Lesion Contrast-to-noise Ratio Research Articles

Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors.

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors. Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves. The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results. Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images.

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning

ObjectiveTo improve the PET image quality by a deep progressive learning (DPL) reconstruction algorithm and evaluate the DPL performance in lesion quantification.MethodsWe reconstructed PET images from 48 oncological patients using ordered subset expectation maximization (OSEM) and deep progressive learning (DPL) methods. The patients were enrolled into three overlapped studies: 11 patients for image quality assessment (study 1), 34 patients for sub-centimeter lesion quantification (study 2), and 28 patients for imaging of overweight or obese individuals (study 3). In study 1, we evaluated the image quality visually based on four criteria: overall score, image sharpness, image noise, and diagnostic confidence. We also measured the image quality quantitatively using the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), contrast-to-background ratio (CBR), and contrast-to-noise ratio (CNR). To evaluate the performance of the DPL algorithm in quantifying lesions, we compared the maximum standardized uptake values (SUVmax), SBR, CBR, SNR and CNR of 63 sub-centimeter lesions in study 2 and 44 lesions in study 3.ResultsDPL produced better PET image quality than OSEM did based on the visual evaluation methods when the acquisition time was 0.5, 1.0 and 1.5 min/bed. However, no discernible differences were found between the two methods when the acquisition time was 2.0, 2.5 and 3.0 min/bed. Quantitative results showed that DPL had significantly higher values of SBR, CBR, SNR, and CNR than OSEM did for each acquisition time. For sub-centimeter lesion quantification, the SUVmax, SBR, CBR, SNR, and CNR of DPL were significantly enhanced, compared with OSEM. Similarly, for lesion quantification in overweight and obese patients, DPL significantly increased these parameters compared with OSEM.ConclusionThe DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.

Utility of spectral CT with orthopedic metal artifact reduction algorithms for 125I seeds implantation in mediastinal and hepatic tumors.

Virtual monochromatic image (VMI) combined with orthopedic metal artifact reduction algorithms (VMI + O-MAR) can effectively reduce artifacts caused by metal implants of different types. Nevertheless, so far, no study has systematically evaluated the efficacy of VMI + O-MAR in reducing various types of metal artifacts induced by 125I seeds. The aim of this study was to assess the effectiveness of combining spectral computed tomography (CT) images with O-MAR in reducing metal artifacts and improving the image quality affected by artifacts in patients after 125I radioactive seeds implantation (RSI). A total of 45 patients who underwent dual-layer detector spectral CT (DLCT; IQon, Philips Healthcare) scanning of mediastinal and hepatic tumors after 125I RSI were retrospectively included. Spectral data were reconstructed into conventional image (CI), VMI, CI combined with O-MAR (CI + O-MAR), and VMI + O-MAR to evaluate the de-artifact effect and image quality improvement. Objective indicators included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) of lesions affected by artifacts. Subjective indicators included assessment of overcorrected artifacts and new artifacts, different morphology of artifacts, and overall image quality. In artifact-affected lesion areas, SNR and CNR in the CI/VMI + O-MAR groups were better than those in CI groups (all P values <0.05). The AI showed a downward trend as VMI keV increased (all P values <0.001). The AI values of the CI/VMI (50-150 keV) group were all higher than the groups of CI/VMI + O-MAR (50-150 keV) (P<0.001). Overcorrection artifacts and new artifacts were concentrated in the VMI50/70 keV groups. In the evaluation of artifact morphology, as the VMI keV increased, the number of near-field banding artifacts in hyperdense artifacts gradually decreased, whereas the number of minimal or no artifacts increased, and the total number of hyperdense artifacts were decreased. The diagnostic and image quality scores of hyperdense artifacts were higher than those of hypodense artifacts as VMI keV increased. High VMI level combined with O-MAR substantially improve objective and subjective image quality, lesion display ability, and diagnostic confidence of CT follow-up after 125I RSI, especially at the VMI + O-MAR 150 keV level.

