Visual impression and texture analysis of advanced modeled iterative reconstruction (ADMIRE): improved assessment of image quality in CT for better estimation of dose reduction potential.

To evaluate the image quality (IQ) of advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) applying image texture and image visual impression as a supplement to physical parameters such as noise level and spatial resolution. An ACR-phantom with four modules was examined at different radiation dose levels. To characterise the image texture, two Haralick texture parameters, contrast and entropy, were assessed at different dose levels and reconstruction algorithms. The visual impression of images and the low-contrast detectability were evaluated by the structural similarity index (SSIM). The spatial resolution was determined by the modulation transfer functions and the line spread function. The Haralick texture parameters, contrast and entropy, decreased with increasing ADMIRE levels. ADMIRE III, IV and V offered a comparable contrast and entropy to those calculated by filtered back projection (FBP) with a radiation dose reduction up to 50%. SSIM (low-contrast detectability) improved with increasing ADMIRE levels. SSIM calculated by ADMIRE IV and V revealed comparable IQ to FBP with a decreased CTDIvolup to 50%. Spatial resolution was retained up to 90% dose reduction. Compared to FBP at the same dose level, the image noise decreased up to 61% with higher ADMIRE levels (σFBP= 17.3 HU andσADMIREV= 10.6 HU at 6.65 mGy). Taking texture analysis and visual perception into account, a more realistic assessment of the dose reduction potential of ADMIRE can be achieved than quality metrics based alone on physical measurements.

Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties.

Barren lands are being transformed into agricultural fields with the growing demand for agriculture-based products. Hence, monitoring these regions for better planning and management is crucial. Surveying with high-resolution RS (remote sensing) satellites like Worldview-2 provides a faster and cheaper solution than conventional surveys. In the study, the arid region comprising cropland and barrenlands are efficiently and autonomously delineated using its spectral and textural properties using state-of-the-art random forest (RF) ensemble classifiers. The textural information window size is optimized and at a GLCM (gray-level co-occurrence matrix) window size of 13, a stable trend in classification accuracy was observed. A further rise in window sizes did not improve the classification accuracy; beyond GLCM 19, a decline in accuracy was observed. Comparing GLCM-13 RF with the no-GLCM RF classifier, the GLCM-based classifiers performed better; thus, the textural information assisted in removing isolated crop-classified outputs that are falsely predicted pixel groups. Still, it also obscured information about barren lands present within croplands. Delineation accuracy was 93.8 % for the no-GLCM RF classifier, whereas, for the GLCM-13 RF classifier, an accuracy of 97.3 % was observed. Thus, overall, a 3.5 % improvement in accuracy was observed while using the GLCM RF classifier with window size 13. The textural information with proper calibration over high-spatial resolution datasets improves crop delineation in the present study. Henceforth, a more accurate cropland identification will provide a better estimate of the actual cropland area in such an arid region, which will assist in formulating a better resource management policy.

Noninvasive assessment of coronary microvascular dysfunction using SPECT myocardial perfusion imaging and myocardial perfusion entropy quantification in a rodent model of type 2 diabetes.

