Placing Regions Of Interest Research Articles

Application of bolus tracking: The effect of ROI positions on the images quality of cervicocerebral CT angiography

BackgroundCervicocerebral CT angiography (CTA) using the bolus tracking technique has been widely used for the assessment of cerebrovascular diseases. Regions of interest (ROI) can be placed in the descending aorta, ascending aorta, and the aortic arch. However, no study has compared the arteries and veins display when when the region of interest (ROI) is placed at different sites. In this study, we showed the impact of ROI positions on the image quality of cervicocerebral CTA. MethodsTwo hundred and seventy patients who underwent cervicocerebral CTA with bolus tracking technique were randomly divided into three groups based on the position of the ROI placement: ascending aorta (Group 1, n = 90), aortic arch (Group 2, n = 90), and descending aorta (Group 3, n = 90). The scanning parameters and contrast agent injection protocols were consistent across all groups. Three observers independently assessed the objective image quality, while two observers jointly assessed the subjective image quality using a grade scale: poor (grade 1), average (grade 2), good (grade 3), and excellent (grade 4). The differences in intravascular CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), AVCR (arterial venous contrast ratio), and subjective image quality scores were compared among the three groups. ResultsThe CT values of the intracranial veins (superior sagittal sinus, ethmoid sinus and great cerebral vein) in group 1 were significantly lower than those in group 3 (p < 0.001). However, no significant differences were observed in CT values, SNR and CNR in the internal carotid artery and middle cerebral artery among the three groups. The proportion of images with grade 4 was significantly higher in group 1 than group 2 and 3 (41.1% vs 15.6% and 13.3%, p < 0.001). The proportion of images with grade 1 was significantly lower in group 1 than group 2 and 3 (1.1% vs 6.6% and 17.8%, p < 0.001). ConclusionThe ROI positions for cervicocerebral CTA did not affect the arterial image quality, but venous structures imaging was affected when the ROI was placed in the ascending aorta.

Abstract 4172: Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling

Abstract Background: Tissue-based high-plex platforms evaluate numerous biomarkers in limited tissue sections. Certain tissue features and assay technical limitations may jeopardize the accuracy of this analysis. In this study, we aim to determine tissue specific features that influence image-based region of interest (ROI) selection and segmentation for spatial profiling of carcinomas, and to develop pathology approaches to overcome them. Methodology: A total of 317 samples from different carcinoma types processed with the GeoMx Digital spatial profiling protein assay protocol were assessed to identify deviations from original strategies for ROI placement and segmentation (Table 1). ROI were selected by a pathologist. The initial strategy for all cases was to place rectangles ROIs of 660*785 µm in tumor area and to segment based on morphology biomarkers as indicated in Table 1. Strategies to overcome technical and tissue specific features were recorded and quantified. Results: A total of 2068 ROIs and 4422 AOIs were evaluated. In all the cases, tissue displayed specific features that limited planned ROI placement and segmentation. Table 1 describes these features by tissue type and profiling strategies. A total of 970 ROIs were solved using different annotation type or changes in segmentation strategy, 584 bypassed the limitations by changing the processing protocol; and 322 were not solved due to non-compliance with assay requirements. Conclusions: We identified tissue features that limit the planned ROI selection and segmentation in spatial high-plex profiling, mainly due to tissue processing, distinct histology, type of sample, and presence of adjacent non-neoplastic tissue. A deep understanding of tumor architecture, biology and platform related technical limitations is needed to select areas of analysis for spatial profiling. Table 1. ROI and segmentation strategy methodology and tissue specific features with pathology appro Histological type (Profiling segments) Anal Carcinoma (Tu/TME) Lung NSCLC (Tu/TME) Lung NSCLC TMA (Tu/Tcells/Mϕ) Lung NSCLC TMA (Tu/TME) CRC MT Tu/Immune Breast Ca Tu/Immune/else Pathology Approach (ROIs) Methodology Samples 48 33 80 80 6 70 Biopsy type Incisional (Core/other), Whole tissue Incisional (Core/other)Whole tissue TMA TMA Whole tissue Core Morphology markers/mIF Channels Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 CD68: Cy5/666 Syto13: FITC/525PanCK: Cy3/568CD20: Texas red/615CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: CD45: Texas red/615 Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 Strategy for segmentation Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+)TME (PanCK-) Tumor (PanCK+) T cell (CD3+) Mϕ (CD68+) Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+) Immune (CD45+) Tumor (PanCK+) Immune (CD45+) Else (PanCK-CD45-) Total ROIs/AOIs 224/435 144/252 80/218 82/162 944/1573 594/1782 Tissue specific features (ROI number/percentage) Folds (ROIs) 10 1 0 0 9 5 Draw Polygons (25) Adjacent* Non-Tumoral tissue 22 7 9 7 388 40 Draw Polygons (473) Tissue Derived Autofluorescence 3 105 0 1 0 45 Draw Polygons (13). New segment to exclude autofluorescence (141) Sample Size 26 19 1 2 5 138 Polygons (191) Necrosis 13 0 1 1 217 6 Polygons (238) Biological absence of morphology marker 10 0 0 0 0 5 Polygons non segmented for Tu and TME (15) Solid tumors with scant TME 0 21 0 0 0 9 Polygons non segmented for Tu and TME (30) Eosin autofluorescence 0 0 0 0 0 584 Stained new section with Validated PanCK in Cy5/666 channel (584) Failure to reach the minimum number of cell requirements 13 36 22 2 249 43 Excluded segments (322) New scan to select more areas (43) Citation Format: Sharia Hernandez, Claudio J. Arrechedera, Pedro Rocha, Larisa Kostousov, Sean Barnes, Khan Khaja, Luisa M. Solis-Soto. Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4172.

