Regressor

In the last two decades, several methods for airway segmentation from chest CT images have been proposed. The following natural step is the development of a tool to accurately assess the morphology of the bronchial system in all its aspects to help physicians better diagnosis and prognosis complex pulmonary diseases such as COPD, chronic bronchitis and bronchiectasis. Traditional methods for the assessment of airway morphology usually focus on lumen and wall thickness and are often limited due to resolution and artifacts of the CT image. Airway wall cartilage is an important characteristic related to airway integrity that has shown to be deteriorated during the airway disease process. In this paper, we propose the development of a Model-Based GAN Regressor (MBGR) that, thanks to a model-based GAN generator, generate synthetic airway samples with the morphological components necessary to resemble the appearance of real airways on CT at will and that simultaneously measures lumen, wall thickness, and amount of cartilage on pulmonary CT images. The method is evaluated by first computing the relative error on generated images to show that simulating the cartilage helps improve the morphological quantification of the airway structure. We then propose a cartilage index that summarizes the degree of cartilage of bronchial trees structures and perform an indirect validation with subjects with COPD. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways morphology, with the final goal to improve the diagnosis and prognosis of pulmonary diseases.

A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound

The detection, quantification and characterization of coronary atherosclerotic plaques has a major effect on the diagnosis and treatment of coronary artery disease (CAD). Different studies have reported and evaluated the noninvasive ability of Computed Tomography Coronary Angiography (CTCA) to identify coronary plaque features. The identification of calcified plaques (CP) and non-calcified plaques (NCP) using CTCA has been extensively studied in cardiovascular research. However, NCP detection remains a challenging problem in CTCA imaging, due to the similar intensity values of NCP compared to the perivascular tissue, which surrounds the vasculature. In this work, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of CP and NCP utilizing CTCA images and we compare the findings with virtual histology intravascular ultrasound (VH-IVUS) and manual expert's annotations. Bland-Altman analyses were employed to assess the agreement between the presented methodology and VH-IVUS. The assessment of the plaque volume, the lesion length and the plaque area in 18 coronary lesions indicated excellent correlation with VH-IVUS. More specifically, for the CP lesions the correlation of plaque volume, lesion length and plaque area was 0.93, 0.84 and 0.85, respectively, whereas the correlation of plaque volume, lesion length and plaque area for the NCP lesions was 0.92, 0.95 and 0.81, respectively. In addition to this, the segmentation of the lumen, CP and NCP in 1350 CTCA slices indicated that the mean value of DICE coefficient is 0.72, 0.7 and 0.62, whereas the mean HD value is 1.95, 1.74 and 1.95, for the lumen, CP and NCP, respectively.

Accurate Measurement of Airway Morphology on Chest CT Images

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.

Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling

BACKGROUND

Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification.

OBJECTIVES

In this study, a Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin.

METHODS

The method presented incorporates information fusion from multiple modalities, modelling of image resolution (measurement blurring), image noise, two priors helping to reduce noise and partial volume. Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation. Regularization based on isotropic and directional Markov Random Field priors is integrated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image. Sub-methods as variations of the main method are generated by using a combination of the models.

RESULTS

Testing of the sub-methods is performed on two patients using single modality three-dimensional (3D) image (magnetic resonance, MR or computerized tomography, CT) as well as registered MR-CT images with information fusion. Numerical, visual and statistical analyses of the methods are conducted. High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images. The methods are also compared with our Bayesian segmentation method proposed in a previous study. The performance is found to be similar to our previous Bayesian approach, but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent.

CONCLUSIONS

The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head. Meanwhile, free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more systematic way as compared with our previous study.

Image analysis for cystic fibrosis: computer-assisted airway wall and vessel measurements from low-dose, limited scan lung CT images

Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results.

Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry

With the wide dissemination of computed tomography (CT) screening for lung cancer, measuring the nodule volume accurately with computer-aided volumetry software is increasingly important. Many studies for determining the accuracy of volumetry software have been performed using a phantom with artificial nodules. These phantom studies are limited, however, in their ability to reproduce the nodules both accurately and in the variety of sizes and densities required. Therefore, we propose a new approach of using computer-simulated nodules based on the point spread function measured in a CT system. The validity of the proposed method was confirmed by the excellent agreement obtained between computer-simulated nodules and phantom nodules regarding the volume measurements. A practical clinical evaluation of the accuracy of volumetry software was achieved by adding simulated nodules onto clinical lung images, including noise and artifacts. The tested volumetry software was revealed to be accurate within an error of 20 % for nodules >5 mm and with the difference between nodule density and background (lung) (CT value) being 400-600 HU. Such a detailed analysis can provide clinically useful information on the use of volumetry software in CT screening for lung cancer. We concluded that the proposed method is effective for evaluating the performance of computer-aided volumetry software.

