Siemens Scanner Research Articles

Diagnostic Reference Range (DRR) in Computed Tomography (CT) Imaging Based on Clinical Indications Related to Thorax, Abdomen and Pelvic Regions

The concept of diagnostic reference range (DRR) is introduced to address the balance between a patient’s risk (radiation dose) and benefit (image quality necessary for an accurate diagnosis). R provides a minimum estimated dose that covers 25–75% range of dose distribution. The International Commission on Radiological Protection, ICRP (2017) recommended the 75th percentile as the upper d 25th percentile as the lower limit of DRR [1]. Dose below the lower range should compromise accurate interpretation of the image and dose above the upper range may be in excess and need to be reviewed. Meanwhile, Diagnostic Reference Levels (DRLs) are set at the 75th percentile of dose distribution and recommended by ICRP as a guidance to identify any unusual computed tomography (CT) procedures that consistently exceed the established DRLs.
 The aim of this study was to establish the local DRR for CT scanning based on clinical indications associated to thorax, abdomen, and pelvic regions in adult patients at Department of Radiology, Hospital Universiti Sains Malaysia (HUSM), Kelantan. This study involved a retrospective survey on data of adult patients underwent CT scanning based on 5 most common clinical indications related to thorax, abdomen, and pelvic regions at two departments, Radiology and Trauma Department, HUSM. This study obtained human ethical approval from the university ethical committee (Protocol code: USM/JEPeM/22010025). The CT dose metrics (dose length product, DLP and total DLP) and associated technical factors were collected.
 Results showed that the five most common clinical indications were cancer staging, coronary artery disease, pulmonary embolism, stones (CTU protocol) and stones (KUB protocol). The DLP values were varied based on their clinical indications and CT scanner types. Figure 1 shows boxplots representing the DRRs of DLP based on five most common clinical indications associated to CT scans of thorax, abdomen, and pelvic regions for Toshiba and Siemens CT scanners. The highest total DLP values was 4722 mGy.cm observed from CT scanning of cancer staging using Siemens scanner. While the lowest DLP value is 40 mGy.cm for stones scanning using the KUB protocol also using Siemens scanner. The main factor that contributes to higher DLP is the longer scanning length.
 This study demonstrates that the dose descriptor values, DLP and total DLP were varied for similar anatomical region based on different image quality requirement for each clinical indication. Thus, utilisation of both DRR and DRL are crucial to minimise the unnecessary dose to patient and ensure the optimum image quality is produced by each CT scanning.

2.49 Transfer Learning of Scanner-Generalization Neural Networks for Predicting General Psychopathology Factor (p factor) in Adolescents Based on Resting-State Functional Connectivity

2.49 Transfer Learning of Scanner-Generalization Neural Networks for Predicting General Psychopathology Factor (p factor) in Adolescents Based on Resting-State Functional Connectivity

Early life metal exposure is associated with reduced fractional anisotropy in the corpus callosum in children

Background: The corpus callosum (CC) is the largest white matter structure in the brain, connecting the left and right hemispheres into functional networks subserving cognition and behavior. Neuroimaging studies demonstrate rapid CC development during the first years of life. During these early-life critical windows, the CC is vulnerable to perturbation by neurotoxicants including heavy metals. Most environmental studies consider single neurotoxicant exposures at a single time point, potentially missing the effects of joint exposure across a developmental stage. Here, we investigated associations between early life metal mixture exposure and CC white matter microstructure integrity in children. Methods: In a preliminary analysis of 47 children (8-13 years; 22 females) enrolled in a neuroimaging substudy within the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study, we estimated weekly exposure (22nd-week gestation through 43rd week postnatal) to manganese (Mn), zinc (Zn), and lead (Pb) using laser ablation-inductively coupled plasma-mass spectrometry of teeth. We estimated CC white matter microstructure integrity using fractional anisotropy (FA) from diffusion tensor imaging (DTI) acquired in a 3T Siemens scanner. We used lagged weighted quantile sum (lWQS) regression to estimate the time-varying mixture effect on FA in the CC. Results: A higher metal mixture index in the 24th-43rd postnatal weeks was associated with decreased FA in the CC genu, body, and splenium (maximum β = -0.65 [95% CI -0.25, -1.07]), driven mainly by Zn and Pb. Conclusion: The CC may demonstrate a postnatal critical window to metals, with higher exposure to Zn and Pb associated with reduced FA. These results may help understand the role of exposure timing in driving neurodevelopmental outcomes, pointing to future optimal and timely public health interventions.