Abstract 5505: Automating a spatial profiling workflow to explore the effects of hypoxia in the tumor microenvironment in head and neck cancer

Abstract Head and neck cancer (HNC) is a heterogeneous group of malignancies that arise from the mucosal surfaces of the upper aerodigestive tract. The tumor microenvironment (TME) of HNC is characterized by the presence of immune cells, stromal cells, and extracellular matrix components. A key feature of the TME is hypoxia, which promotes tumor growth, invasion, and metastasis by altering the expression of genes involved in angiogenesis, cell survival, and metabolism. Understanding the complex interplay between hypoxia and immune infiltrates in the TME of HNC is crucial for the development of novel therapeutic strategies for the treatment of this disease. Whole transcriptome analysis by digital spatial profiling is an excellent method of probing the TME, but assessing large cohorts can be time consuming. Automating a profiling workflow to reduce hands-on time and region of interest (ROI) selection bias will enable exploration of large cohorts to identify mechanisms of action, potential drug targets, and biomarkers. We developed an optimized spatial multi-omic workflow to enable high-throughput spatial analysis on GeoMx® Digital Spatial Profiler (DSP) using the Whole Transcriptome Atlas (WTA) and immuno-fluorescent morphology markers: SYTO82 (nuclei), CAIX (hypoxia), pan-cytokeratin (epithelium), CD3 (T-cells). A.I.-based analysis (Oncotopix® Discovery) of serial section H&amp;E images and GeoMx IF images was developed to identify ROIs for GeoMx collection. Immune hot and cold selection used leukocyte density; tumor/stromal interface selection used epithelial areas. Areas of illumination (AOI) were chosen using concentric CAIX expression gradients. Integrated analysis of digital images using Oncotopix Discovery and the whole transcriptome was done to assess the above TME compartments. Automated ROI placement based on tumor/stroma, hypoxia and immune infiltration and AI/Deep Learning based AOI segmentation reduced AOI selection time and improved accuracy of tissue compartment enrichment, especially between samples and tissue types. Automated development of hypoxia gradient-based AOI enabled a selection strategy not possible in the standard DSP software. Cell phenotyping using IF morphology scan was used to supervise cell deconvolution. DSP results correlated well with patient outcomes. This work shows that ROI-based spatial analyses can be used to explore the effects of hypoxia levels on immune infiltration in HNC. Automated AI-based ROI selection provides a means of sampling relevant tumor subtypes based on hypoxia and immune infiltrate criteria in an unbiased, reproducible manner, and can provide a standardized, automated method for selecting ROIs and segmenting AOIs across a cohort of mixed tissue types and pathological subtype, improving throughput. Citation Format: David Mason, Kyla Teplitz, James Robert Mansfield, Kelly Hunter, Joana Campos, Jill Brooks. Automating a spatial profiling workflow to explore the effects of hypoxia in the tumor microenvironment in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5505.