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases. This retrospective study included 128 patients who underwent contrast-enhanced dual-energy CT for hepatic metastasis surveillance between July 2019 and June 2022 using a 30% reduced iodine contrast dose in the portal phase. Three image types were reconstructed: 50-keV virtual monoenergetic images (50-keV VMI); linearly blended images simulating 120-kVp images (120-kVp); and post-processed 120-kVp images using DLICA (DLICA 120-kVp). Three reviewers evaluated lesion conspicuity, image contrast, and subjective image noise. We also measured image noise, contrast-to-noise ratios (CNRs), and signal-to-noise ratios (SNRs). The diagnostic performance for hepatic metastases was evaluated using a jackknife alternative free-response receiver operating characteristic method with the consensus of two independent radiologists as the reference standard. DLICA 120-kVp demonstrated significantly higher CNR of lesions to liver (5.7±3.1 vs. 3.8±2.1 vs. 3.8±2.1) and higher SNR compared with 50-keV VMI and 120-kVp (p<0.001 for all). DLICA 120-kVp had significantly lower image noise than 50-kVp VMI for all regions (p<0.001 for all). DLICA 120-kVp also exhibited superior lesion conspicuity (4.0 [3.3-4.3] vs. 3.7 [3.0-4.0] vs. 3.7 [3.0-4.0]), higher image contrast, and lower subjective image noise compared with 50-keV VMI and 120-kVp (p<0.001 for all). Although there was no significant difference in the figure of merit for lesion diagnosis among the three methods (p=0.11), DLICA 120-kVp had a significantly higher figure of merit for lesions with a diameter <20 mm than 50-keV VMI (0.677 vs. 0.648, p=0.007). On a per-lesion basis, DLICA 120-kVp also demonstrated higher sensitivity than the 50-keV VMI (81.2% vs. 72.9%, p<0.001). The specificities per lesion were not significantly different among the three algorithms (p=0.15). DLICA at 120-kVp provided superior lesion conspicuity and image quality and similar diagnostic performance for hypovascular hepatic metastases compared with 50-keV VMI.

Improvement in Intraoperative Image Quality in Transcranial Magnetic Resonance-Guided Focused Ultrasound Surgery Using Transmitter Gain Adjustment

Introduction: Transcranial magnetic resonance-guided focused ultrasound surgery (TcMRgFUS) has the advantage of allowing immediate evaluation of therapeutic effects after each sonication and intraoperative magnetic resonance imaging (MRI) to visualize the lesion. When the image shows that the lesion has missed the planned target and the therapeutic effects are insufficient, the target of the subsequent ablation can be finely adjusted based on the image. The precision of this adjustment is determined by the image quality. However, the current intraoperative image quality with a 3.0T MRI system is insufficient for precisely detecting the lesion. Thus, we developed and validated a method for improving intraoperative image quality. Methods: Because intraoperative image quality is affected by transmitter gain (TG), we acquired T2-weighted images (T2WIs) with two types of TG: the automatically adjusted TG (auto TG) and the manually adjusted TG (manual TG). To evaluate the character of images with 2 TGs, the actual flip angle (FA), the image uniformity, and the signal-to-noise ratio (SNR) were measured using a phantom. Then, to assess the quality of intraoperative images, T2WIs with both TGs were acquired during TcMRgFUS for 5 patients. The contrast-to-noise ratio (CNR) of the lesion was retrospectively estimated. Results: The images of the phantom with the auto TG showed substantial variations between the preset and actual FAs (p < 0.01), whereas on the images with the manual TG, there were no variations between the two FAs (p > 0.05). The total image uniformity was considerably lower with the manual TG than with the auto TG (p < 0.01), indicating that the image’s signal values with the manual TG were more uniform. The manual TG produced significantly higher SNRs than the auto TG (p < 0.01). In the clinical study, the lesions were clearly detected in intraoperative images with the manual TG, but they were difficult to identify in images with the auto TG. The CNR of lesions in images with manual TG was considerably higher than in images with auto TG (p < 0.01). Conclusion: Regarding intraoperative T2WIs using a 3.0T MRI system during TcMRgFUS, the manual TG method improved image quality and delineated the ablative lesion more clearly than the current method with auto TG.