Approximations of noise covariance in multi-slice helical CT scans: impact on lung nodule size estimation

Multi-slice computed tomography (MSCT) scanners have become popular volumetric imaging tools. Deterministic and random properties of the resulting CT scans have been studied in the literature. Due to the large number of voxels in the three-dimensional (3D) volumetric dataset, full characterization of the noise covariance in MSCT scans is difficult to tackle. However, as usage of such datasets for quantitative disease diagnosis grows, so does the importance of understanding the noise properties because of their effect on the accuracy of the clinical outcome. The goal of this work is to study noise covariance in the helical MSCT volumetric dataset. We explore possible approximations to the noise covariance matrix with reduced degrees of freedom, including voxel-based variance, one-dimensional (1D) correlation, two-dimensional (2D) in-plane correlation and the noise power spectrum (NPS). We further examine the effect of various noise covariance models on the accuracy of a prewhitening matched filter nodule size estimation strategy. Our simulation results suggest that the 1D longitudinal, 2D in-plane and NPS prewhitening approaches can improve the performance of nodule size estimation algorithms. When taking into account computational costs in determining noise characterizations, the NPS model may be the most efficient approximation to the MSCT noise covariance matrix.

Airway wall attenuation: a biomarker of airway disease in subjects with COPD

The computed tomographic (CT) densities of imaged structures are a function of the CT scanning protocol, the structure size, and the structure density. For objects that are of a dimension similar to the scanner point spread function, CT will underestimate true structure density. Prior investigation suggests that this process, termed contrast reduction, could be used to estimate the strength of thin structures, such as cortical bone. In this investigation, we endeavored to exploit this process to provide a CT-based measure of airway disease that can assess changes in airway wall thickening and density that may be associated with the mural remodeling process in subjects with chronic obstructive pulmonary disease (COPD). An initial computer-based study using a range of simulated airway wall sizes and densities suggested that CT measures of airway wall attenuation could detect changes in both wall thickness and structure density. A second phantom-based study was performed using a series of polycarbonate tubes of known density. The results of this again demonstrated the process of contrast reduction and further validated the computer-based simulation. Finally, measures of airway wall attenuation, wall thickness, and wall area (WA) divided by total cross-sectional area, WA percent (WA%), were performed in a cohort of 224 subjects with COPD and correlated with spirometric measures of lung function. The results of this analysis demonstrated that wall attenuation is comparable to WA% in predicting lung function on univariate correlation and remain as a statistically significant correlate to the percent forced expiratory volume in 1 s predicted when adjusted for measures of both emphysema and WA%. These latter findings suggest that the quantitative assessment of airway wall attenuation may offer complementary information to WA% in characterizing airway disease in subjects with COPD.

About objective 3-d analysis of airway geometry in computerized tomography

The technology of multislice X-ray computed tomography (MSCT) provides volume data sets with approximately isotropic resolution, which permits a noninvasive 3-D measurement and quantification of airway geometry. In different diseases, like emphysema, chronic obstructive pulmonary disease (COPD), or cystic fribrosis, changes in lung parenchyma are associated with an increase in airway wall thickness. In this paper, we describe an objective measuring method of the airway geometry in the 3-D space. The limited spatial resolution of clinical CT scanners in comparison to thin structures like airway walls causes difficulties in the measurement of the density and the thickness of these structures. Initially, these difficulties will be addressed and then a new method is introduced to circumvent the problems. Therefore the wall thickness is approximated by an integral based closed-form solution, based on the volume conservation property of convolution. We evaluated the method with a phantom containing 10 silicone tubes and proved the repeatability in datasets of eight pigs scanned twice. Furthermore, a comparison of CT datasets of 16 smokers and 15 nonsmokers was done. Further medical studies are ongoing.

Classifying CT image data into material fractions by a scale and rotation invariant edge model

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges--arch model--is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.

Improvement of spatial resolution in the longitudinal direction for isotropic imaging in helical CT

Experiments were conducted to confirm the isotropic spatial resolution of multislice CT with a 0.5 mm slice thickness. Isotropic spatial resolution means that the spatial resolution in the transaxial plane (X-Y plane) and that in the longitudinal direction (Z direction) are equivalent. To obtain point spread function (PSF) values in the X-Y-Z directions, three-dimensional voxel data were obtained by helical scanning of a bead phantom. The modulation transfer function (MTF) values were then obtained by three-dimensional Fourier transform of the PSF. Evaluation of the spatial resolution in the X-Y-Z directions by the MTF values showed that the spatial resolution in the Z direction does not depend on the reconstruction kernel used. It was also found that the spatial resolution in the Z direction, as compared with that in the X-Y plane, is superior with the standard kernel for the abdomen and is inferior with the high-definition kernel for the ears/bones. By performing sharpening filter processing in the Z direction with a high-definition kernel, comparable spatial resolution could be obtained in the X-Y-Z directions. It was confirmed that adjusting the spatial resolution in the Z direction with the reconstruction kernel used is an effective method for isotropic imaging.