Inter- and intra-rater reproducibility of quantitative T1 measurement using semiautomatic region of interest placement in myometrium.

This study aimed to investigate the inter- and intraobserver reproducibility of quantitative T1 (qT1) measurements using manual and semiautomatic region of interest (ROI) placements. We hypothesized the usefulness of the semiautomatic method, which utilizes a three-dimensional (3D) anatomical relationship between the myometrium and other tissues, for minimizing ROI placement variation, thereby improving qT1 reproducibility compared to the manual approach. The semiautomatic approach, which considered anatomical relationships, was expected to enhance reproducibility by reducing ROI placement variabilities. This study recruited 23 healthy female volunteers. Data with variable flip angle (VFA) and inversion recovery were acquired using 3D-spoiled gradient echo and spin echo sequences, respectively. T1 maps were generated with VFA. Manual and semiautomatic ROI placements were independently conducted. Mean qT1 values were calculated from the T1 maps using the corresponding pixel values of the myometrial ROI. Inter- and intraobserver reproducibility of qT1 values was investigated. The inter- and intraobserver reproducibility of qT1 values was evaluated by calculating the coefficient of variation (CoV). Further, reproducibility was evaluated with inter- and intraobserver errors and intraclass correlation coefficients (ICCs). Bland-Altman analysis was utilized to compare the results, estimate bias, and determine the limits of agreement. The mean inter- and intraobserver CoV of the qT1 values for semiautomatic ROI placement was significantly lower than those for manual ROI placement (p < 0.05 and p < 0.01, respectively). ICCs for semiautomatic ROI placement were greater than those for manual ROI placement. Further, the mean inter- and intraobserver errors for semiautomatic ROI placement were significantly lower than those for manual ROI placement (p < 0.05 and p < 0.01, respectively). Semiautomatic ROI placement demonstrated high reproducibility of qT1 measurements compared with manual methods. Semiautomatic ROI placement may be useful for evaluating uterine qT1 with high reproducibility.

Variabilities in apparent diffusion coefficient (ADC) measurements of the spleen and the paraspinal muscle: A single center large cohort study

PurposeEvaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADCspleen) and the paraspinal muscles (ADCmuscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI). MethodsTwo MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm2). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADCspleen and the bilateral ADCmuscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed. ResultsThe ADCspleen was more scattered and variable than the ADCmuscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADCspleen (ICC, 0.47; CV, 0.070) and ADCmuscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were −0.1 (−25.7%–25.6%) and −0.3 (−8.8%–8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADCspleen (ICC, 0.72; CV, 0.061) and ADCmuscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (−20.0%–24.2%) and 0.7 (−10.0%–11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADCmuscle measurements were lower than those of the ADCspleen both in the repeatability and the comparability analyses (all p < 0.001). ConclusionParaspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.

Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study.

Mammography interpretation is challenging with high error rates. This study aims to reduce the errors in mammography reading by mapping diagnostic errors against global mammographic characteristics using a radiomics-based machine learning approach. A total of 36 radiologists from cohort A (n = 20) and cohort B (n = 16) read 60 high-density mammographic cases. Radiomic features were extracted from three regions of interest (ROIs), and random forest models were trained to predict diagnostic errors for each cohort. Performance was evaluated using sensitivity, specificity, accuracy, and AUC. The impact of ROI placement and normalization on prediction was investigated. Our approach successfully predicted both the false positive and false negative errors of both cohorts but did not consistently predict location errors. The errors produced by radiologists from cohort B were less predictable compared to those in cohort A. The performance of the models did not show significant improvement after feature normalization, despite the mammograms being produced by different vendors. Our novel radiomics-based machine learning pipeline focusing on global radiomic features could predict false positive and false negative errors. The proposed method can be used to develop group-tailored mammographic educational strategies to help improve future mammography reader performance.

Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms

Magnetic resonance imaging plays an important role in the evaluation of patients with known or suspected periampullary masses. The utilization of volumetric apparent diffusion coefficient (ADC) histogram evaluation for the entire lesion eradicates the potential for subjectivity in the region of interest placement, thus guaranteeing the accuracy of computation and repeatability. To investigate the value of volumetric ADC histogram analysis in the differentiation of intestinal-type (IPAC) and pancreatobiliary-type periampullary adenocarcinomas (PPAC). This retrospective study included 69 patients with histopathologically confirmed periampullary adenocarcinoma (54 PPAC and 15 IPAC). Diffusion-weighted imaging was obtained at b values of 1000mm²/s. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated independently by two radiologists. Using the interclass correlation coefficient, the interobserver agreement was evaluated. The ADC parameters for the PPAC group were all lower than those of the IPAC group. The PPAC group had higher variance, skewness, and kurtosis than the IPAC group. However, the difference between the kurtosis (P=.003), the 5th (P=.032), 10th (P=.043), and 25th (P=.037) percentiles of ADC values was statistically significant. The area under the curve (AUC) of the kurtosis was the highest (AUC=0.752; cut-off value=-0.235; sensitivity=61.1%; specificity=80.0%). Volumetric ADC histogram analysis with b values of 1000mm²/s can discriminate subtypes noninvasively before surgery.

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

ObjectivesMultiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT).MethodsWe retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient’s disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders.ResultsThe interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71–0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT.ConclusionThe mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC.Trial registrationRetrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

Automated region of interest placement on cervical ultrasound images for assessing preterm birth risk

Spontaneous preterm birth (sPTB) is one of the leading causes of infant morbidity. Medical interventions can prevent death caused by preterm birth if the risk is predicted at early stages. Quantitative ultrasound (QUS) is found valuable for predicting sPTB risk with a limitation of requiring a medically trained image analyst to manually draw a region of interest (ROI) on the cervix of a B-mode ultrasound image. An automated ROI placement algorithm was designed and trained to reduce the reliance on human annotations. The algorithm utilized a deep neural network with an optimized U-Net architecture to locate cervical tissues. A total of 8670 ultrasound images with sonographer-drawn ROIs were used for algorithm training and testing, followed by several postprocessing steps to yield the final ROIs. Quantitative comparison between algorithm-generated and sonographer-drawn ROIs yielded an average pixel accuracy of 96% and a dice coefficient of 88%. In addition, the QUS’s attenuation coefficient (AC) and backscatter coefficient (BSC) obtained from the algorithm-generated ROIs were highly correlated to those obtained from the sonographer-drawn ROIs with a Pearson correlation coefficient of 0.93 and 0.85, respectively. The results support the feasibility of automating QUS imaging for sPTB risk assessment. [Work supported by NIHR01HD089935.]

Impact of flexible noise control (FNC) image processing parameters on portable chest radiography.

There is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using a clinical chest program with 85kV and five radiation dose levels at 40″ source-to-image distance with software-based scatter reduction method. All images were processed without and with FNC. Noise analysis was performed in two regions of interest (ROI) on subtracted noise-only images, and line profiles were generated through a lung-rib interface. In addition, noise power spectra (NPS) analysis was performed in solid water phantoms of 10 and 20cm thicknesses, using the same acquisition program and a range of dose levels. Last, feedback on retrospectively deidentified, reprocessed, and randomized clinical images from 20 portable chest exams was gathered from two thoracic radiologists. Noise reduction performances of FNC were demonstrated, with the level depending on specific FNC parameters, dose levels, ROI placement, and phantom sizes. Higher frequency textural patterns were revealed through the NPS analysis, which varied based on FNC parameters, dose levels, and phantom sizes. Overall, the vendor default parameter FGA0.5 yielded the highest noise reduction and textural artifacts. Radiologist feedback showed consistent preference of no FNC due to the presence of textural artifacts in the FNC-processed images. An algorithm improvement to avoid introducing artifacts would be desired.

