Contrast-to-noise Ratio Research Articles

Comparing visualization performance of liquid embolic agents using a novel injectable phantom.

Radiographic visualization of liquid embolic agents (LEAs) during embolization procedures in neurovascular territory represents a crucial feature to ensure efficacy and safety for the patients during endovascular treatment of arteriovenous shunting lesions. Radiopacity of available LEAs varies significantly and limited methods are currently available for comparison. The purpose of this study was to compare the contrast resolution (CR) during injection under blank roadmap of various LEAs, as well as standard contrast material. An injectable angiographic phantom was designed consisting of parallel tubings between 313 and 1000 micron. Under roadmap, eight radiopaque liquid agents were injected and analyzed: Onyx18®, 34® Squid®12, 18, PHIL®25% (PHIL®25), PHIL®30% (PHIL®30).TrufillTM (NBCA), 30% dilution and Omnipaque®300. CR was evaluated as a contrast to noise ratio (CNR) and calculated as mean peak signal (Sa) minus mean background signal (Sb) divided by the standard deviation of the background signal (Std) . Omnipaque 300 and NBCA were found to have the highest CR. PHIL®25 demonstrated the lowest CNR (45% of Omnipaque CNR). Onyx 18 and 34 (Both around 82% of Omnipaque CNR) demonstrated higher CNR compared to Squid®12 and 18 (52-55% of Omnipaque CNR). On average, at 500 micron there is a >70% reduction in CNR, and at 313 micron there is a 90% reduction in CNR compared to 1000 micron. Significantly different CNR between most LEAs and iodinated contrast media was evident under roadmap conditions and should be considered prior to injection.

Comparison of the Image Quality of Turbo Spin Echo and Echo-Planar Diffusion-Weighted Imaging of the Head and Neck Region.

Magnetic resonance imaging (MRI) of the head and neck region is notably challenging due to the complex anatomy and the critical need for high-resolution imaging to accurately diagnose various pathologies. The two prominent MRI techniques used in this context are turbo spin echo (TSE) and echo-planar diffusion-weighted imaging (EP-DWI). TSE is recognized for providing high-resolution anatomical images, whereas EP-DWI offers functional imaging that highlights the diffusion of water molecules, essential for detecting early pathological changes. This study aims to compare the image quality of TSE and EP-DWI in the head and neck region to assess their diagnostic efficacy and clinical utility. This retrospective study was conducted at Saveetha Medical College and Hospital over six months. A total of 100 patients (50 males and 50 females, aged 18-65 years) with various head and neck pathologies were included. Patients underwent both TSE and EP-DWI sequences using a Philips MULTIVA 1.5 T scanner. Image quality was assessed based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact presence, and lesion detection. Two experienced radiologists independently reviewed the images, with inter-observer agreement calculated using Cohen's kappa coefficient. The mean SNR for TSE was significantly higher than EP-DWI (45.2 vs. 28.7, p<0.01), indicating superior image clarity and detail in TSE images. TSE demonstrated a higher mean CNR compared to EP-DWI (25.4 vs. 15.8, p<0.01), suggesting better differentiation between different tissue types and pathologies. Artifacts were more frequent in EP-DWI images (45% vs. 15%), with motion artifacts being the most common. TSE detected more lesions (120 vs. 95), with more precise delineation of lesions. The inter-observer agreement was excellent for both TSE and EP-DWI, with kappa values of 0.85 and 0.80, respectively. TSE MRI provides superior image quality compared to EP-DWI for evaluating the head and neck region. The enhanced SNR and CNR in TSE images result in clearer and more detailed visualizations of anatomical structures and pathological changes, with fewer artifacts. While EP-DWI is valuable for functional imaging, its role should be complementary to TSE. The study suggests that TSE should be the preferred modality for detailed anatomical assessment in the head and neck region. Further studies with larger sample sizes and advanced imaging techniques may provide additional insights into optimizing MRI protocols for head and neck imaging.

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen’s Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

Background and ObjectiveWe develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners. MethodsWe designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1–5 likert scoring system. ResultsObjective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data. ConclusionThis work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.