Quantitative MRI of chronic pancreatitis: results from a multi-institutional prospective study, magnetic resonance imaging as a non-invasive method for assessment of pancreatic fibrosis (MINIMAP).

PurposeTo determine if quantitative MRI techniques can be helpful to evaluate chronic pancreatitis (CP) in a setting of multi-institutional study.MethodsThis study included a subgroup of participants (n = 101) enrolled in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED) study (NCT03099850) from February 2019 to May 2021. MRI was performed on 1.5 T using Siemens and GE scanners at seven clinical centers across the USA. Quantitative MRI parameters of the pancreas included T1 relaxation time, extracellular volume (ECV) fraction, apparent diffusion coefficient (ADC), and fat signal fraction. We report the diagnostic performance and mean values within the control (n = 50) and CP (n = 51) groups. The T1, ECV and fat signal fraction were combined to generate the quantitative MRI score (Q-MRI).ResultsThere was significantly higher T1 relaxation time; mean 669 ms (± 171) vs. 593 ms (± 82) (p = 0.006), ECV fraction; 40.2% (± 14.7) vs. 30.3% (± 11.9) (p < 0.001), and pancreatic fat signal fraction; 12.2% (± 5.5) vs. 8.2% (± 4.4) (p < 0.001) in the CP group compared to controls. The ADC was similar between groups (p = 0.45). The AUCs for the T1, ECV, and pancreatic fat signal fraction were 0.62, 0.72, and 0.73, respectively. The composite Q-MRI score improved the diagnostic performance (cross-validated AUC: 0.76).ConclusionQuantitative MR parameters evaluating the pancreatic parenchyma (T1, ECV fraction, and fat signal fraction) are helpful in the diagnosis of CP. A Q-MRI score that combines these three MR parameters improves diagnostic performance. Further studies are warranted with larger study populations including patients with acute and recurrent acute pancreatitis and longitudinal follow-ups.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00261-022-03654-7.

Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising.

High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T2 ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T2 relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T2 -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T2 values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T2 maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T2 maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.

Evaluation of renal vasculature and its variants by CT angiography

Background: Kidneys are a pair of retroperitoneal organs supplied by a single renal artery and vein. However, the classic illustration of the renal vasculature, formed by one renal artery and one renal vein, occurs in ≤25% of cases. MDCT angiography is presently the most preferred investigation for assessing prospective renal donors. Aims and Objectives: This study aims to familiarize urologists with the variations found in the renal vascular system, emphasizing prevalence, the adequate, appropriate terms, and the clinical and surgical implications involved. Materials and Methods: A cross-sectional study in 50 patients clinically indicated to undergo CT Renal angiographic study over 2 years is included in the study. This study was carried out to look for any anatomical variants in renal vasculature. CT examination was performed on a multidetector 16 slice CT Siemens scanner in the department of radiodiagnosis. Once conventional CT is done, the data obtained can produce 3D post-processing images that simulate conventional angiograms. Results: Out of 50 patients, single renal arteries are present in 27, and multiple renal arteries are present in 23 patients. Out of 27 patients with single renal arteries, 22.2 % has perihilar arterial branching. The most common variant was accessory renal arteries, which are found in 56% of individuals, followed by aberrant renal arteries found in 30% of individuals. Double renal arteries are found in 8.7% and triple renal arteries in 4.3% of cases. Out of 50 patients, 48 patients have a single renal vein, whereas two patients have supernumerary veins. The present study found right renal vein duplication in only 2% of patients. Conclusion: Renal arteries and veins show numerous variations in their origin sites, numbers, course, and division patterns. These variants exist in the population with high prevalence and are thus crucial in preoperative and intra-operative analysis for better prognosis and reduced complications. Preoperative CT evaluation of renal vasculature using MDCT angiography helps depict the presence or absence of renal arterial and venous variants.