PURPOSE: Coronary microvascular dysfunction (CMVD) plays a major role in the occurrence of cardiovascular events (CVE). We recently suggested the clinical potential of myocardial perfusion entropy (MPE) quantification from SPECT myocardial perfusion images (MPI) for the prognosis of CVE occurrence. We hypothesized that the quantification of MPE from SPECT MPI would allow the assessment of CMVD-related MPE variations in a preclinical model of type 2 diabetes (T2D) including treatment with the anti-diabetic incretin liraglutide (LIR). METHODS: Optimal conditions for the preclinical quantification of MPE using 201Tl SPECT MPI were determined in rats with a T2D-like condition induced by a high-fat diet and streptozotocin injection (feasibility study, n = 43). Using such conditions, echocardiography and post-mortem LV capillary density evaluation were then used in order to assess the effect of LIR and the ability of MPE to assess CMVD (therapeutic study, n = 39). RESULTS: The feasibility study identified dobutamine stress and acute NO synthase and cyclooxygenase inhibition as optimal conditions for the quantification of MPE, with significant increases in MPE being observed in T2D animals (P < 0.01 vs controls). In the therapeutic study, T2D rats were hyperglycemic (5.5 ± 0.5 vs 1.1 ± 0.3 g/L for controls, P < 0.001) and had a significantly lower left ventricular ejection fraction (LVEF) (65 ± 4% vs 74 ± 9%, P < 0.01) and LV capillary density (2400 ± 300 vs 2800 ± 600 mm-3, P < 0.05). LIR partially restored glycemia (3.9 ± 0.6 g/L, P < 0.05 vs controls and T2D), totally prevented LVEF impairment (72 ± 7%, P = NS vs CTL), with no significant effect on capillary density. MPE was significantly increased in T2D rats (7.6 ± 0.5 vs 7.1 ± 0.5, P < 0.05), with no significant improvement in T2D-LIR rats (7.4 ± 0.4, P = NS vs controls and T2D). CONCLUSION: MPE quantification allowed the preclinical noninvasive assessment of CMVD. Both MPE and capillary density quantification suggested that LIR did not improve T2D-induced CMVD. The relevance of MPE for CMVD assessment warrants further clinical investigation.

Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes.

BACKGROUND: Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance. RESULTS: We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in oocyte appearance in the nematode Caenorhabditis elegans. Max-min value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max-min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max-min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging. CONCLUSIONS: The Max-min value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.

Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer.

BACKGROUND: Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has a worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance. OBJECTIVE: To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4 from GS 4 + 3 PCa. METHODS: Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved and randomly divided into training (60%) and testing (40%) sets. Axial image showing the maximum tumor size is selected in the T2WI and ADC maps for further image texture feature analysis. Three hundred texture features are computed from each region of interest (ROI) using MaZda software. Feature reduction is implemented to obtain 30 optimal features, which are then used to generate the most discriminative features (MDF). Receiver operating characteristic (ROC) curve analysis is performed on MDF values in the training sets to achieve cutoff values for determining the correct rates of discrimination between two Gleason patterns in the testing sets. RESULTS: ROC analysis on T2WI and ADC-derived MDF values in the training set (n = 51) results in a mean area under the curve (AUC) of 0.953±0.025 (with sensitivity 0.9274±0.0615 and specificity 0.897±0.069), and 0.985±0.013 (with sensitivity 0.9636±0.0446 and specificity 0.9726±0.0258), respectively. Using the corresponding MDF cutoffs, 95.3% (ranges from 76.5% to 100%) and 94.1% (ranged from 76.5% to 100%) of test cases (n = 34) are correctly discriminated using T2WI and ADC-derived MDF values, respectively. CONCLUSIONS: The study demonstrates that using T2WI and ADC-derived image texture parameters has a potential predictive role in differentiating GS 3 + 4 and GS 4 + 3 PCa.

The impact of Platelet Rich Plasma (PRP) in osseointegration of oral implants in dental panoramic radiography: texture based evaluation.

PURPOSE: In this study the temporal texture differentiation associated with the bone formation properties, around loaded oral implants after Platelet Rich Plasma (PRP) employment, was investigated in Panoramic Radiographs. MATERIALS AND METHODS: Thirty eligible patients are randomly assigned to two groups. The test group received PRP application around new implants, while in the control group no PRP treatment was made. The bone-to-implant contact region was analyzed in a clinical sample of 60 Digitized Panoramic Radiographs, 30 corresponding to immediate implant loading (Class-I) and 30 after an 8 month follow-up period (Class-II). This region was sampled by 1146 circular Regions-of-Interest (ROIs), resulting from a specifically designed segmentation scheme based on Markov-Random-Fields (MRF). From each ROI, 41 textural features were extracted, then reduced to a subset of 4 features due to redundancy and employed as input to Receiver-Operating-Characteristic (ROC) analysis, to assess the textural differentiation between two classes. RESULTS: The selected subset, achieved Area-Under-Curve (AUC) values ranging from 0.77-0.81 in the PRP group, indicating the significant temporal textural differentiation has been made. In the control group, the AUC values ranged from 0.56-0.68 demonstrating lesser osseo integration activity. CONCLUSION: This study provides evidences that PRP application may favor bone formation around loaded dental implants that could modify the dental treatment planning.