Decorrelated compounding of synthetic aperture ultrasound imaging to detect low contrast thermal lesions induced by focused ultrasound

PurposeDecorrelated Compounding (DC) for synthetic aperture ultrasound can reduce speckle variation in images, suggesting enhanced detectability of low-contrast targets in tissue including thermal lesions produced by focused ultrasound (FUS). The DC imaging method has primarily been investigated in simulation and in phantom studies. This work investigates the feasibility of the DC method in monitoring thermal therapy via image guidance and non-invasive thermometry based on the change in backscattered energy (CBE). MethodsEx vivo porcine tissue was exposed to FUS exposures at acoustic powers of 5 W and 1 W, with peak pressure amplitudes of 0.64 MPa and 0.27 MPa respectively. During FUS exposure, RF echo data frames was acquired using a 7.8 MHz linear array probe and a Verasonics VantageTM ultrasound scanner (Verasonics Inc., Redmond, WA). RF echo data was taken to produce B-mode images, as reference images. Synthetic aperture RF echo data was also acquired and processed using delay-and-sum (DAS), a combination of spatial and frequency compounding referred to as Traditional Compounding (TC), and the proposed DC imaging methods. Image quality was assessed using the contrast-to-noise ratio (CNR) at the FUS beam focus, and the speckle SNR (sSNR) of the background region as preliminary metrics. A calibrated thermocouple was placed near the FUS beam focus for temperature measurements and calibrations using the CBE method. ResultsThe DC imaging method significantly improved image quality to detect low contrast thermal lesions in treated ex vivo porcine tissue in comparison to other imaging methods. In comparison to B-mode imaging, the lesion CNR measured using the DC imaging was shown to improve up to a factor of approximately 5.5. The corresponding sSNR improved by a factor of approximately 4.2 in comparison to B-mode imaging. CBE calculation using the DC imaging method yielded more precise measurements of the backscattered energy compared to other imaging methods studied. ConclusionsThe despeckling performance of the DC imaging method significantly improves the lesion CNR in comparison to B-mode imaging. This suggests that the proposed method can detect low-contrast thermal lesions induced by FUS therapy that are not detectable using standard B-mode imaging. Furthermore, the signal change at the focal point were more precisely measured by DC imaging, and the signal change in response to FUS exposure follows the temperature profile more closely than changes measured using B-mode, as well as synthetic aperture DAS and TC images. These suggest that DC imaging can potentially be used with the CBE method to improve non-invasive thermometry.

Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow-compensated diffusion images in the liver-A quantitative evaluation of postprocessing algorithms.

To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively. The weighted averaging algorithm performed best according to the total quality score ( ). The further ranking was outlier exclusion algorithm ( ), p-mean algorithm ( ), percentile algorithm ( ), and exception set algorithm ( ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe. Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.

Free-Breathing Liver Magnetic Resonance Imaging With Respiratory Frequency-Modulated Continuous-Wave Radar-Trigger Technique: A Preliminary Study