The impacts of vertical off-centring, tube voltage, and phantom size on computed tomography numbers: An experimental study

IntroductionThis experimental study explored the effect of vertical off-centring on computed tomography (CT) numbers in combination with various tube voltages and phantom sizes for two CT units. MethodsCIRS Model 062 Electron Density and system performance phantoms were imaged on Siemens Emotion 16-slice CT and GEMINI-GXL scanners, respectively. Uniformity and accuracy were evaluated as a function of vertical off-centring (20, 40, 60, and 80 mm above the gantry isocentre) using different water phantom sizes (18, 20, and 30 cm) and tube voltages (80, 90, 110, 120, 130 and 140 kVp). ResultsVertical off-centring and phantom size accounted for 92% of the recorded variance and the resultant change in CT numbers. The uniformity test recorded maximum changes of 14 and 27.2 HU for peripheral ROIs across the X- and Y-axes for an 80 mm phantom shift above the gantry isocentre on the GEMINI GXL and Siemens scanners, respectively. The absolute CT number differences between the superior and inferior ROIs were 13.7 HU for the 30 cm phantom and 4.8 HU for the 20 cm phantom for 80 mm vertical off-centring. The largest differences were observed at lower tube voltages. ConclusionsIt is essential to highlight the significance of CT number variation in clinical decision-making. Phantom off-centring affected the uniformity of these numbers, which were further impacted by the ROI position in this experimental study. CT number variation was more evident in peripheral phantom areas, lower tube voltages and larger phantom sizes. Implications for practiceCT number is observed to be a variable under certain common conditions. This significantly impacts several applications where clinical decisions depend on CT number accuracy for tissue lesion characterisation.

Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals.

Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.

56: EXTERNAL VALIDATION OF PET-BASED RADIOMIC MODELS TO IDENTIFY PATIENTS WITH RESIDUAL ESOPHAGEAL CANCER AFTER NEOADJUVANT CHEMORADIOTHERAPY

Abstract Background and aim High-throughput quantitative imaging (‘radiomics’) has been proposed to predict tumor response in various types of cancer. Internally validated radiomic models based on post-treatment PET features plus cT-stage have been developed (Beukinga et al. 2018) to detect residual tumor after neoadjuvant chemoradiotherapy for esophageal cancer. The aim of the present study was to externally validate the published models. Methods The external validation cohort comprised esophageal cancer patients who underwent chemoradiotherapy according to the CROSS regimen followed by immediate resection in four Dutch institutes between 2013–2019. Outcome was tumor regression grade (TRG) 1 (0% residual vital tumor) versus TRG-2-3-4 (≥ 1% tumor). Preoperative FDG-PET/CT was performed 6–12 weeks after nCRT. Gross tumor volumes on CT were transposed to post-treatment PET scans and were manually adapted in consensus by two investigators. Radiomic features were extracted using the open-source software ‘Pyradiomics’, with settings similar to the published models. Discrimination and calibration were assessed for the 6 internally validated models with optimism-corrected AUCs&amp;gt;0.77. Results Some 189 patients were included in the external validation cohort. Baseline characteristics and outcome (TRG-1: 40/189 patients (21%); TRG2–3-4: 149/189 patients (79%)) were comparable to the derivation sample. Since features were dependent on different scanner manufacturers (data not shown), the cohort was reduced with only scans from Siemens scanners (n = 130), similar to the derivation sample. The model including cT-stage and the grey-level-co-occurrence-matrix feature ‘Joint Energy’ had highest AUC of 0.71 (95% CI 0.60–0.81), and a calibration slope and intercept of −0.19 and 2.83 respectively. At a probability threshold of 0.5, sensitivity was 81%, specificity 31% and balanced accuracy 56%. Conclusion The predictive performance of the previously developed radiomic models could not be replicated in the present multicenter external validation cohort. The decreased overall performance indicates overfitting of the models to the derivation sample. Possible causes are unintentional dependency of radiomic features on scanner types, scanning protocols, tumor delineation methods and timing of post-treatment FDG-PET/CT. We aim at improving the generalizability of the models, to enable future application in clinical decision-making for individual patients.