The Infrared Thermography Toolbox: An Open-access Semi-automated Segmentation Tool for Extracting Skin Temperatures in the Thoracic Region including Supraclavicular Brown Adipose Tissue

Infrared thermography (IRT) is widely used to assess skin temperature in response to physiological changes. Yet, it remains challenging to standardize skin temperature measurements over repeated datasets. We developed an open-access semi-automated segmentation tool (the IRT-toolbox) for measuring skin temperatures in the thoracic area to estimate supraclavicular brown adipose tissue (scBAT) activity, and compared it to manual segmentations. The IRT-toolbox, designed in Python, consisted of image pre-alignment and non-rigid image registration. The toolbox was tested using datasets of 10 individuals (BMI = 22.1 ± 2.1 kg/m2, age = 22.0 ± 3.7 years) who underwent two cooling procedures, yielding four images per individual. Regions of interest (ROIs) were delineated by two raters in the scBAT and deltoid areas on baseline images. The toolbox enabled direct transfer of baseline ROIs to the registered follow-up images. For comparison, both raters also manually drew ROIs in all follow-up images. Spatial ROI overlap between methods and raters was determined using the Dice coefficient. Mean bias and 95% limits of agreement in mean skin temperature between methods and raters were assessed using Bland–Altman analyses. ROI delineation time was four times faster with the IRT-toolbox (01:04 min) than with manual delineations (04:12 min). In both anatomical areas, there was a large variability in ROI placement between methods. Yet, relatively small skin temperature differences were found between methods (scBAT: 0.10 °C, 95%LoA[-0.13 to 0.33 °C] and deltoid: 0.05 °C, 95%LoA[-0.46 to 0.55 °C]). The variability in skin temperature between raters was comparable between methods. The IRT-toolbox enables faster ROI delineations, while maintaining inter-user reliability compared to manual delineations. (Trial registration number (ClinicalTrials.gov): NCT04406922, [May 29, 2020]).

Speed of sound index for liver steatosis estimation: a reliability study in normal subjects.

PURPOSE Non-alcoholic fatty liver disease (NAFLD) is the most widespread type of chronic liver disease in the Western countries. Ultrasound (US) is widely used for NAFLD staging. The Resona 7 US system (Mindray Bio-Medical Electronics Co., Ltd.) includes an image optimization and speed of ultrasound-related feature, Sound Speed Index (SSI). SSI is applied in a region of interest (ROI) that could potentially aid in tissue characterization. The purpose of this study is to evaluate the reliability of SSI on various examination parameters on normal subjects. METHODS Twenty normal subjects were examined by two radiologists performing SSI measurements in the liver in different ROI depths and sizes. Intraclass correlation coefficient (ICC) was calculated to measure intra- and inter-observer variability and inter-ROI variability. RESULTS For all ROIs and both radiologists, the mean inter-observer ICC was 0.62 and the mean intraobserver ICC was 0.52 and 0.79. The mean SSI values for all ROIs and examiners were in the range 1528.79-1540.16 m/s. CONCLUSION The results indicate that SSI can lead to reliable measurements on normal subjects, independent of ROI size but dependent on ROI placement. More studies processing NAFLD patients, utilizing reference methods of liver fat quantification either for reliability or correlation with SSI, should be performed to further investigate the relevance of the SSI as a potential biomarker in clinical practice for liver steatosis grading.

Influence of the area of the aqueduct and region of interest on quantification of stroke volume in healthy volunteers using phase-contrast cine magnetic resonance imaging.