PurposeThe aim of this study is to evaluate the performance of free-breathing liver MRI with a novel respiratory frequency-modulated continuous-wave radar-trigger (FT) technique on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) for both healthy volunteers and patients in comparison to navigator-trigger (NT) and belt-trigger (BT) techniques.MethodsIn this prospective study, 17 healthy volunteers and 23 patients with known or suspected liver diseases were enrolled. Six sequences (T2WI and DWI with FT, NT, and BT techniques) were performed in each subject. Quantitative evaluation and qualitative assessment were analyzed by two radiologists. Overall image quality, blurring, motion artifacts, and liver edge delineations were rated on a 4-point Likert scale. The liver and lesion signal-to-noise ratio (SNR), the lesion-to-liver contrast-to-noise ratio (CNR), as well as the apparent diffusion coefficient (ADC) value were quantitatively calculated.ResultsFor volunteers, there were no significant differences in the image quality Likert scores and quantitative parameters on T2WI and DWI with three respiratory-trigger techniques. For patients, NT was superior to other techniques for image quality on T2WI; conversely, little difference was found on DWI in qualitative assessment. The mean SNR of the liver on T2WI and DWI with BT, NT, and FT techniques was similar in patients, which is in line with volunteers. FT performed better in terms of higher SNR (705.13 ± 434.80) and higher CNR (504.41 ± 400.69) on DWI at b50 compared with BT (SNR: 651.83 ± 401.16; CNR:429.24 ± 404.11) and NT (SNR: 639.41 ± 407.98; CNR: 420.64 ± 416.61) (p < 0.05). The mean ADC values of the liver and lesion with different techniques in both volunteers and patients showed non-significant difference.ConclusionFor volunteers, the performance of T2WI as well as DWI with three respiratory-trigger techniques was similarly good. As for patients, FT-DWI is superior to BT and NT techniques in terms of higher lesion SNR and CNR at b50.

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence

PurposeAcceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and the image quality of a single-shot fat-suppressed turbo spin-echo (TSE) T2-weighted sequence with deep learning reconstruction (HASTEDL) with that of a fat-suppressed T2-weighted BLADE TSE sequence in patients with focal liver lesions. Materials and methodsNinety-five patients (52 men, 43 women; mean age: 61 ± 14 [SD]; age range: 28–87 years) with 42 focal liver lesions (17 hepatocellular carcinomas, 10 sarcoidosis lesions, 9 myeloma lesions, 3 liver metastases and 3 focal nodular hyperplasias) who underwent liver MRI at 1.5 T including HASTEDL and BLADE sequences were retrospectively included. Overall image quality, noise level in the liver, lesion conspicuity and sharpness of liver lesion contours were assessed by two independent readers. Liver signal-to-noise ratio (SNR) and lesion contrast-to-noise ratio (CNR) were measured and compared between the two sequences, as well as the mean duration of the sequences (Student t-test or Wilcoxon test for paired data). ResultsMedian overall quality on HASTEDL images (3; IQR: 3, 3) was significantly greater than that on BLADE images (2; IQR: 1, 3) (P < 0.001). Median noise level in the liver on HASTEDL images (0; IQR: 0, 0.5) was significantly lower than that on BLADE images (1; IQR: 1, 2) (P < 0.001). On HASTEDL images, mean liver SNR (107.3 ± 39.7 [SD]) and mean focal liver lesion CNR (87.0 ± 76.6 [SD]) were significantly greater than those on BLADE images (67.1 ± 23.8 [SD], P < 0.001 and 48.6 ± 43.9 [SD], P = 0.027, respectively). Acquisition time was significantly shorter with the HASTEDL sequence (18 ± [0] s; range: 18–18 s) compared to BLADE sequence (152 ± 47 [SD] s; range: 87–263 s) (P < 0.001). ConclusionBy comparison with the BLADE sequence, HASTEDL sequence significantly reduces acquisition time while improving image quality, liver SNR and focal liver lesions CNR.

Interindividual Comparison of Frequency-Selective Nonlinear Blending to Conventional CT for Detection of Focal Liver Lesions Using MRI as the Reference Standard.