Impact of Infant Thoracic Non-cardiac Perioperative Critical Care on Homotopic-Like Corpus Callosum and Forebrain Sub-regional Volumes.

Previously, we reported quantitatively smaller total corpus callosum (CC) and total forebrain size in critically ill term-born and premature patients following complex perioperative critical care for long-gap esophageal atresia (LGEA) that included Foker process repair. We extended our cross-sectional pilot study to determine sub-regional volumes of CC and forebrain using structural brain MRI. Our objective was to evaluate region-specific CC as an in-vivo marker for decreased myelination and/or cortical neural loss of homotopic-like sub-regions of the forebrain. Term-born (n = 13) and premature (n = 13) patients, and healthy naïve controls (n = 21) <1-year corrected age underwent non-sedated MRI using a 3T Siemens scanner, as per IRB approval at Boston Children's Hospital following completion of clinical treatment for Foker process. We used ITK-SNAP (v.3.6) to manually segment six sub-regions of CC and eight sub-regions of forebrain as per previously reported methodology. Group differences were assessed using a general linear model univariate analysis with corrected age at scan as a covariate. Our analysis implicates globally smaller CC and forebrain with sub-region II (viz. rostral body of CC known to connect to pre-motor cortex) to be least affected in comparison to other CC sub-regions in LGEA patients. Our report of smaller subgenual forebrain implicates (mal)adaptation in limbic circuits development in selected group of infant patients following LGEA repair. Future studies should include diffusion tractography studies of CC in further evaluation of what appears to represent global decrease in homotopic-like CC/forebrain size following complex perioperative critical care of infants born with LGEA.

Impact of pixel value truncation on image quality of low dose chest CT.

In some noisy low dose CT lung cancer screening images, we noticed that the CT density values of air were increased and the visibility of emphysema was distinctly decreased. By examining histograms of these images, we found that the CT density values were truncated at -1024 HU. The purpose of this study was to investigate the effect of pixel value truncation on the visibility of emphysema using mathematical models. Assuming CT noise follows a normal distribution, we derived the relationship between the mean CT density value and the standard deviation (SD) when the pixel values below -1024 HU are truncated and replaced by -1024 HU. To validate our mathematical model, 20 untruncated phantom CT images were truncated by simulation, and the mean CT density values and SD of air in the images were measured and compared with the theoretical values. In addition, the mean CT density values and SD of air were measured in 100 cases of real clinical images obtained by GE, Siemens, and Philips scanners, respectively, and the agreement with the theoretical values was examined. Next, the contrast-to-noise ratio (CNR) between air (-1000 HU) and lung parenchyma (-850 HU) was derived from the mathematical model in the presence and absence of truncation as a measure of the visibility of emphysema. In addition, the radiation dose ratios required to obtain the same CNR in the case with and without truncation were also calculated. The mathematical model revealed that when the pixel values are truncated, the mean CT density values are proportional to the noise magnitude when the magnitude exceeds a certain level. The mean CT density values and SD measured in the images with pixel values truncated by simulation and in the real clinical images acquired by GE and Philips scanners agreed well with the theoretical values from our mathematical model. In the Siemens images, the measured and theoretical values agreed well when a portion of the truncated values were replaced by random values instead of simply replacing by -1024 HU. The CNR of air and lung parenchyma was lowered by truncating CT density values compared to that of no truncation. Furthermore, it was found that higher radiation dose was required to obtain the same CNR with truncation as without. As an example, when the noise SD was 60 HU, the radiation dose required for the GE and Philips truncation method was about 1.2 times higher than that without truncation, and that for the Siemens truncation method was about 1.4 times higher. It was demonstrated mathematically that pixel value truncation causes a brightening of the mean CT density value and decreases the CNR of emphysema. Our results indicate that it is advisable to turn off truncation at -1024 HU, especially when scanning at low and ultra-low radiation doses in the thorax.

