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Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%. [ABSTRACT FROM AUTHOR]

The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes’ nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) has revolutionises the diagnosis of retinal disease based on the detection of microscopic rather than subcellular changes in retinal anatomy. However, currently the technique is limited to the detection of microscopic rather than subcellular changes in retinal anatomy. However, coherence based imaging is extremely sensitive to both changes in optical contrast and cellular events at the micrometer scale, and can generate subtle changes in the spectral content of the OCT image. Here we test the hypothesis that OCT image speckle (image texture) contains information regarding otherwise unresolvable features such as organelle changes arising in the early stages of neuronal degeneration. Using ultrahigh resolution (UHR) OCT imaging at 800 nm (spectral width 140 nm) we developed a robust method of OCT image analyses, based on spatial wavelet and texture-based parameterisation of the image speckle pattern. For the first time we show that this approach allows the non-invasive detection and quantification of early apoptotic changes in neurons within 30 min of neuronal trauma sufficient to result in apoptosis. We show a positive correlation between immunofluorescent labelling of mitochondria (a potential source of changes in cellular optical contrast) with changes in the texture of the OCT images of cultured neurons. Moreover, similar changes in optical contrast were also seen in the retinal ganglion cell- inner plexiform layer in retinal explants following optic nerve transection. The optical clarity of the explants was maintained throughout in the absence of histologically detectable change. Our data suggest that UHR OCT can be used for the non-invasive quantitative assessment of neuronal health, with a particular application to the assessment of early retinal disease. [ABSTRACT FROM AUTHOR]

We examined the influence of linear perspective cues and texture gradients in the perceptual rescaling of stimuli over a highly-salient Ponzo illusion of a corridor. We performed two experiments using the Method of Constant Stimuli where participants judged the size of one of two rings. In experiment 1, one ring was presented in the upper visual-field at the end of the corridor and the other in the lower visual-field at the front of the corridor. The perceived size of the top and bottom rings changed as a function of the availability of linear perspective and textures. In experiment 2, only one ring was presented either at the top or the bottom of the image. The perceived size of the top but not the bottom ring changed as a function of the availability of linear perspective and textures. In both experiments, the effects of the cues were additive. Perceptual rescaling was also stronger for the top compared to the bottom ring. Additional eye-tracking revealed that participants tended to gaze more in the upper than the lower visual-field. These findings indicate that top-down mechanisms provide an important contribution to the Ponzo illusion. Nonetheless, additional maximum likelihood estimation analyses revealed that linear perspective fulfilled a greater contribution in experiment 2, which is suggestive of a bottom-up mechanism. We conclude that both top-down and bottom-up mechanisms play important roles. However, the former seems to fulfil a more prominent role when both stimuli are presented in the image. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). Methods: T2w, DWI (12 b values, 0–2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. Results: In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. Conclusion: Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types. [ABSTRACT FROM AUTHOR]

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization. [ABSTRACT FROM AUTHOR]

As fetal gestational age increases, other modalities such as ultrasound have demonstrated increased levels of heterogeneity in the normal placenta. In this study, we introduce and apply ROI-based texture analysis to a retrospective fetal MRI database to characterize the second-order statistics of placenta and to evaluate the relationship between heterogeneity and gestational age. Positive correlations were observed for several Haralick texture metrics derived from fetal-brain specific T2-weighted and gravid uterus T1-weighted and T2-weighted images, confirming a quantitative increase in placental heterogeneity with gestational age. Our study shows the importance of identifying baseline MR textural changes at certain gestational ages from which placental diseased states may be compared. Specifically, when evaluating for placental invasion or insufficiency, findings should be evaluated in the context of the normal placental aging process, which occurs throughout gestation. [ABSTRACT FROM AUTHOR]