Phase-contrast cine magnetic resonance imaging (PC-MRI) has been used to measure cerebrospinal fluid (CSF) flow dynamics, but the influence of the area of the aqueduct and region of interest (ROI) on quantification of stroke volume (SV) has not been assessed. To assess the influence of the area of the ROI in quantifying the aqueductal SV measured with PC-MRI within the cerebral aqueduct. Nine healthy volunteers (mean age = 29.6 years) were enrolled in the study, and brain MRI examinations were performed on a 3.0-T system. Quantitative analysis of the aqueductal CSF flow was performed using manual ROI placement. ROIs were separately drawn for each of the 12 phases of the cardiac cycle, and changes in aqueduct size during the cardiac cycle were determined. The SV was calculated using 12 different aqueductal ROIs and compared with the SV calculated using a fixed ROI size. There was variation in the size of the aqueduct during the cardiac cycle. In addition, the measured SV increased with a greater area of the ROI. A significant difference in the calculated SVs with the 12 variable ROIs was observed compared with that using a fixed ROI throughout the cardiac cycle. To establish reliable reference values for the SV in future studies, a variable ROI should be considered.

Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements.

Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making. To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements. Retrospective. In all, 109 patients with GBM (n=57) or SBM (n=52) were enrolled. A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI. ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively. Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test. For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC=0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs. The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance. 4 TECHNICAL EFFICACY: Stage 2.

MRI-based quantification of renal fat in obese individuals using different image analysis approaches.

The purpose of this study was to evaluate different renal proton density fat fraction (PDFF) analysis approaches. Additionally, we assessed renal fat in obese individuals and lean individuals. This was a retrospective observational case-control study. Twenty-eight obese individuals and 14 lean controls underwent MRI with multi-point Dixon technique for PDFF maps. The following renal PDFF image analysis approaches were performed and compared: (1) five circular regions of interest (ROIs) in six slices, (2) three circular ROIs in one slice, (3) freehand segmentation of renal parenchyma in one slice, and (4) freehand segmentation of renal parenchyma avoiding the renal border in one slice. Furthermore, renal PDFF was compared between obese and lean individuals. Methods 1, 2, and 4 were positively correlated (r ≥ 0.498, p ≤ 0.001). Renal PDFF values varied more with regards to ROI placement within slices than mean PDFF between slices. Using all methods, the obese individuals had significantly higher renal PDFF values compared with the lean controls. Renal PDFF should be measured covering large areas of the kidney while excluding artifacts. This can be achieved using multiple circular ROIs. Increased lipid accumulation in the kidneys was related to obesity.

Optimizing Region of Interest Size and Placement for Opportunistic CT

Opportunistic CT is increasingly used clinically, however, no guidance exists regarding optimal region of interest (ROI) size or placement to obtain Hounsfield Unit (HU) data. As ROI size and placement alter HU, standardization is desirable. This studies purposes were to: 1.) Evaluate various L1 and L4 ROI sizes and locations to obtain reproducible HU values and; 2.) Compare HU values from axial and sagittal images. On lumbar spine CT, 2 physicians independently placed varying size circular ROIs (100, 200,300 mm2 and maximum) at L1 and L4 on axial and sagittal images. Subsequent analyses placed a 200 mm2 ROI at L1 and L4 in anterior, mid and posterior aspects and also cranial, central and caudal and left/right locations. Intra and inter-observer reliability was determined using intraclass correlation coefficients. ROI size comparison was performed by ANOVA and between axial and sagittal by t-test. Limits of agreement was assessed by Bland-Altman. The study included 30 spine clinic patients; 21F/9M mean (SD) age 59.2 (14.4) yrs. Vertebral body centroid identification, i.e., CT slice location used for HU measurement, was nearly identical between readers with ICC >0.99 for both projections. Intra- and inter-observer reliability was excellent for all sizes on both projections with ICC >0.95; Table 1. HU did not differ between the 4 ROI sizes on either projection for L1 and L4; all p >0.95; Table 1. Correlations between ROI sizes were excellent; r >0.95. Axial HU was higher than sagittal; p < 0.001 for all ROI sizes at L1 and L4; Table 1. Anterior, mid or posterior placement did not affect axial or sagittal HU; p >0.5 at L1 or L4; Table 2. However, moving away from the vertebral body centroid, either cranial/caudal or left/right, altered HU; Table 3. Limits of agreement between cranial/caudal and left/right location were large (from 10-90 HU). Non-radiologists can reliably identify the vertebral body centroid to facilitate ROI placement on axial and sagittal CT images and measure HU. While ROI size did not affect HU, 200 mm2 is recommended as larger ROI sizes may extend to the vertebral cortex in small patients which changes HU. Axial results were ∼6-10 HU higher than sagittal. Anterior, mid or posterior ROI location did not affect HU but image plane (left/right or cranial/caudal) alters HU likely reflecting anisotropic vertebral microarchitecture. For clinical use, we recommend default ROI placement be a 200 mm2 circular ROI placed at the vertebral body centroid.