Comparison of Strain-Encoding and Feature-Tracking Derived Myocardial Deformation Assessment of Left Ventricular Function in a Pediatric and Adult Congenital Heart Disease Cohort.

Cardiac magnetic resonance (CMR) strain can be assessed with feature-tracking (FT), which utilizes a post-processing algorithm to quantify myocardial deformation on routine cine images, and strain-encoding magnetic resonance imaging (SENC), which uses parallel magnetization tags combined with out-of-plane phase-encoding gradients to quantify deformation. Assessing agreement is critical to determine whether results can be translated between methods. We compared SENC to FT in the assessment of left ventricle (LV) global longitudinal strain (GLS) and global circumferential strain (GCS) in a cohort of pediatric and adult congenital heart disease (ACHD) patients. Pediatric subjects and ACHD patients underwent CMR on 1.5T Siemens scanners, including balanced steady-state-free precession (bSSFP) cine imaging and SENC acquisitions in apical two and four chamber, left ventricular outflow tract, and short axis views. bSSFP cine imaging FT analysis was completed with Medis QStrain. Myocardial Solutions MyoStrain was used to analyze SENC. Correlation was assessed by Spearman's rank correlation coefficient. Agreement between techniques was assessed with concordance correlation coefficient (CCC) and Bland-Altman. The cohort included 134 patients, 75 with congenital heart disease (56%). The median age was 16.3years (IQR 13.7, 19.5). Median LV ejection fraction was 57% (IQR 54.4, 61.6). SENC and FT were in poor agreement for GLS (Spearman's ρ = 0.58, p < 0.001; CCC 0.24) and GCS (Spearman's ρ = 0.29, p < 0.001; CCC 0.03). There was poor agreement between SENC and FT derived GLS and GCS in a cohort of pediatric and ACHD patients, suggesting that SENC and FT cannot be used interchangeably.

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network.

Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners. The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV). The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively. In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%). The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results across multiple scanners, thus representing a suitable alternative quantitative method of comparative brain tissue analysis for individual patients.

Abstract WP97: Automated Analysis For Detecting Large Vessel Occlusion On Non Contrast Ct In Acute Stroke

Background and Purpose: Rapid detection of large vessel occlusion (LVO) is essential for rapid triage for thrombectomy to salvage at-risk tissue. Early detection of LVO on noncontrast CT head (NCCT) could facilitate triage before advanced imaging is obtained. RAPID hyperdense vessel sign (HVS), a new fully automated software- tool for HVS detection is compared to experienced radiologists, for early LVO detection on NCCT, and validated with CTA. Methods: Scans were selected from a database of 335 scans, of which 166 were selected by stratified random selection. Images were evaluated by the Rapid automated software, and six readers, two radiologists, and four neuroradiologists. The Rapid algorithm returns a score between zero and one, and a threshold of 0.5 was applied of HVS. Radiologists read each scan twice, 30 days apart. A scan rating confidence scale was used, from “very confident of absence,” to “very confident of HVS,” and a separate “technically inadequate” rating, and this was compared to the Rapid HVS score. “Very Confident,” or “Confident” rated scans were considered HVS positive. Results: Demographics: 54% were men. Ages ranged 23 - 94 with a median of 65. GE, Philips, Siemens, and Toshiba scanners are represented. Agreement with RAPID for HVS detection was 75% to 84%. Below a HVS score of 0.4 the proportion of LVO positive scans was 46% (indeterminate), between 0.4 and 0.6 the proportion was 62.5% (probable), and above 0.6 the proportion was 90% (likely). The scan rating confidence grade correlated with the Rapid HVS score. The software identified 53% of the LVOs, comparable to human readers (26-57%) A clinical practice simulation of RAPID followed by a reader identified 61.3% (p=0.02 vs readers alone). Among the 61 patients with no LVO on CTA, readers had a false positive rate of less than 7% as compared to 15% for the software. Median software processing time was 54 seconds. Conclusions: Expert readers, and the Rapid software, detect an HVS in about 50% of CT head images with LVO on CTA. When Rapid software is used in tandem with an experienced reader, HVS can be detected in up to two thirds of patients with LVO on CTA. This high percentage of early LVO detection would facilitate faster triage of patients for more advanced imaging and/or therapeutic interventions.