Pathology images are color in nature due to the use of chemical staining in biopsy examination. Aware of the high color diagnosticity in pathology images, this work introduces a compact rotation-invariant texture descriptor, named quantized diagnostic counter-color pattern (QDCP), for digital pathology image understanding. On the basis of color similarity quantified by the inner product of unit-length color vectors, local counter-color textons are indexed first. Then the underlined distribution of QDCP indexes is estimated by an image-wise histogram. Since QDCP is computed based on color difference directly, it is robust to small color variation usually observed in pathology images. This study also discusses QDCP’s extraction, parameter settings, and feature fusion techniques in a generic pathology image analysis pipeline, and introduces two more descriptors QDCP-LBP and QDCP/LBP. Experimentation on public pathology image sets suggests that the introduced color texture descriptors, especially QDCP-LBP, outperform prior color texture features in terms of strong descriptive power, low computational complexity, and high adaptability to different image sets. [ABSTRACT FROM AUTHOR]

Motion in a distorted virtual 3D space may cause visually induced motion sickness. Geometric distortions in stereoscopic 3D can result from mismatches among image capture, display, and viewing parameters. Three pairs of potential mismatches are considered, including 1) camera separation vs. eye separation, 2) camera field of view (FOV) vs. screen FOV, and 3) camera convergence distance (i.e., distance from the cameras to the point where the convergence axes intersect) vs. screen distance from the observer. The effect of the viewer’s head positions (i.e., head lateral offset from the screen center) is also considered. The geometric model is expressed as a function of camera convergence distance, the ratios of the three parameter-pairs, and the offset of the head position. We analyze the impacts of these five variables separately and their interactions on geometric distortions. This model facilitates insights into the various distortions and leads to methods whereby the user can minimize geometric distortions caused by some parameter-pair mismatches through adjusting of other parameter pairs. For example, in postproduction, viewers can correct for a mismatch between camera separation and eye separation by adjusting their distance from the real screen and changing the effective camera convergence distance. [ABSTRACT FROM AUTHOR]

Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level. [ABSTRACT FROM AUTHOR]

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively. [ABSTRACT FROM AUTHOR]

Objective: To investigate the feasibility and accuracy of texture analysis to distinguish through objective and quantitative image information between healthy and infarcted myocardium with computed tomography (CT). Materials and methods: Twenty patients (5 females; mean age 56±10years) with proven acute myocardial infarction (MI) and 20 patients (8 females; mean age 42±15years) with no cardiac abnormalities (hereafter termed controls) underwent contrast-enhanced cardiac CT. Short axis CT images of the left ventricle (LV) were reconstructed at the slice thicknesses 1mm, 2mm, and 5mm. Two independent, blinded readers segmented the LV in controls and patients. Texture analysis was performed yielding first-level features based on the histogram (variance, skewness, kurtosis, entropy), second-level features based on the gray-level co-occurrence matrix (GLCM) (contrast, correlation, energy and homogeneity), and third-level features based on the gray-level run-length matrix (GLRLM). Results: Inter-and intrareader agreement was good to excellent for all histogram (intraclass correlation coefficient (ICC):0.70–0.93) and for all GLCM features (ICC:0.66–0.99), and was variable for the GLRLM features (ICC:-0.12–0.99). Univariate analysis showed significant differences between patients and controls for 2/4 histogram features, 3/4 GLCM and for 6/11 GLRLM features and all assessed slice thicknesses (all,p<0.05). In a multivariate logistic regression model, the single best variable from each level, determined by ROC analysis, was included stepwise. The best model included kurtosis (OR 0.08, 95%CI:0.01–0.65,P = 0.018) and short run high gray-level emphasis (SRHGE, OR 0.97, 95%CI:0.94–0.99,P = 0.007), with an area-under-the-curve (AUC) of 0.90 (95%CI:0.80–0.99). The best results for kurtosis and SRHGE (AUC = 0.78) were obtained at a 5mm slice thickness. A cut-off value of 14.4 for kurtosis+0.013*SRHGE predicted acute MI with a sensitivity of 95% (specificity 55%). Conclusion: Our study illustrates the feasibility of texture analysis for distinguishing healthy from acutely infarcted myocardium with cardiac CT using objective, quantitative features, with most reproducible and accurate results at a short axis slice thickness of 5mm. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of automated grading of invasive breast carcinoma through the encoding of histological images as VLAD (Vector of Locally Aggregated Descriptors) representations on the Grassmann manifold. The proposed method considers each image as a set of multidimensional spatially-evolving signals that can be efficiently modeled through a higher-order linear dynamical systems analysis. Subsequently, each H&E (Hematoxylin and Eosin) stained breast cancer histological image is represented as a cloud of points on the Grassmann manifold, while a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. To evaluate the efficiency of the proposed methodology, two datasets with different characteristics were used. More specifically, we created a new medium-sized dataset consisting of 300 annotated images (collected from 21 patients) of grades 1, 2 and 3, while we also provide experimental results using a large dataset, namely BreaKHis, containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results have shown that the proposed method outperforms a number of state of the art approaches providing average classification rates of 95.8% and 91.38% with our dataset and the BreaKHis dataset, respectively. [ABSTRACT FROM AUTHOR]