Feasibility study to assess lesion repair in relapsing-remitting multiple sclerosis: A randomized controlled pilot clinical trial of domperidone add-on treatment.

BACKGROUND: Identification of therapies to promote repair in multiple sclerosis is challenged by the lack of an accepted trial model and associated outcome measures. The goal of this study was to determine the feasibility of a new trial model that enrolls disease modifying therapy (DMT)-treated relapsing-remitting multiple sclerosis (RRMS) participants who have enhancing lesions on clinically indicated brain MRI, and to explore estimates of lesion repair using MRI. METHODS: This was a single site randomized controlled clinical trial. Recruitment took place between November 2015 and January 2019, with final follow-up in February 2019. DMT-treated RRMS participants aged 18-60 years with at least one gadolinium-enhancing lesion on clinically indicated brain MRI were included. Participants were randomized 2:1 to oral domperidone add-on 10-mg three times daily for 16 weeks or no add-on treatment (control). The primary outcomes were feasibility of the model pre-defined as recruitment of 24 participants within 36 months with a 79 % completion rate, and MRI outcomes of lesion repair measured at 16 and 32 weeks using texture analysis, magnetization transfer imaging (MTI), and diffusion tensor imaging (DTI). The impact of domperidone on serum prolactin at 6 and 16 weeks was also evaluated. RESULTS: Of 237 RRMS participants screened, 17 (14 women) were randomized: 12 to domperidone add-on and 5 to control. All completed the study. Median (range) age was 38.9 (26.7-55.9) years; EDSS was 1.5 (1.0-3.5); and disease duration was 12.9 (2.9-23.3) years. Both groups showed improvement in MRI texture and diffusion fractional anisotropy (FA) at 32 weeks, and the domperidone group demonstrated additional recovery at 16 weeks in both texture and FA. There was no significant group difference in any MRI outcome. Of the 12 domperidone participants, 7 had ≥4x higher serum prolactin than normal. There were no serious adverse events. CONCLUSION: The recruitment target was not met and therefore the trial model was not feasible despite a full completion rate. The imaging techniques performed well, especially MRI texture analysis, suggesting the sample size being sufficient for estimating lesion repair. The main challenge of this trial model may be recruiting gadolinium-enhancing lesions in DMT-treated RRMS participants. Prolactin is safe and may hold promise as a remyelination therapy. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02493049.

Posterior Cortical Cognitive Deficits Are Associated With Structural Brain Alterations in Mild Cognitive Impairment in Parkinson's Disease.

Context: Cognitive impairments are common in patients with Parkinson's disease (PD) and are heterogeneous in their presentation. The "dual syndrome hypothesis" suggests the existence of two distinct subtypes of mild cognitive impairment (MCI) in PD: a frontostriatal subtype with predominant attentional and/or executive deficits and a posterior cortical subtype with predominant visuospatial, memory, and/or language deficits. The latter subtype has been associated with a higher risk of developing dementia. Objective: The objective of this study was to identify structural modifications in cortical and subcortical regions associated with each PD-MCI subtype. Methods: One-hundred and fourteen non-demented PD patients underwent a comprehensive neuropsychological assessment as well as a 3T magnetic resonance imaging scan. Patients were categorized as having no cognitive impairment (n = 41) or as having a frontostriatal (n = 16), posterior cortical (n = 25), or a mixed (n = 32) MCI subtype. Cortical regions were analyzed using a surface-based Cortical thickness (CTh) method. In addition, the volumes, shapes, and textures of the caudate nuclei, hippocampi, and thalami were studied. Tractometric analyses were performed on associative and commissural white matter (WM) tracts. Results: There were no between-group differences in volumetric measurements and cortical thickness. Shape analyses revealed more abundant and more extensive deformations fields in the caudate nuclei, hippocampi, and thalami in patients with posterior cortical deficits compared to patients with no cognitive impairment. Decreased fractional anisotropy (FA) and increased mean diffusivity (MD) were also observed in the superior longitudinal fascicle, the inferior fronto-occipital fascicle, the striato-parietal tract, and the anterior and posterior commissural tracts. Texture analyses showed a significant difference in the right hippocampus of patients with a mixed MCI subtype. Conclusion: PD-MCI patients with posterior cortical deficits have more abundant and more extensive structural alterations independently of age, disease duration, and severity, which may explain why they have an increased risk of dementia.

