scholarly journals Pancreas MRI segmentation into head, body, and tail enables regional quantitative analysis of heterogeneous disease

2021 ◽  
Author(s):  
Alexandre Triay Bagur ◽  
Paul Aljabar ◽  
Gerard R Ridgway ◽  
Michael Brady ◽  
Daniel Bulte
Keyword(s):  
Quantitative Imaging ◽  
Region Of Interest ◽  
Automated Analysis ◽  
Pancreatic Disease ◽  
Proton Density ◽  
Disease Heterogeneity ◽  
Pancreas Head ◽  
Pancreatic Fat ◽  
Significant Difference ◽  
Multiple Experts

Pancreatic disease can be spatially inhomogeneous. For this reason, quantitative imaging studies of the pancreas have often targeted the 3 main anatomical pancreatic parts, head, body, and tail, traditionally using a balanced region of interest (ROI) strategy. Existing automated analysis methods have implemented whole-organ segmentation, which provides an overall quantification, but fails to address spatial heterogeneity in disease. A method to automatically refine a whole-organ segmentation of the pancreas into head, body, and tail subregions is presented for abdominal magnetic resonance imaging (MRI). The subsegmentation method is based on diffeomorphic registration to a group average template image, where the parts are manually annotated. For a new whole-pancreas segmentation, the aligned template's part labels are automatically propagated to the segmentation of interest. The method is validated retrospectively on the UK Biobank imaging substudy (scanned using a 2-point Dixon protocol at 1.5 tesla), using a nominally healthy cohort of 100 subjects for template creation, and 50 independent subjects for validation. Pancreas head, body, and tail were annotated by multiple experts on the validation cohort, which served as the benchmark for the automated method's performance. Good intra-rater (Dice overlap mean, Head: 0.982, Body: 0.940, Tail: 0.961, N=30) as well as inter-rater (Dice overlap mean, Head: 0.968, Body: 0.905, Tail: 0.943, N=150) agreement was observed. No significant difference (Wilcoxon rank sum test, DSC, Head: p=0.4358, Body: p=0.0992, Tail: p=0.1080) was observed between the manual annotations and the automated method's predictions. Results on regional pancreatic fat assessment are also presented, by intersecting the 3-D parts segmentation with one 2-D multi-echo gradient-echo slice, available from the same scanning session, that was used to compute MRI proton density fat fraction (MRI-PDFF). Initial application of the method on a type 2 diabetes cohort showed the utility of the method for assessing pancreatic disease heterogeneity.

scholarly journals Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6199
Author(s):  
Chidozie N. Ogbonnaya ◽  
Xinyu Zhang ◽  
Basim S. O. Alsaedi ◽  
Norman Pratt ◽  
Yilong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
Spatial Relationship ◽  
Region Of Interest ◽  
Texture Features ◽  
Gray Level ◽  
Discriminative Ability ◽  
Significant Difference ◽  
Clinically Significant ◽  
Occurrence Matrix

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Calcium-Suppressed Technique in Dual-Layer Detector Computed Tomography to Evaluate Knee Articular Cartilage

2020 ◽  
Vol 16 ◽  
Author(s):  
Qinglin Meng ◽  
Mengqi Liu ◽  
Weiwei Deng ◽  
Ke Chen ◽  
Botao Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Bone Marrow ◽  
Articular Cartilage ◽  
Bone Marrow Edema ◽  
Knee Joints ◽  
Infrapatellar Fat Pad ◽  
Proton Density ◽  
Knee Cartilage ◽  
Significant Difference ◽  
Marrow Edema