A saliency attention model for predicting eye direction is proposed in this paper. This work is inspired by the success of the topological structure and Earth Mover’s Distance (EMD) approach. Firstly, we extract visual saliency features such as color contrast, intensity contrast, orientation, and texture. Then, we eliminate disconnected regions in the feature maps to keep topological structure. Secondly, we calculate center surround difference using across-scale EMD between different scales feature maps, rather than utilizing the Difference of Gaussian (DoG), which is used in many other saliency attention models. Thirdly, we across-scale fuse the feature maps in different scale and same feature. Lastly, we take advantage of competition function to calculate feature maps in same feature to form a saliency map, which is use to predict eye direction. Experimental results demonstrated the proposed model outperformed the state-of-the-art schemes in eye direction prediction community. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map. Student’s t-test was used to test the difference between group means. Analysis of covariance (ANCOVA) was performed with adjustments for age, sex, and tumor volume. The receiver operating characteristic (ROC) analysis was performed to compare diagnostic performances. Malignant myxoid-containing STTs had significantly higher kurtosis (P = 0.040), energy (P = 0.034), correlation (P<0.001), and homogeneity (P = 0.003), but significantly lower contrast (P<0.001) and variance (P = 0.001) compared with benign myxoid-containing STTs. Contrast showed the highest area under the curve (AUC = 0.923, P<0.001), sensitivity (94.12%), and specificity (86.96%). Our results reveal the potential utility of texture analysis of ADC maps for differentiating benign and malignant myxoid-containing STTs. [ABSTRACT FROM AUTHOR]

The objectives were to compare the performance of a segmentation algorithm, based on the minimization of an uncertainty function, to delineate contours of external elastic membrane and lumen of human coronary arteries imaged with 40 and 60 MHz IVUS, and to use values of this function to delineate portions of contours with highest uncertainty. For 8 patients, 40 and 60 MHz IVUS coronary data acquired pre- and post-interventions were used, for a total of 68,516 images. Manual segmentations of contours (on 2312 images) performed by experts at three core laboratories were the gold-standards. Inter-expert variability was highest on contour points with largest values of the uncertainty function (p < 0.001). Inter-expert variability was lower at 60 than 40 MHz for external elastic membrane (p = 0.013) and lumen (p = 0.024). Average differences in plaque (and atheroma) burden between algorithmic contours and experts’ contours were within inter-expert variability (p < 0.001). [ABSTRACT FROM AUTHOR]