Proto-Object Based Saliency Model With Texture Detection Channel.

The amount of visual information projected from the retina to the brain exceeds the information processing capacity of the latter. Attention, therefore, functions as a filter to highlight important information at multiple stages of the visual pathway that requires further and more detailed analysis. Among other functions, this determines where to fixate since only the fovea allows for high resolution imaging. Visual saliency modeling, i.e. understanding how the brain selects important information to analyze further and to determine where to fixate next, is an important research topic in computational neuroscience and computer vision. Most existing bottom-up saliency models use low-level features such as intensity and color, while some models employ high-level features, like faces. However, little consideration has been given to mid-level features, such as texture, for visual saliency models. In this paper, we extend a biologically plausible proto-object based saliency model by adding simple texture channels which employ nonlinear operations that mimic the processing performed by primate visual cortex. The extended model shows statistically significant improved performance in predicting human fixations compared to the previous model. Comparing the performance of our model with others on publicly available benchmarking datasets, we find that our biologically plausible model matches the performance of other models, even though those were designed entirely for maximal performance with little regard to biological realism.

Parametric mapping of cellular morphology in plant tissue sections by gray level granulometry.

BACKGROUND: The cellular morphology of plant organs is strongly related to other physical properties such as shape, size, growth, mechanical properties or chemical composition. Cell morphology often vary depending on the type of tissue, or on the distance to a specific tissue. A common challenge in quantitative plant histology is to quantify not only the cellular morphology, but also its variations within the image or the organ. Image texture analysis is a fundamental tool in many areas of image analysis, that was proven efficient for plant histology, but at the scale of the whole image. RESULTS: This work presents a method that generates a parametric mapping of cellular morphology within images of plant tissues. It is based on gray level granulometry from mathematical morphology for extracting image texture features, and on Centroidal Voronoi Diagram for generating a partition of the image. Resulting granulometric curves can be interpreted either through multivariate data analysis or by using summary features corresponding to the local average cell size. The resulting parametric maps describe the variations of cellular morphology within the organ. CONCLUSIONS: We propose a methodology for the quantification of cellular morphology and of its variations within images of tissue sections. The results should help understanding how the cellular morphology is related to genotypic and / or environmental variations, and clarify the relationships between cellular morphology and chemical composition of cell walls.

Automatic gauze tracking in laparoscopic surgery using image texture analysis.

BACKGROUND AND OBJECTIVE: Inadvertent retained surgical gauzes are an infrequent medical error but can have devastating consequences in the patient health and in the surgeon professional reputation. This problem seems easily preventable implementing standardized protocols for counting but due to human errors it still persists in surgery. The omnipresence of gauzes, their small size, and their similar appearance with tissues when they are soaked in blood make this error eradication really complex. In order to reduce the risk of accidental retention of surgical sponges in laparoscopy operations, in this paper we present an image processing system that tracks the gauzes on the video captured by the endoscope. METHODS: The proposed image processing application detects the presence of gauzes in the video images using texture analysis techniques. The process starts dividing the video frames into square blocks and each of these blocks is analyzed to determine whether it is similar to the gauze pattern. The video processing algorithm has been tested in a laparoscopic simulator under different conditions: with clean, slightly stained and soaked in blood gauzes as well as against different biological background tissues. Several methods, including different Local Binary Patterns (LBP) techniques and a convolutional neural network (CNN), have been analyzed in order to achieve a reliable detection in real time. RESULTS: The proposed LBP algorithm classifies the individual blocks in the image with 98% precision and 94% sensitivity which is sufficient to make a robust detection of any gauze that appears in the endoscopic video even if it is stained or soaked in blood. The results provided by the CNN are superior with 100% precision and 97% sensitivity, but due to the high computational demand, real-time video processing is not attainable in this case with standard hardware. CONCLUSIONS: The algorithm presented in this paper is a valuable tool to avoid the retention of surgical gauzes not only because of its reliability but also because it processes the video transparently and unattended, without the need for additional manipulation of special equipment in the operating room.