Background: Calcium-suppressed (CaSupp) technique involving spectral-based images has been used to observe bone marrow edema by removing calcium components from the image. Objective: This study aimed to evaluate the knee articular cartilage using the CaSupp technique in dual-layer detector computed tomography (DLCT). Methods: Twenty-eight healthy participants and two patients with osteoarthritis were enrolled, who underwent DLCT and magnetic resonance imaging (MRI) examination. CaSupp images were reconstructed from spectral-based images using a calcium suppression algorithm and were overlaid conventional CT images for visual evaluation. The morphology of the knee cartilage was evaluated, and the thickness of the articular cartilage was measured on sagittal proton density– weighted and CaSupp images in the patellofemoral compartment. Results: No abnormal signal or density, cartilage defect, and subjacent bone ulceration were observed in the lateral and medial femorotibial compartments and the patellofemoral compartment on MRI images and CaSupp images for the 48 normal knee joints. CaSupp images could clearly identify cartilage thinning, defect, subjacent bone marrow edema, and edema of the infrapatellar fat pad in the same way as MRI images in the three knee joints with osteoarthritis. A significant difference was found in the mean thickness of the patellar cartilage between MRI images and CaSupp images, while the femoral cartilage presented no significant difference in thickness between MRI images and CaSupp images over all 48 knee joints. Conclusion: The present study demonstrated that CaSupp images could effectively be used to perform the visual and quantitative assessment of knee cartilage.

scholarly journals Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2469
Author(s):  
Chen-Yi Xie ◽  
Chun-Lap Pang ◽  
Benjamin Chan ◽  
Emily Yuen-Yuen Wong ◽  
Qi Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Imaging ◽  
Risk Groups ◽  
Review Of Literature ◽  
Primary Tumors ◽  
Non Invasive ◽  
Significant Difference ◽  
Recent Developments ◽  
Health Significance ◽  
Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

Extrastriatal Dopamine D2/3 Receptor Availability in Alcohol Use Disorder and Individuals at High Risk

2022 ◽  
pp. 1-10
Author(s):  
Gianna Spitta ◽  
Tobias Gleich ◽  
Kristin Zacharias ◽  
Oisin Butler ◽  
Ralph Buchert ◽  
...  
Keyword(s):  
High Risk ◽  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Protective Factor ◽  
Region Of Interest ◽  
Anterior Cingulate ◽  
Dopamine D2 ◽  
Positron Emission ◽  
Significant Difference ◽  
Meta Analyses

<b><i>Introduction:</i></b> Reduced striatal dopamine D2/3 receptor availability in alcohol use disorder (AUD) has been demonstrated in recent clinical studies and meta-analyses. However, only a limited number of studies investigated extrastriatal D2/3 availability in AUD or in at-risk populations. In line with a dimensional understanding of addiction, extrastriatal dopaminergic neuroadaptations have been suggested to be relevant from a pathobiological perspective. <b><i>Methods:</i></b> We investigated D2/3 receptor availability via <sup>18</sup>F-fallypride positron emission tomography applying a region of interest (ROI) approach. We selected ROIs for the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC). Our sample included 19 healthy controls (low risk [LR]), 19 individuals at high risk (HR) to develop addiction, and 20 recently detoxified AUD patients. <b><i>Results:</i></b> We found significantly higher D2/3 receptor availability of HR compared to AUD in the left and right rostral ACC (rACC), as well as in the left ventrolateral PFC (vlPFC). We did not observe a significant difference between AUD and LR. After corrections for multiple comparisons none of the ROIs reached significance throughout the group comparison. The D2/3 receptor availability in the left rACC was inversely correlated with symptom severity assessed with the Alcohol Dependency Scale. <b><i>Discussion:</i></b> To our knowledge, the present work is the first study investigating extrastriatal D2/3 receptor availabilities in individuals at HR and patients with AUD. The observation that D2/3 receptor availabilities are highest in HR might suggest that their pathobiology differs from subjects with AUD. Future studies are necessary to clarify the intraindividual course of this biomarker over different disease stages and its possible role as a risk or protective factor.

scholarly journals Linearity and Bias of Proton Density Fat Fraction as a Quantitative Imaging Biomarker: A Multicenter, Multiplatform, Multivendor Phantom Study