BACKGROUND. Diagnosing liver lesions is challenging. CT is used for primary diagnosis, but its contrast resolution is limited. Investigating methods to improve detection of liver lesions is important. OBJECTIVE. The purpose of this study was to evaluate the effect of frequency-selective nonlinear blending on the detectability of liver lesions on CT. METHODS. A retrospective search yielded 109 patients with 356 malignant and benign liver lesions (191 principally diagnosed, 165 incidental findings) who underwent contrast-enhanced CT (CECT) in the portal venous phase and liver MRI between January 2012 and December 2017. Nonlinear blending was applied to CECT examinations, and three blinded readers independently rated the quality (5-point Likert scale) of randomly presented images. Focal lesions (n = 356) were evaluated for lesion identification and categorization to assess sensitivity. For 191 lesions (primary diagnosis), two readers evaluated CECT and nonlinear blending CT to compare lesion size and the accuracy of subjective measurements. A fourth reader performed ROI measurements for calculation of contrast-to-noise ratio (CNR), and a fifth reader reviewed MRI as the standard of reference. Statistics included interobserver agreement, quantitative comparisons of CNR, lesion size, and subjective image analyses of image quality and sensitivity for detecting liver lesions. RESULTS. Three readers rated the image quality of nonlinear blending CT (rating, 4; 10th-90th percentiles, 4-5) higher than that of CECT (rating, 2; 10th-90th percentiles, 1-3) (p < .001). CECT had good interreader agreement (interclass correlation coefficient [ICC], 0.81; 95% CI, 0.76-0.85), as did nonlinear blending CT (ICC, 0.75; 95% CI, 0.69-0.79). The median CNR of liver lesions increased with nonlinear blending (CECT, 4.18 [10th-90th percentiles, 1.67-9.06]; nonlinear blending CT, 12.49 [10th-90th percentiles, 6.18-23.39]; p < .001). Bland-Altman analysis of lesion size showed a reduction in underestimation from 2.5 (SD, 9.2) mm (95% CI, 1.2-3.9 mm) with CECT to 0.1 (SD, 3.9) mm (95% CI, -0.68 to 0.46 mm) for nonlinear blending CT (concordance correlation coefficient, 0.99). Sensitivity for detecting liver lesions increased to 86% for nonlinear blending CT. The sensitivity of CECT was 76%. CONCLUSION. Frequency-selective nonlinear blending in CECT increases image quality and CNR, increases the precision of size measurement, and increases sensitivity for detecting liver lesions. CLINICAL IMPACT. Use of nonlinear blending CT improves liver lesion detection and increases the accuracy of lesion size measurement, which is important when local ablation or liver transplant is being considered.

Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

BackgroundTo investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification.MethodsThe CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm3. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUVmax, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale.ResultsIn the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUVmax, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference.ConclusionsThe SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT.

3D T1-weighted turbo spin echo contrast-enhanced MRI at 1.5T for frameless brain metastases radiotherapy.

Performance of 3D-T1W-TSE has been proven superior to 3D-MP-GRE at 3T on brain metastases (BM) contrast-enhanced (CE) MRI. However, its performance at 1.5T is largely unknown and sparsely reported. This study aims to assess image quality, lesion detectability and conspicuity of 1.5T 3D-T1W-TSE on planning MRI of frameless BM radiotherapy. 94 BM patients to be treated by frameless brain radiotherapy were scanned using 3D-T1W-TSE with immobilization on multi-vendor 1.5T MRI-simulators. BMs were jointly diagnosed by 4 reviewers. Enhanced lesion conspicuity was quantitatively assessed by calculating contrast ratio (CR) and contrast-to-noise ratio (CNR). Signal-to-noise ratio (SNR) reduction of white matter due to the use of flexible coil was assessed. Lesion detectability and conspicuity were compared between 1.5T planning MRI and 3T diagnostic MRI by an oncologist and a radiologist in 10 patients. 497 BMs were jointly diagnosed. The CR and CNR were 75.2 ± 39.9% and 14.2 ± 8.1, respectively. SNR reduced considerably from 31.7 ± 8.3 to 21.9 ± 5.4 with the longer distance to coils. 3T diagnostic MRI and 1.5T planning MRI yielded exactly the same detection of 84 BMs. Qualitatively, lesion conspicuity at 1.5T was not inferior to that at 3T. Quantitatively, lower brain SNR and lesion CNR were found at 1.5T, while lesion CR at 1.5T was highly comparable to that at 3T. 1.5T 3D-T1W-TSE planning MRI of frameless BM radiotherapy was comprehensively assessed. Highly comparable BM detectability and conspicuity were achieved by 1.5T planning MRI compared to 3T diagnostic MRI. 1.5T 3D-T1W-TSE should be valuable for frameless brain radiotherapy planning.