Soft Tissue Standardization Improves Humerus Tissue Thickness Precision and Lowers BMD in Total Shoulder Arthroplasty Patients

DXA technology requires soft tissue attenuation measurement. This sampling is confined within a standard soft tissue region of interest (ROI) defined by the blue squares on Figure 1, which is automatically placed and used to compare with radiation absorption over bone. Variable placement of the soft tissue ROI likely alters BMD measurement and reproducibility. The aim of this study was to measure humeral BMD in total shoulder arthroplasty (TSA) patients. As no shoulder feature is available in GE Lunar enCORE v18, hip atypical femur fracture (AFF) software was utilized. In preliminary evaluation, we observed that soft tissue ROI placement and amount of tissue sampled were inconsistent (Figure 2). We hypothesized that use of a consistent soft tissue ROI would change BMD and improve precision. The purpose of this analysis was to compare humerus BMD and precision prior to and following soft tissue ROI standardization. After manufacturer consultation, all scans were reanalyzed using a soft tissue ROI of a fixed size (100 pixels tall, 50 pixels wide) manually placed on every scan distal and lateral to the humeral head (Figure 3). This study of TSA patients, 1-5 years post-surgery, included full humerus scans obtained on a GE Lunar iDXA with v17 or v18 software and analyzed with v18. Each participant had bilateral shoulder scans performed twice to assess precision and humerus BMD was measured at custom ROIs. BMD and tissue thickness obtained with automated and manual approaches were compared using T-test. Precision was determined using the ISCD calculator and compared between methods with F-test. Thirty subjects were included (20M/10F) with mean age and years’ post-surgery of 70.6 and 2.7 respectively. Average tissue thickness was reduced 13.5% (p < 0.01) with manual tissue sampling. Mean BMD among the nine custom ROIs was 2.2-8.5% lower (p < 0.01) with manual tissue sampling (Table 1). Additionally, tissue thickness %CV improved (p < 0.01) from 47.6 to 14.8% and 25.9 to 9.6%. Similarly, BMD precision improved (p < 0.01) at the 1/3 humerus and distal implant ROIs on the TSA side (Table 2). When measuring skeletal sites without optimized software available, it is necessary to critically evaluate software performance. In this study, soft tissue ROI standardization markedly improved tissue thickness sampling and reduced BMD at custom humerus ROIs. Optimization and automation of humerus DXA software would likely further improve BMD precision and measurement.

Infarct quantification with cardiovascular magnetic resonance using "standard deviation from remote" is unreliable: validation in multi-centre multi-vendor data