Objective: The present work proposes a new clinical approach to TTFields therapy of glioblastoma. The approach combines targeted surgical skull removal (craniectomy) with TTFields therapy to enhance the induced electrical field in the underlying tumor tissue. Using computer simulations, we explore the potential of the intervention to improve the clinical efficacy of TTFields therapy of brain cancer. Methods: We used finite element analysis to calculate the electrical field distribution in realistic head models based on MRI data from two patients: One with left cortical/subcortical glioblastoma and one with deeply seated right thalamic anaplastic astrocytoma. Field strength was assessed in the tumor regions before and after virtual removal of bone areas of varying shape and size (10 to 100 mm) immediately above the tumor. Field strength was evaluated before and after tumor resection to assess realistic clinical scenarios. Results: For the superficial tumor, removal of a standard craniotomy bone flap increased the electrical field strength by 60–70% in the tumor. The percentage of tissue in expected growth arrest or regression was increased from negligible values to 30–50%. The observed effects were highly focal and targeted at the regions of pathology underlying the craniectomy. No significant changes were observed in surrounding healthy tissues. Median field strengths in tumor tissue increased with increasing craniectomy diameter up to 50–70 mm. Multiple smaller burr holes were more efficient than single craniectomies of equivalent area. Craniectomy caused no significant field enhancement in the deeply seated tumor, but rather a focal enhancement in the brain tissue underlying the skull defect. Conclusions: Our results provide theoretical evidence that small and clinically feasible craniectomies may provide significant enhancement of TTFields intensity in cerebral hemispheric tumors without severely compromising brain protection or causing unacceptable heating in healthy tissues. A clinical trial is being planned to validate safety and efficacy. [ABSTRACT FROM AUTHOR]

Objective: Evaluation of a new iterative reconstruction algorithm (IMR) for detection/rule-out of pulmonary embolism (PE) in ultra-low dose computed tomography pulmonary angiography (CTPA). Methods: Lower dose CT data sets were simulated based on CTPA examinations of 16 patients with pulmonary embolism (PE) with dose levels (DL) of 50%, 25%, 12.5%, 6.3% or 3.1% of the original tube current setting. Original CT data sets and simulated low-dose data sets were reconstructed with three reconstruction algorithms: the standard reconstruction algorithm “filtered back projection” (FBP), the first generation iterative reconstruction algorithm iDose and the next generation iterative reconstruction algorithm “Iterative Model Reconstruction” (IMR). In total, 288 CTPA data sets (16 patients, 6 tube current levels, 3 different algorithms) were evaluated by two blinded radiologists regarding image quality, diagnostic confidence, detectability of PE and contrast-to-noise ratio (CNR). Results: iDose and IMR showed better detectability of PE than FBP. With IMR, sensitivity for detection of PE was 100% down to a dose level of 12.5%. iDose and IMR showed superiority to FBP regarding all characteristics of subjective (diagnostic confidence in detection of PE, image quality, image noise, artefacts) and objective image quality. The minimum DL providing acceptable diagnostic performance was 12.5% (= 0.45 mSv) for IMR, 25% (= 0.89 mSv) for iDose and 100% (= 3.57 mSv) for FBP. CNR was significantly (p < 0.001) improved by IMR compared to FBP and iDose at all dose levels. Conclusion: By using IMR for detection of PE, dose reduction for CTPA of up to 75% is possible while maintaining full diagnostic confidence. This would result in a mean effective dose of approximately 0.9 mSv for CTPA. [ABSTRACT FROM AUTHOR]

Purpose: To assess image quality of CT pulmonary angiography (CTPA) at reduced radiation exposure (RD-CTPA) and contrast medium (CM) volume using two different iterative reconstruction (IR) algorithms (iDose4 and iterative model reconstruction (IMR)) in comparison to filtered back projection (FBP). Materials and Methods: 52 patients (body weight < 100 kg, mean BMI: 23.9) with suspected pulmonary embolism (PE) underwent RD-CTPA (tube voltage: 80 kV; mean CTDIvol: 1.9 mGy) using 40 ml CM. Data were reconstructed using FBP and two different IR algorithms (iDose4 and IMR). Subjective and objective image quality and conspicuity of PE were assessed in central, segmental, and subsegmental arteries. Results: Noise reduction of 55% was achieved with iDose4 and of 85% with IMR compared to FBP. Contrast-to-noise ratio significantly increased with iDose4 and IMR compared to FBP (p<0.05). Subjective image quality was rated significantly higher at IMR reconstructions in comparison to iDose4 and FBP. Conspicuity of central and segmental PE significantly improved with the use of IMR. In subsegmental arteries, iDose4 was superior to IMR. Conclusions: CTPA at reduced radiation exposure and contrast medium volume is feasible with the use of IMR, which provides improved image quality and conspicuity of pulmonary embolism in central and segmental arteries. [ABSTRACT FROM AUTHOR]