Averaging Strategy to Form the Imaging for Routine Reading of Insulinoma from Pancreatic Perfusion Dataset

Objective To determine the appropriate averaging strategy for pancreatic perfusion datasets to create images for routine reading of insulinoma.Methods Thirty-nine patients undergoing pancreatic perfusion CT in Peking Union Medical College Hospital and diagnosed as insulinoma by pathology were enrolled in this retrospective study.The time-density curve of abdominal aorta calculated by software dynamic angio was used to decide the timings for averaging.Five strategies,by averaging 3,5,7,9 and 11 dynamic scans in perfusion,all including peak enhancement of the abdominal aorta,were investigated in the study.The image noise,pancreas signal-to-noise ratio(SNR),lesion contrast and lesion contrast-to-noise ratio(CNR)were recorded and compared.Besides,overall image quality and insulinoma depiction were also compared.ANOVA and Friedman's test were performed.Results The image noise decreased and the SNR of pancreas increased with the increase in averaging time points(all P<0.001).The lesion contrast(69.81±41.35)averaged from 5 scans showed no significant difference compared with that(72.77±45.25)averaged from 3 scans(P=0.103),both of which were higher than that in other groups(all P≤0.001).The lesion CNRs of the last four groups showed no significant difference(all P>0.99)and were higher than that of the first group(all P<0.05).There was no significant difference in overall image quality among the 5 groups(P=0.977).Conclusions Image averaged from 5 scans showed moderate image noise,pancreas SNR and relatively high lesion contrast and lesion CNR.Therefore,it is advised to be used in image averaging to detect insulinoma.

Multiparametric dual-energy CT for distinguishing nasopharyngeal carcinoma from nasopharyngeal lymphoma

ObjectivesTo determine the optimal kiloelectron volt of noise-optimized virtual monoenergetic images [VMI (+)] for visualization of nasopharyngeal carcinoma (NPC) and nasopharyngeal lymphoma (NPL), and to explore the clinical value of quantitative parameters derived from dual-energy computed tomography (DECT) for distinguishing the two entities. Materials and methodsEighty patients including 51 with NPC and 29 with NPL were enrolled. The VMIs (+) at 40–80 keV with an interval of 10 keV were reconstructed by contrast enhanced images. The overall image quality and demarcation of lesion margins, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed in VMIs (+) and polyenergetic images (PEI). Normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λHU) and effective atomic number (Zeff) were calculated. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve. ResultsThe 40 keV VMI (+) yielded highest overall image quality scores, demarcation of lesion margins scores, SNR and CNR. The values of NIC, λHU and Zeff in NPL were higher than those in NPC (P < 0.001). Multivariate logistic regression model combining NIC, λHU and Zeff showed the best performance for distinguishing NPC from NPL (AUC: 0.947, sensitivity: 93.1 % and specificity: 92.2 %). ConclusionVMI (+) reconstruction at 40 keV was optimal for visualizing NPC and NPL. Quantitative parameters derived from DECT were helpful for differentiating NPC from NPL.

Evaluation of a Correction Method for 111In-Pentetreotide SPECT Imaging of Gastroenteropancreatic Neuroendocrine Tumors.