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests.

PURPOSE: Routine quarterly quality assurance (QA) assessment of single photon emission computed tomography (SPECT) systems includes analysis of multipurpose phantoms containing spheres and rods of various sizes. When evaluated by accreditation agencies, criteria applied to assess image quality are largely subjective. Determining a quantified image characteristic metric that emulates human reader impressions of image quality could be quite useful. Our investigation was conducted to ascertain whether image texture analysis metrics, such as those applied to PET scans to detect neoplasms, could prove helpful in linking qualitative statements of phantom sphere and rod visibility to quantified parameters. Because it is not obvious whether it is preferable to submit reconstructions to accrediting agencies performed using typical clinical (CLIN) protocol processing parameters or to follow agencies' filtered backprojection (FBP) suggestions, we applied texture analysis metrics to determine the degree to which these choices affect equipment capability assessment. METHODS AND MATERIALS: Data were processed retrospectively for 125 different Tc-99 m SPECT scans of standardized phantoms for 14 rotating Anger detector systems as part of routine quarterly QA. Algorithms were written to compute several classes of image metrics: quantile curve metrics, image texture analysis gray-level co-occurrence matrix (GLCM) metrics, contrast metrics, and count histogram metrics. For qualitative image scores, two experienced physicists independently graded sphere and rod visibility on a 5-level scale and assigned dichotomous visibility scores, without knowledge of quantified texture analysis metrics or each other's readings. The same phantom was used to collect 15 additional data sets with two dual-detector SPECT/CT systems, reconstructed both by FBP parameters that have been suggested by accrediting agencies and by manufacturers' default settings for CLIN SPECT/CT bone imaging protocols by ordered subsets expectation maximization (OSEM), incorporating attenuation correction using the CT scan. Image characteristics metrics were compared for FBP and CLIN reconstructions. RESULTS: For spheres, the metric with the strongest rank correlation with 5-level scale readings was the quantile curve slope (ρ = 0.83, P < 0.0001), while for rods it was GLCM Energy normalized to the maximum GLCM Energy value (EnergyNorm) (ρ = -0.88, P < 0.0001). Compared to dichotomous readings, the metric with the highest ROC area under curve (AUC) for spheres was the quantile curve slopes (AUC = 96 ± 1%, sensitivity = 91%, specificity = 90%), and for rods was EnergyNorm (AUC = 98 ± 1%, sensitivity = 92%, specificity = 95%). Image contrast was higher for all sphere sizes and rod EnergyNorm was lower for sectors of intermediate-sized rods for FBP compared to CLIN reconstructions, in agreement with more rods judged to be visible from FBP than CLIN reconstructions (47% vs 33%, P = 0.002). CONCLUSIONS: When preparing to submit quality assurance images of standardized phantoms to accrediting agencies, a reliable gauge of sphere and rod visibility can be predicted accurately using quantified reader-independent image texture analysis metrics, which also provide a useful basis for choosing among alternative image reconstruction options.

Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer.

AIM: To investigate the merits of texture analysis on parametric maps derived from pharmacokinetic modeling with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as imaging biomarkers for the prediction of treatment response in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: In this retrospective study, 19 HNSCC patients underwent pre- and intra-treatment DCE-MRI scans at a 1.5T MRI scanner. All patients had chemo-radiation treatment. Pharmacokinetic modeling was performed on the acquired DCE-MRI images, generating maps of volume transfer rate (K(trans)) and volume fraction of the extravascular extracellular space (ve). Image texture analysis was then employed on maps of K(trans) and ve, generating two texture measures: Energy (E) and homogeneity. RESULTS: No significant changes were found for the mean and standard deviation for K(trans) and ve between pre- and intra-treatment (P > 0.09). Texture analysis revealed that the imaging biomarker E of ve was significantly higher in intra-treatment scans, relative to pretreatment scans (P < 0.04). CONCLUSION: Chemo-radiation treatment in HNSCC significantly reduces the heterogeneity of tumors.

Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics.

The identification of interdicted nuclear or radioactive materials requires the application of dedicated techniques. In this work, a new approach for characterizing powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. 26 different UOCs samples were evaluated applying the Angle Measure Technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1000× magnification by Scanning Electron Microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90% with Principal Component Analysis (PCA), while partial least square discriminant analysis (PLS-DA) applied only on the 14 black colour UOCs powder samples, allowed their classification only on the basis of their surface texture features (sensitivity>0.6; specificity>0.6). This preliminary study shows that this method was able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation.

Frequential versus spatial colour textons for breast TMA classification.

Advances in digital pathology are generating huge volumes of whole slide (WSI) and tissue microarray images (TMA) which are providing new insights into the causes of cancer. The challenge is to extract and process effectively all the information in order to characterize all the heterogeneous tissue-derived data. This study aims to identify an optimal set of features that best separates different classes in breast TMA. These classes are: stroma, adipose tissue, benign and benign anomalous structures and ductal and lobular carcinomas. To this end, we propose an exhaustive assessment on the utility of textons and colour for automatic classification of breast TMA. Frequential and spatial texton maps from eight different colour models were extracted and compared. Then, in a novel way, the TMA is characterized by the 1st and 2nd order Haralick statistical descriptors obtained from the texton maps with a total of 241 × 8 features for each original RGB image. Subsequently, a feature selection process is performed to remove redundant information and therefore to reduce the dimensionality of the feature vector. Three methods were evaluated: linear discriminant analysis, correlation and sequential forward search. Finally, an extended bank of classifiers composed of six techniques was compared, but only three of them could significantly improve accuracy rates: Fisher, Bagging Trees and AdaBoost. Our results reveal that the combination of different colour models applied to spatial texton maps provides the most efficient representation of the breast TMA. Specifically, we found that the best colour model combination is Hb, Luv and SCT for all classifiers and the classifier that performs best for all colour model combinations is the AdaBoost. On a database comprising 628 TMA images, classification yields an accuracy of 98.1% and a precision of 96.2% with a total of 316 features on spatial textons maps.

(90)Y-PET/CT Imaging Quantification for Dosimetry in Peptide Receptor Radionuclide Therapy: Analysis and Corrections of the Impairing Factors.

PURPOSE: We evaluated the possibility to assess (90)Y-PET/CT imaging quantification for dosimetry in (90)Y-peptide receptor radionuclide therapy. METHODS: Tests were performed by Discovery 710 Elite (GE) PET/CT equipment. A body-phantom containing radioactive-coplanar-spheres was filled with (90)Y water solution to reproduce different signal-to-background-activity-ratios (S/N). We studied minimum detectable activity (MDA) concentration, contrast-to-noise ratio (CNR), and full-width-at-half-maximum (FWHM). Subsequently, three recovery coefficients (RC)-based correction approaches were evaluated: maximum-RC, resolution-RC, and isovolume-RC. The analysis of the volume segmentation thresholding method was also assessed to derive a relationship between the true volume of the targets and the threshold to be applied to the PET images. (90)Y-PET/CT imaging quantification was then achieved on some patients and related with preclinical tests. Moreover, the dosimetric evaluation was obtained on the target regions. RESULTS: CNR value was greater than 5 if the MDA was greater than 0.2 MBq/mL with no background activity and 0.5-0.7 MBq/mL with S/N ranging from 3 to 6. FWHM was equal to 7 mm. An exponential fitting of isovolume RCs-based correction technique was adopted for activity quantification. Adaptive segmentation thresholding exponential curves were obtained and applied for target volume identification in three signal-to-background-activity-ratios. The imaging quantification study and dosimetric evaluations in clinical cases was feasible and the results were coherent with those obtained in preclinical tests. CONCLUSIONS: (90)Y-PET/CT imaging quantification is possible both in phantoms and in patients. Absorbed dose evaluations in clinical applications are strongly related to targets activity concentration.