Conversion from dose-length product to effective dose in computed tomography venography of the lower extremities

For radiation dose assessment of computed tomography (CT), effective dose (ED) is often estimated by multiplying the dose-length product (DLP), provided automatically by the CT scanner, by a conversion factor. We investigated such conversion in CT venography of the lower extremities performed in conjunction with CT pulmonary angiography. The study subjects consisted of eight groups imaged using different scanners and different imaging conditions (five and three groups for the GE and Siemens scanners, respectively). Each group included ten men and ten women. The scan range was divided into four anatomical regions (trunk, proximal thigh, knee and distal leg), and DLP was calculated for each region (regional DLP). Regional DLP was multiplied by a conversion factor for the respective region, to convert it to ED. The sum of the ED values for the four regions was obtained as standard ED. Additionally, the sum of the four regional DLP values, an approximate of the scanner-derived DLP, was multiplied by the conversion factor for the trunk (0.015 mSv mGy cm−1), as a simplified method to obtain ED. When using the simplified method, ED was overestimated by 32.3%−70.2% and 56.5%−66.2% for the GE and Siemens scanners, respectively. The degree of overestimation was positively and closely correlated with the contribution of the middle and distal portions of the lower extremities to total radiation exposure. ED/DLP averaged within each group, corresponding to the conversion factor, was 0.0089−0.0114 and 0.0091−0.0096 mSv mGy cm−1 for the GE and Siemens scanners, respectively. In CT venography of the lower extremities, ED is greatly overestimated by multiplying the scanner-derived DLP by the conversion factor for the trunk. The degree of overestimation varies widely depending on the imaging conditions. It is recommended to divide the scan range and calculate ED as a sum of regional ED values.

A Method for Evaluating the T2∗-weighting Effect in MRI

To propose a method for evaluating the T2*-weighting effect in MRI. Multiple solutions with different concentrations of a superparamagnetic iron oxide contrast agent were made and their signal intensities on T2*-weighted images were measured. The relationship between iron concentration and signal intensity was determined, and we simulated an iron concentration map representing a simplified model of a brain microbleed and converted the pixel values in the map to signal intensity based on the determined relationship, generating a simulated T2*-weighted image. An 'S-value' parameter was defined to evaluate the low-intensity regions in the simulated image. S-values were obtained using T2*-weighted sequences acquired with different echo time (TE) values on three MRI scanners (Philips 1.5 T, GE 3.0 T, and Siemens 3.0 T). Another parameter (A-value) defined by the American Society for Testing and Materials (ASTM-F2119) for assessing artifacts was applied to evaluate the weighting effect in the T2*-weighted image of a laboratory-made susceptibility-effect phantom. With all three scanners, the S-values increased as the TE increased, indicating enhancement of the T2*-weighting effect. For every TE, the S-values obtained for the Philips scanner were the largest, followed by those for the GE and Siemens scanners. The results of this comparative evaluation were similar to those obtained using A-values. Comparisons with the established A-value parameter showed our proposed method for the quantitative evaluation of the T2*-weighting effect using S-values to be valid. The proposed method has the advantage that the S-values do not depend on a specific susceptibility-effect phantom.

Design and Evaluation of MeVisLab Networks for Co-Registration and Cropping of Positron Emission Tomography/Computed Tomography Scans.