Radiology ◽  
2021 ◽  
Vol 298 (3) ◽  
pp. 640-651
Author(s):  
Houchun H. Hu ◽  
Takeshi Yokoo ◽  
Mustafa R. Bashir ◽  
Claude B. Sirlin ◽  
Diego Hernando ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Phantom Study ◽  
Imaging Biomarker ◽  
Proton Density ◽  
Proton Density Fat Fraction ◽  
Quantitative Imaging Biomarker ◽  
Fat Fraction

scholarly journals Effect of Titanium Implants Coated with Radiation-Crosslinked Collagen on Stability and Osseointegration in Rat Tibia

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2520 ◽  
Author(s):  
Eun-Bin Bae ◽  
Ji-Hyun Yoo ◽  
Sung-In Jeong ◽  
Min-Su Kim ◽  
Youn-Mook Lim ◽  
...  
Keyword(s):  
Gamma Radiation ◽  
Human Mesenchymal Stem Cells ◽  
Region Of Interest ◽  
Bone Volume ◽  
Titanium Implants ◽  
Control Group ◽  
Gene Expressions ◽  
Significant Difference

This study aimed to evaluate the titanium (Ti) implants coated with collagen type Ⅰ crosslinked using gamma-irrigation or glutaraldehyde (GA). The in vitro surface observations, quantification assay, and cell studies using human mesenchymal stem cells (hMSCs) were conducted. For in vivo experiments, the implants were divided into three groups and inserted into the rat tibias: control group (non-treated Ti implant), GA group (Ti implants coated with GA-crosslinked collagen) and 25 kGy group (Ti implants coated with gamma-radiation-crosslinked collagen at dose of 25 kGy). The animals were sacrificed at 4 weeks after implantation and the tissue sections were obtained. New bone volume (mm3) and bone-to-implant contact (BIC, %) within the region of interest (ROI) was measured. The in vitro results showed the highest osteogenic differentiation and levels of osteogenesis-related gene expressions in the 25 kGy group without cytotoxicity. The new bone volume of GA group was significantly higher than the control (p < 0.05). In the result of the BIC, the 25 kGy group was significantly higher than the control (p < 0.05). However, there was no significant difference between the experimental groups. Within the limitations of this study, Ti implant coated with gamma-radiation-crosslinked collagen has potential utility without side effects from chemical agents.

The Influence of Alteration of kVp and mAs towards the Image Quality of Acrylic Using CBCT

2019 ◽  
Vol 829 ◽  
pp. 252-257
Author(s):  
Azhari ◽  
Yohanes Hutasoit ◽  
Freddy Haryanto
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Signal To Noise ◽  
Bite Block ◽  
Significant Difference ◽  
Noise Ratio ◽  
Post Hoc ◽  
Radiograph Image

CBCT is a modernized technology in producing radiograph image on dentistry. The image quality excellence is very important for clinicians to interpret the image, so the result of diagnosis produced becoming more accurate, appropriate, thus minimizing the working time. This research was aimed to assess the image quality using the blank acrylic phantom polymethylmethacrylate (PMMA) (C­5H8O2)n in the density of 1.185 g/cm3 for evaluating the homogeneity and uniformity of the image produced. Acrylic phantom was supported with a tripod and laid down on the chin rest of the CBCT device, then the phantom was fixed, and the edge of the phantom was touched by the bite block. Furthermore, the exposure of the X-ray was executed toward the acrylic phantom with various kVp and mAs, from 80 until 90, with the range of 5 kV and the variation of mA was 3, 5, and 7 mA respectively. The time exposure was kept constant for 25 seconds. The samples were taken from CBCT acrylic images, then as much as 5 ROIs (Region of Interest) was chosen to be analyzed. The ROIs determination was analyzed by using the ImageJ® software for recognizing the influence of kVp and mAs towards the image uniformity, noise and SNR. The lowest kVp and mAs had the result of uniformity value, homogeneity and signal to noise ratio of 11.22; 40.35; and 5.96 respectively. Meanwhile, the highest kVp and mAs had uniformity value, homogeneity and signal to noise ratio of 16.96; 26.20; and 5.95 respectively. There were significant differences between the image uniformity and homogeneity on the lowest kVp and mAs compared to the highest kVp and mAs, as analyzed with the ANOVA statistics analysis continued with the t-student post-hoc test with α = 0.05. However, there was no significant difference in SNR as analyzed with the ANOVA statistic analysis. The usage of the higher kVp and mAs caused the improvement of the image homogeneity and uniformity compared to the lower kVp and mAs.