Objectives: To address the question as to whether echocardiographic and/or microcomputed tomography (microCT) analysis can be utilized to assess the extent of Coxsackie B virus (CVB) induced myocarditis in the absence of left ventricular dysfunction in the mouse. Background: Viral myocarditis is a significant clinical problem with associated inflammation of the myocardium and myocardial injury. Murine models of myocarditis are commonly used to study the pathophysiology of the disease, but methods for imaging the mouse myocardium have been limited to echocardiographic assessment of ventricular dysfunction and, to a lesser extent, MRI imaging. Methods: Using a murine model of myocarditis, we used both echocardiography and microCT to assess the extent of myocardial involvement in murine myocarditis using both wild-type mice and CVB cleavage-resistant dystrophin knock-in mice. Results: Areas of increased echogenicity were only observed in the myocardium of Coxsackie B virus infected mice. These echocardiographic abnormalities correlated with the extent of von Kossa staining (a marker of membrane permeability), inflammation, and fibrosis. Given that calcium phosphate uptake as imaged by von Kossa staining might also be visualized using microCT, we utilized microCT imaging which allowed for high-resolution, 3-dimensional images of radiodensities that likely represent calcium phosphate uptake. As with echocardiography, only mice infected with Coxsackie B virus displayed abnormal accumulation of calcium within individual myocytes indicating increased membrane permeability only upon exposure to virus. Conclusions: These studies demonstrate new, quantitative, and semi-quantitative imaging approaches for the assessment of myocardial involvement in the setting of viral myocarditis in the commonly utilized mouse model of viral myocarditis. [ABSTRACT FROM AUTHOR]

Low-dose protocols for respiratory gating in cardiothoracic small-animal imaging lead to streak artifacts in the images reconstructed with a Feldkamp-Davis-Kress (FDK) method. We propose a novel prior- and motion-based reconstruction (PRIMOR) method, which improves prior-based reconstruction (PBR) by adding a penalty function that includes a model of motion. The prior image is generated as the average of all the respiratory gates, reconstructed with FDK. Motion between respiratory gates is estimated using a nonrigid registration method based on hierarchical B-splines. We compare PRIMOR with an equivalent PBR method without motion estimation using as reference the reconstruction of high dose data. From these data acquired with a micro-CT scanner, different scenarios were simulated by changing photon flux and number of projections. Methods were evaluated in terms of contrast-to-noise-ratio (CNR), mean square error (MSE), streak artefact indicator (SAI), solution error norm (SEN), and correction of respiratory motion. Also, to evaluate the effect of each method on lung studies quantification, we have computed the Jaccard similarity index of the mask obtained from segmenting each image as compared to those obtained from the high dose reconstruction. Both iterative methods greatly improved FDK reconstruction in all cases. PBR was prone to streak artifacts and presented blurring effects in bone and lung tissues when using both a low number of projections and low dose. Adopting PBR as a reference, PRIMOR increased CNR up to 33% and decreased MSE, SAI and SEN up to 20%, 4% and 13%, respectively. PRIMOR also presented better compensation for respiratory motion and higher Jaccard similarity index. In conclusion, the new method proposed for low-dose respiratory gating in small-animal scanners shows an improvement in image quality and allows a reduction of dose or a reduction of the number of projections between two and three times with respect to previous PBR approaches. [ABSTRACT FROM AUTHOR]