Objective:The aim of the present study was to design and evaluate two MeVisLab networks, one for co-registration of positron emission tomography/computed tomography (PET/CT) images and second for cropping the co-registered PET/CT images.Materials and Methods:Two MeVisLab networks, one to co-register and export PET/CT DICOM images and second for cropping the co-registered PET/CT images were designed using different modules of registration toolkit MERIT. One hundred and twenty-five PET/CT studies were exported from Siemens and GE scanners in DICOM format. These images were co-registered and cropped with our designed networks. The images co-registered with our network were compared visually with the co-registered images of same PET/CT studies on vendor provided workstations by an experienced nuclear medicine physician (NMP). The perfection of the cropping of co-registered images was also assessed visually.Results:Visually, NMP found all 125 images co-registered using the network designed in our study similar to the co-registered images of vendor provided workstations. Furthermore, the cropping of all co-registered images was perfectly done by our network.Conclusion:Two MeVisLab networks designed and evaluated in the present study can be used for co-registration of PET/CT DICOM images and cropping the co-registered PET/CT images.

OL92 - Radiomics features of 11[C]-MET PET/CT in primary brain tumors: preliminary results on grading discrimination using a machine learning model

1096 Background: Nowadays Artificial Intelligence (AI) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumors. Previous data suggest that, in primary brain tumor, amino acid PET may demarcate more accurately the active disease than paramagnetic enhanced MRI that is the standard method of the evaluation in brain tumors and help in the evaluation of disease grading, as a fundamental basis for the correct clinical management of these patients. The aim of this study is to propose a radiomics workflow in order to create a predictive model on 11[C]-MET PET/CT scan images capable to discriminate between low-grade and high-grade primary brain tumors. Materials and Methods: in this retrospective study we selected fifty-six patients affected by primary brain tumor who underwent 11[C]-MET PET/CT from January 2016 to December 2019 at the Nuclear Medicine department of the Cannizzaro Hospital of Catania. Histological specimen the analysis was available in all patients in order to confirm the diagnosis and grading disease. PET/CT acquisition was performed after 10 minutes from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 minutes. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to perform the segmentation and the extraction of features in order to identify the volume of interest (VOI) and to extract features of first, second, and third-order. LIFEx calculates 44 RF reflecting the VOI shape, the VOI voxel values, the histogram of VOI values, or the VOI textural content. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the outcome. A machine learning model based on discriminant analysis was tested in the evaluation of grading prediction (low vs high-grade) of 11[C]-MET PET/CT. Results: The machine learning model applicated in this study to stratify brain tumors as “low” and “high” grade showed discriminant texture features as follow: two features in fixed ROI respectively NGLDM Busyness (p-value: 0.1615) and GLZLM LZLGE (p-value 0.3207); three features in total volume respectively SHAPE Sphericity (p-value 0.0314), SHAPE Compacity (p-value 0.0215) and HISTO Kurtosis (p-value 0.0232). The results of performances, using a semi-automatic and user-independent segmentation process and an innovative feature extraction process on fixed ROI, showed a good performance in prediction of disease grade with an accuracy of 70.31% (AUC 64.13%) in all patients, of 80.51% (AUC 65.73%) in GE tomograph, of 84.98%(AUC 78.91%) in Siemens Tomograph. Using our model of machine learning with discriminant analysis the results of the performances were suboptimal in all patients group( 24 Siemens plus 32 GE) with an accuracy of 57.25 % and AUC of 58.51%. However in subgroup analysis, despite the low number of patients and different scanner, our model demonstrated good performances in the prediction of disease grade as follow: GE Scanner Accuracy 71.64 and AUC 62.8%; Siemens Scanner Accuracy 72,88% and AUC 78.91%. Conclusions: This preliminary study on radiomics features analysis of 11[C]-MET PET/CT suggests a possible value of Artificial Intelligence to assess primary brain tumors and to predict disease outcome in terms of grading discrimination at diagnosis. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

Abstract 11438: Texture Analysis Identifies Differences in Cardiac Magnetic Resonance T1 Maps of Pediatric Heart Transplant Patients with Rejection