scholarly journals Area Covered for Shear Wave Velocity Calculation Affects the Shear Wave Velocity Values

2019 ◽  
Vol 35 (3) ◽  
pp. 182-187
Author(s):  
Amitabh Dashottar ◽  
Erin Montambault ◽  
Jeffrey R. Betz ◽  
Kevin D. Evans
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Region Of Interest ◽  
Ultrasound Elastography ◽  
Maximum Area ◽  
Shear Wave Velocities ◽  
Significant Difference ◽  
Technical Specifications ◽  
The Mean

Although ultrasound elastography is established as a reliable and valid tool for assessment of skeletal muscles, guidelines around the technical specifications, data selection, and acquisition parameters still lack consensus. One such parameter is the use of the quantification box (Q-box) that calculates the shear wave velocity/modulus, within a selected region of interest (ROI). Currently, no data compare the effect of the elastographic area within the ROI to the mean shear wave velocity calculations, using a Q-box. In this study, the mean shear wave velocity calculated over a smaller (single Q-box) ROI is compared to the mean shear wave velocity calculated over maximum area of elastogram, within a ROI. Comparison of mean shear wave velocity revealed a significant difference ( t = 2.79, P = .007) between the means calculated over maximum area of elastogram for only nonuniform elastograms. The rater agreement for the classification scheme was assessed (κ = 0.85). To prevent possible overestimation of shear wave velocities, it may be necessary to place the Q-box over the maximum elastographic area.

Utility of T2 mapping in the staging of thyroid-associated ophthalmopathy: efficiency of region of interest selection methods

2020 ◽  
Vol 61 (11) ◽  
pp. 1512-1519 ◽  
Author(s):  
Wen Chen ◽  
Hao Hu ◽  
Huan-Huan Chen ◽  
Guo-Yi Su ◽  
Tao Yang ◽  
...  
Keyword(s):  
T2 Mapping ◽  
Region Of Interest ◽  
Roc Curves ◽  
Rank Test ◽  
Selection Methods ◽  
Measuring Time ◽  
Significant Difference ◽  
Thyroid Associated Ophthalmopathy ◽  
Signed Rank Test ◽  
The Difference

Background Discriminating the stage of thyroid-associated ophthalmopathy (TAO) is crucial for the treatment strategy and prognosis prediction. Utility of conventional magnetic resonance imaging in the disease staging is limited. Purpose To investigate the performance of T2 mapping based on different region of interest (ROI) selection methods in the staging of TAO. Material and Methods Thirty-two patients with TAO were retrospectively enrolled. Two radiologists independently measured the T2 relaxation time (T2RT) of extraocular muscles using two different ROIs (hotspot [ROIHS]: T2RT-hot; single-slice [ROISS]: T2RT-mean, T2RT-max, T2RT-min). Independent-samples t test, Wilcoxon signed rank test, Spearman correlation analysis, receiver operating characteristic (ROC) curves analyses, multiple ROC comparisons, and intra-class correlation coefficient (ICC) were used for statistical analyses. Results No significant difference was found in the measuring time between ROIHS and ROISS methods ( P = 0.066). T2RT-mean demonstrated the highest ICC for measurement, followed by T2RT-max and T2RT-min, and T2RT-hot showed the poorest reproducibility. Active TAOs showed significantly higher values for all the T2RTs than inactive mimics (all P < 0.001). Significant positive correlations were found between T2RTs and CAS (all P < 0.005). T2RT-hot and T2RT-max showed significantly higher areas under the curve than that of T2RT-mean ( P = 0.013 and 0.024, respectively), while the difference between T2RT-hot and T2RT-max was not significant ( P = 0.970). Conclusion The T2RTs derived from both ROI selection methods could be useful for the staging of TAO. The results of measuring time, reproducibility, and diagnostic performance suggest that T2RT-max would be the optimal indicator for staging.

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