The number of patients with the extremely rare disease gastroenteropancreatic (GEP) neuroendocrine tumor (NET) has increased rapidly in recent years. 111In-pentetreotide SPECT in somatostatin receptor scintigraphy has been used for the assessment of GEP NET patients. To diagnose GEP NET, appropriate selection of image correction parameters is critical. Correction methods may improve the 111In-pentetreotide SPECT image quality, but there is currently no standard technique. The purpose of this study was to determine the optimal correction parameter settings for 111In-pentetreotide SPECT. Methods: A phantom study produced images with a tumor-to-background ratio of as high as 16:1. A triple energy window was used for scatter correction (SC), and attenuation correction (AC) was CT-based. Correlation analysis was performed in 4 groups: no correction (NC), SC, AC, and combined SC with AC (CC). The 111In-pentetreotide SPECT results for 20 randomly selected patients (13 men and 7 women; age range, 37-81 y) with confirmed GEP NET were analyzed using data collected 4 h after injection of 111 MBq of 111In-pentetreotide. Emission data were reconstructed using ordered-subset expectation maximization (OSEM) with different settings. Different combinations of the correction parameters were used to analyze the contrast-to-noise ratios (CNRs) obtained with the phantom. In the clinical study, 20 GEP NET patients were used to evaluate the GEP NET lesion CNR by 4 different image correction methods obtained from 111In-pentetreotide SPECT images: NC, SC, AC, and CC. NC was used as a reference method. Results: The phantom study revealed that the optimal energy window in the photopeak for somatostatin receptor scintigraphy was 171 keV ± 10% and 245 keV ± 7.5%, and the optimal OSEM reconstruction conditions were 8 subsets and 6 iterations. Among the OSEM collection conditions, CC produced a significantly higher CNR than NC or SC (P < 0.05). In the clinical study, CC was found to increase the CNR (P < 0.05). Conclusion: CC improves the correction in 111In-pentetreotide SPECT studies, compared with NC, providing better contrast and sharper outlines of lesions and organs.

Breast Cancer Conspicuity on Computed Versus Acquired High b-Value Diffusion-Weighted MRI

On unenhanced diffusion-weighted imaging (DWI), computing or synthesizing high b-value images from lower b-value acquisitions can enhance breast cancer visibility. This study aimed to evaluate relative lesion conspicuity on computed versus acquired diffusion-weighted images and investigate clinical characteristics influencing optimal b-values. Women with newly diagnosed breast cancer were prospectively enrolled and underwent 3T breast MRI with DWI. Lesion contrast-to-noise ratio (CNR) was measured across a range of b-values (0-2500 s/mm2) for computed and acquired DWI. Three readers independently compared lesion visibility between computed and acquired DWI and selected the optimal b-value. Computed versus acquired DWI was compared quantitatively based on CNR by paired t-test and qualitatively based on reader preference using a sign test. Optimal b-values by qualitative and quantitative assessment were compared by paired t-test, and associations with clinical characteristics were assessed by Wilcoxon rank sum test. The study included 30 women (median age, 48 years); 28 with invasive carcinoma, 2 DCIS. Lesion CNR was higher on acquired versus computed images (p = 0.018), while lesion visibility by reader assessment was not different (p = 0.36). Optimal b-values selected by readers (mean, b = 1411 ± 383 s/mm2) were slightly higher than those based on peak CNR (b = 1233 ± 463 s/mm2, p = 0.023), and were higher for younger (≤50 years) versus older women (p = 0.002) and dense versus nondense breasts (p = 0.015). Lesion CNR on computed high b-value images was slightly reduced versus acquired images, but our study suggests that this did not significantly impact lesion visibility. Computing high b-value images offers extra flexibility to adjust b-value during interpretation.

Silent susceptibility-weighted angiography to detect hemorrhagic lesions in the brain: a clinical and phantom study

To compare the effectiveness of silent susceptibility-weighted angiography (sSWAN), a new imaging technique with lower acoustic noise, with conventional susceptibility-weighted angiography (cSWAN) in the detection of intracranial hemorrhagic lesions. We measured the acoustic and background noise during sSWAN and cSWAN imaging and calculated the contrast-to-noise ratio (CNR) of the phantom consisting of eight chambers with different concentrations of superparamagnetic iron oxide. In the clinical study, we calculated the CNRs of hemorrhagic lesions in 15 patients and evaluated the images for conspicuity and artifact on each sequence and scored them on a 4-point scale. We also evaluated whether hypointense areas observed on sSWAN or cSWAN increased in size from those on T2*-weighted imaging (T2*-WI). Acoustic noise for sSWAN (57.9 ± 0.32 dB [background noise 51.3 dB]) was significantly less than that for cSWAN (89.0 ± 0.22 dB [background noise 50.9 dB]). The CNRs of phantoms for sSWAN were slightly but not significantly lower than those for cSWAN (P = 0.18). The CNRs of hemorrhagic lesions did not show significant differences between sSWAN and cSWAN (P = 0.17). There were no significant differences between sSWAN and cSWAN with respect to the scores for conspicuity, artifact, and change in size of hypointense areas from T2*-WI. sSWAN is equivalent to cSWAN with respect to the image quality for the detection of hemorrhagic lesions but has lower acoustic noise.