Introduction: Graft rejection is a significant contributor to morbidity and mortality in pediatric heart transplant recipients, despite surveillance by endomyocardial biopsy (EMB). Cardiac Magnetic Resonance (CMR) T1 mapping has shown potential for noninvasive diagnosis of rejection. However, variability in normative T1 values across field strengths, vendors, and individual scanners limits the utility of absolute T1 values to diagnose rejection across institutions. Texture analysis is an emerging method for quantifying spatial distribution of relative voxel intensities rather than using the absolute voxel intensities themselves and may serve as an additional method for evaluating T1 maps. Hypothesis: We hypothesized that T1 maps from patients with rejection would exhibit differences in texture features from patients without rejection. Methods: This study included 19 cases from 17 pediatric transplant patients (mean age 11.8 ± 5 year, graft age 5.9 ± 4.7 years, 41% female) who underwent CMR imaging and EMB. 8 short axis slices of breath-held T1 mapping using Modified Look-Locker Inversion (MOLLI) recovery on 1.5T Siemens scanner were obtained. Left ventricular myocardium was semiautomatically segmented from T1 maps, excluding the blood pool and epicardial myocardium. Image texture analysis was performed on a single mid-ventricular slice from each case. Rejection was defined as acute cellular rejection (biopsy grade 1R or higher) or negative biopsy with decreased function requiring treatment. T-test analysis was performed to compare differences in texture analysis between patients with and without rejection. Results: Analysis of T1 maps showed differences (p-value ≤ 0.10) between patients with and without rejection in texture features of energy (0.062 ± 0.012 vs 0.091 ± 0.051), entropy (4.59 ± 0.23 vs 4.23 ± 0.60), and variance (0.031 ± 0.005 vs 0.025 ± 0.009). Discussion: In this small pilot study, texture analysis identified differences in tissue character between patients with and without graft rejection. Evaluation of tissue character via texture analysis may provide an additional method for diagnosis of rejection, given the variability in normative T1 values across institutions.

Abstract 11163: Biomechanics of Left Ventricular Remodeling in a Chronic Swine Model of Myocardial Infarction

Introduction: Adverse left ventricular (LV) remodeling after a myocardial infarction (MI) is an insidious process whose severity predicts future decompensated heart failure, morbidity and mortality. Our understanding of LV remodeling has been extensive at the molecular level, but little is known of the biomechanical changes that occur as the LV remodels, dilates, thins, and potentially has rearranged myofibers. In this study, we developed a chronic swine model of post-MI remodeling and used cardiac MRI to assess biomechanics of the remodeling LV after 10 weeks of infarction. Methods: Fourteen Yorkshire swine (n=14) were induced percutaneously with an MI by acute thrombosis of the left circumflex artery with ethanol infusion ( Fig.A1-A2 ), and the animals were survived. Echocardiography and MRI was performed at baseline and at 10 weeks to assess LV function and biomechanics, respectively. MRI was performed on a 3T Siemens scanner, with short axis CINE and tagged imaging ( Fig.A3-A4 ). LV strain, twist and torsion from short-axis tagged images and compared between baseline and 10 week values. Results: Survival rate after the infarction was 85.71%. Ejection fraction reduced from 54.79±5.52% at baseline to 47.29±8.36% 10 weeks post-MI (p=0.023) ( Fig.A5 ). LV end diastolic volume, end systolic volume and sphericity index were significantly elevated ( Fig.A6-A8 ). Radial strain values showed an increasing trend at 10 weeks after an MI, compared to baseline values ( Fig.B3 ). Circumferential strain values showed a decreasing trend by 10 weeks after MI compared to baseline values ( Fig.B4 ). Both ventricular twist and torsion were reduced compared to baseline, but the trend was more evident with LV torsion ( Fig.B5-B6 ) . Conclusions: After an MI of the postero-lateral wall, reduced ejection fraction and dilatation were observed as expected. LV radial strain was increased but circumferential strain, twist, and torsion of the left ventricle were significantly diminished.