BackgroundThe objective of the study was to investigate variability and agreement of the commonly used image processing method “n-SD from remote” and in particular for quantifying myocardial infarction by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR). LGE-CMR in tandem with the analysis method “n-SD from remote” represents the current reference standard for infarct quantification. This analytic method utilizes regions of interest (ROIs) and defines infarct as the tissue with a set number of standard deviations (SD) above the signal intensity of remote nulled myocardium. There is no consensus on what the set number of SD is supposed to be. Little is known about how size and location of ROIs and underlying signal properties in the LGE images affect results. Furthermore, the method is frequently used elsewhere in medical imaging often without careful validation. Therefore, the usage of the “n-SD” method warrants a thorough validation.MethodsData from 214 patients from two multi-center cardioprotection trials were included. Infarct size from different remote ROI positions, ROI size, and number of standard deviations (“n-SD”) were compared with reference core lab delineations.ResultsVariability in infarct size caused by varying ROI position, ROI size, and “n-SD” was 47%, 48%, and 40%, respectively. The agreement between the “n-SD from remote” method and the reference infarct size by core lab delineations was low. Optimal “n-SD” threshold computed on a slice-by-slice basis showed high variability, n = 5.3 ± 2.2.ConclusionThe “n-SD from remote” method is unreliable for infarct quantification due to high variability which depends on different placement and size of remote ROI, number “n-SD”, and image signal properties related to the CMR-scanner and sequence used. Therefore, the “n-SD from remote” method should not be used, instead methods validated against an independent standard are recommended.

Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements

PurposeTo assess the value of histogram analysis, using diffusion kurtosis imaging (DKI), in differentiating glioblastoma multiforme (GBM) from single brain metastasis (SBM) and to compare the diagnostic efficiency of different region of interest (ROI) placements. MethodSixty-seven patients with histologically confirmed GBM (n = 35) and SBM (n = 32) were recruited. Two ROIs—the contrast-enhanced area and whole-tumor area—were delineated across all slices. Eleven histogram parameters of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) from both ROIs were calculated. All histogram parameter values were compared between GBM and SBM, using the Mann–Whitney U test. The accuracies of different histogram parameters were compared using the McNemar test. Receiver operating characteristic (ROC) analyses were conducted to assess the diagnostic performance. ResultsIn the contrast-enhanced area, FA10, FA25, FA75, FA90, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAskewness was significantly lower for GBM than for SBM. FA25 (0.815) had the highest area under the curve (AUC). In the whole-tumor area, FA10, FA25, FA75, FA90, FASD, FAmean, FAmedian, FAmax, MDmax, MDskewness, and MKskewness were significantly higher for GBM than for SBM. FAmedian (0.805) had the highest AUC. The accuracy of FA25 in the contrast-enhanced area was significantly higher than that of the FAmedian in the whole-tumor area. ConclusionsGBM and SBM can be differentiated using the DKI-based histogram analysis. Placing the ROI on the contrast-enhanced area results in better discrimination.

A Pre-Release Algorithm With a Confidence Map for Estimating the Attenuation Coefficient for Liver Fat Quantification.

To compare the estimates of attenuation coefficient (AC) for liver fat quantification between 2 Ultrasound systems and to evaluate the quality measure of a pre-released software. AC were obtained in 30 participants in this single-center IRB-approved, HIPAA compliant study. Images were obtained on the Philips Epiq Elite system using experimental software and the Canon Medical Systems Aplio i800 with released software. Five AC measurements were taken and the median and IQR/M were calculated. Region of interest placement was based on a confidence map. ROI was at the same depth and size for each system. The concordance was estimated using the Lin's concordance correlation coefficient (CCC), the r Pearson's correlation coefficient, the bias-correction factor (Cb), and the Bland-Altman method. The ACs varied from 0.45 to 1.0 dB/cm/MHz for the Philips system and 0.30 to 0.96 dB/cm/MHz for the Canon system. The CCC (95% CI) was 0.792 (0.666-0.918), Pearson's r was 0.839 with Cb of 0.944, and the mean difference was 0.03 (-0.101; 0.162) suggesting the 2 methods are considered to be in agreement. Based on a Philips confidence map to determine the best location for performing the measurements, a depth of 3.5 to 4.0 cm from the liver capsule was determined, which might be significantly different than that of the Canon system. Estimation of the AC of the 2 systems showed a high agreement, that is, a similar trend. Assessment of the placement of the measurement box based on the quality of the measurement might be different between the 2 systems.