Evaluation of frequency-selective non-linear blending technique on brain CT in postoperative children with Moyamoya disease

ObjectiveTo evaluate whether a frequency-selective non-linear blending (BC) technique can improve tissue contrast and infarct detection on non-enhanced brain CT (NECT) in postoperative Moyamoya (MMD) patients. Materials and methodsFrom January 2010 to December 2017, 33 children (13boys and 20girls; mean age 9.1±3.4 years) with MMD postoperatively underwent NECT followed by diffusion MRI. We compared the contrast-to-noise ratio (CNR) between gray matter (GM) and white matter (WM) in NECT and BC images and the CNR between the infarct lesion and adjacent normal-appearing brain in NECT and BC images using a paired t-test. We assessed image noise, GM-WM differentiation, artifacts, and overall quality using a Wilcoxon signed rank test. A McNemar two-tailed test was conducted to compare the diagnostic accuracy of infarct detection. ResultsThe CNR between GM and WM and the CNR of the infarct was better in BC images than in NECT images (3.9±1.0 vs. 1.8±0.6, P<0.001 and 3.6±0.3 vs. 1.9±0.2, P<0.001), with no difference in overall image quality observed. The sensitivity and specificity of infarct detection were 55.0% and 76.9% using NECT, and 70.0% and 69.2% using BC technique. The diagnostic accuracy of NECT and BC technique was 63.6% (21/33) and 69.7% (23/33), respectively. ConclusionThis study showed that the BC technique improved CNR and maintained image quality. This technique may also be used to identify ischemic brain changes in postoperative MMD patients by improving the CNR of the infarct lesion.

Optimizing 3D FLAIR to detect MS lesions: pushing past factory settings for precise results.

To assess the diagnostic value of three 3D FLAIR sequences with differing repetition-times (TR) at 3-Tesla when detecting multiple sclerosis (MS) lesions. In this prospective study, approved by the institutional review board, 27 patients with confirmed MS were prospectively included. One radiologist performed manual segmentations of all high-signal intensity lesions using three 3D FLAIR data sets with different TR of 4800ms ("FLAIR4800"), 8000ms ("FLAIR8000") and 10,000ms ("FLAIR10,000") and two radiologists double-checked it. The main judgment criterion was the overall number of lesions; secondary objectives were the assessment of lesion location, as well as measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A non-parametric Wilcoxon's test was used to compare the differing FLAIR. The FLAIR8000 and FLAIR10,000 detected significantly more overall lesions per patient as compared with the FLAIR4800 [116.1 (± 61.7) (p = 0.02) and 115.8 (± 56.3) (p = 0.03) versus 99.2 (± 66.9), respectively]. The FLAIR8000 and FLAIR10,000 detected four and eight times more cortical or juxta-cortical lesions per patient as compared with FLAIR4800 [1.6 (± 2.2) (p = 0.001) and 4.1 (± 5.9) (p = 6×10-5) versus 0.4 (± 1.1), respectively]. CNR was significantly correlated to the TR value. It was significantly higher with FLAIR10,000 than it was with FLAIR8000 and FLAIR4800 [16.3 (± 3.5) versus 15 (± 2.4) (p = 0.01) and 12 (± 2.2) (p = 2×10-6), respectively] CONCLUSION: An optimized 3D FLAIR with a long TR significantly improved both overall lesion detection and CNR in MS patients as compared to a 3D FLAIR with factory settings.