Enhanced 3D Magnetic Resonance Cholangiopancreatography with Fast Gradient and Spin Echo Sequence: Technical Advancements and Clinical Implications

Objective: The application of fast gradient and spin echo (GRASE) sequence in 3D magnetic resonance cholangiopancreatography (MRCP) was investigated. Methods: Sixty patients who underwent 3D MRCP examination using the Philips Ingenia 3.0T superconducting MR imaging system and a 32-channel phased array direct digital RF receiving coil in the hospital from June 2021 to June 2023 were selected. The acquisition times of the two sequences were compared, and the scanned images of the two sequences were subjectively and objectively evaluated, with the scanning sequence including turbo spin echo (TSE) sequence and GRASE sequence. Results: The display scores, image artifact scores, and overall image quality scores of the GRASE sequence on the common bile duct, gallbladder duct, main pancreatic duct, left and right hepatic ducts were higher than those of the TSE sequence, and the differences were statistically significant (P&lt;0.05). The acquisition time of GRASE sequence was 16 seconds, which was about 96% shorter than that of TSE sequence (399.06±84.53) seconds. The contrast to noise ratio (CNR) of the GRASE sequence was higher than that of the TSE sequence, and the difference was statistically significant (P &lt; 0.05); The signal to noise ratio (SNR) of the GRASE sequence was slightly higher than that of the TSE sequence, but the difference was not statistically significant (P &gt; 0.05). Conclusion: The use of the GRASE sequence in 3D MRCP examinations can improve image quality, shorten acquisition time, and ensure CNR.

Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.

Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance. The 2022 AutoPet Challenge's 1014 PET/CT dataset was used to train the lesion detection model based on 2D and 3D fractional-residual (F-Res) models. To extend this to PET/MRI, a network for converting MR images to synthetic CT (sCT) was developed, using 41 sets of whole-body MR and corresponding CT data. 38 patients' PET/CT and PET/MRI data were used to verify the universal lesion recognition algorithm. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion count were calculated from the resultant lesion masks. Experienced physicians reviewed and corrected the model's outputs, establishing the ground truth. The performance of the lesion detection deep-learning model on different PET images was assessed by detection accuracy, precision, recall, and dice coefficients. Data with a detection accuracy score (DAS) less than 1 was used for analysis of outliers. Compared to PET/CT, PET/MRI scans had a significantly longer delay time (135 ± 45min vs 61 ± 12min) and lower SNR (6.17 ± 1.11 vs 9.27 ± 2.77). However, CNR values were similar (7.37 ± 5.40 vs 5.86 ± 6.69). PET/MRI detected more lesions (with a mean difference of -3.184). TLG and MTV showed no significant differences between PET/CT and PET/MRI (TLG: 119.18 ± 203.15 vs 123.57 ± 151.58, p = 0.41; MTV: 36.58 ± 57.00 vs 39.16 ± 48.34, p = 0.33). A total of 12 PET/CT and 14 PET/MRI datasets were included in the analysis of outliers. Outlier analysis revealed PET/CT anomalies in intestines, ureters, and muscles, while PET/MRI anomalies were in intestines, testicles, and low tracer uptake regions, with false positives in ureters (PET/CT) and intestines/testicles (PET/MRI). The deep learning lesion detection model performs well with both PET/CT and PET/MRI. SNR, CNR and reconstruction parameters minimally impact recognition accuracy, but delay time post-injection is significant.

An Image-Domain Deep-Learning Denoising Technique for Accelerated Parallel Brain MRI: Prospective Clinical Evaluation

Abstract Background Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts. Purpose To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly-accelerated MRI scans, improving image quality and reducing scan time. Materials and Methods Training was performed by retrospectively degrading standard accelerated images. Evaluation was performed with both retrospective and prospectively-collected highly-accelerated images. Retrospective data were taken from ∼2000 MRI studies obtained between August 2016 and October 2022, and prospective data were collected in &amp;gt; 200 subjects between June and November 2022, using scanners from multiple vendors and locations. Scan time data were collected from prospective studies and used to compute time savings per sequence and per protocol for each vendor. Images were evaluated qualitatively by five board-certified radiologists and quantitatively by assessing noise, contrast and spatial resolution. Paired Wilcoxon signed-rank tests were used to compare model outputs to model inputs and low-acceleration images. Results Images from 101 adults from 5 sites and 6 scanner models from different vendors were enrolled. 89% of imaged subjects had noteworthy imaging features or pathology. Model outputs were rated superior to model inputs (p &amp;lt; 0.001) and most were either non-inferior (p inf &amp;gt; 0.05) or superior (p sup &amp;lt; 0.05) to baseline images in qualitative metrics of image quality and feature visibility. Quantitative evaluation of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) improved for model outputs compared to inputs (p &amp;lt; 0.001) or baseline images (p &amp;lt; 0.005). Apparent resolution measured using the full width at half maximum or minimum (FWHM) was either enhanced (p sup &amp;lt; 0.05) or preserved (non-superior p sup &amp;gt; 0.05 and non-inferior p inf &amp;gt; 0.05). The scan time was reduced by an average of 29% (19%-41% per sequence). Conclusion This vendor-agnostic AI-based method achieved robust scan time savings without loss of image quality, potentially allowing for reduced cost and improved patient experience.

Optimization and validation of low-field MP2RAGE T1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers.

Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping. To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers. The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (±SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (±SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation. Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250ms, inversion times (TI1/TI2) of 500/1200ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (±SD) relative error of 3.45%±1.30%, T1NR of 20.13±5.31, and CV of 2.22%±0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (±SD) T1 values: 435.36±10.01ms for WM and 623.29±14.64ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46%±0.31% and 0.35%±0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM. Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.

Iodine concentration, HU accuracy, and metal artifacts evaluation on second-generation dual-layer spectral detector CT images with metal implants: a phantom study.

Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy. To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts. An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted. The MAPEiodine values of all regions of interest across different scanning configurations were all <5%. Almost all APEiodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast. VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.

Ferumoxytol-Enhanced 5D Multiphase Steady-State Imaging Using Rotating Cartesian K-Space With Low-Rank Reconstruction for Pediatric Congenital Heart Disease.

The rotating Cartesian k-space multiphase steady-state imaging with contrast (ROCK-MUSIC) pulse sequence enables acquisition of whole-heart, cardiac phase-resolved images in pediatric congenital heart disease (CHD) without reliance on the ventilator gating signal. Multidimensional reconstruction with low rank tensor (LRT) has shown promise for resolving complex cardiorespiratory motion. To enhance ROCK-MUSIC by resolving cardiorespiratory phases using LRT reconstruction and to enable semi-automatic hyperparameter tuning by developing an image quality scoring model. Retrospective. Thirty patients (45% female, age 2 days to 6.7 years) with CHD. 3-T, four-dimensional (4D) spoiled gradient recalled echo sequence. Eigenvector-based iTerative Self-consistent Parallel Imaging Reconstruction (ESPIRiT) served as the reference comparison for LRT reconstruction. A 4-point Likert scale was used for cardiac and vascular image quality scoring based on cardiac chamber definition, lumen signal uniformity, vascular margin clarity, and motion artifact. Ejection fraction and ventricular volumes were assessed in 16 patients. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness were computed. Intraclass correlation coefficients, Wilcoxon signed-rank test, Bland-Altman. A P-value <0.05 was considered statistically significant. Relative to ESPIRiT, LRT images received significantly higher cardiac (2.81 ± 0.57 vs. 3.19 ± 0.54) and vascular (2.81 ± 0.60 vs. 3.36 ± 0.53) image quality scores. Image quality scoring with semi-automated hyperparameter tuning showed strong correlations (R2 = 0.748) among image quality, SNR, and septal sharpness. Comparison of ejection fraction and volumetry derived from ESPIRiT, and LRT showed no significant systematic difference (P = 0.32). Integration of low-rank reconstruction with ROCK-MUSIC acquisition may be feasible, and semi-automatic hyperparameter tuning could be effective for generating cardiorespiratory resolved images. 2 TECHNICAL EFFICACY: Stage 1.

Ultrahigh-Resolution Visualization of Vascular Heterogeneity in Brain Tumors via Magnetic Nanoparticles-Enhanced Susceptibility-Weighted Imaging.

The precise assessment of vascular heterogeneity in brain tumors is vital for diagnosing, grading, predicting progression, and guiding treatment decisions. However, currently, there is a significant shortage of high-resolution imaging approaches. Herein, we propose a contrast-enhanced susceptibility-weighted imaging (CE-SWI) utilizing the minimalist dextran-modified Fe3O4 nanoparticles (Dextran@Fe3O4 NPs) for ultrahigh-resolution mapping of vasculature in brain tumors. The Dextran@Fe3O4 NPs are prepared via a facile coprecipitation method under room temperature, and exhibit small hydrodynamic size (28 nm), good solubility, excellent biocompatibility, and high transverse relaxivity (r2*, 159.7 mM-1 s-1) under 9.4 T magnetic field. The Dextran@Fe3O4 NPs-enhanced SWI can increase the contrast-to-noise ratio (CNR) of cerebral vessels to 2.5 times that before injection and achieves ultrahigh-spatial-resolution visualization of microvessels as small as 0.1 mm in diameter. This advanced imaging capability not only allows for the detailed mapping of both enlarged peritumoral drainage vessels and the intratumoral microvessels, but also facilitates the sensitive imaging detection of vascular permeability deterioration in a C6 cells-bearing rat glioblastoma model. Our proposed Dextran@Fe3O4 NPs-enhanced SWI provides a powerful imaging technique with great clinical translation potential for the precise theranostics of brain tumors.

Dual-source photon-counting computed tomography for coronary in-stent observation: influence of heart rate and virtual monoenergetic image.

To investigate the effect of heart rate and virtual monoenergetic image (VMI) on coronary stent imaging in dual-source photon-counting detector computed tomography (PCD-CT). A dynamic cardiac phantom was used to vary the heart rate at 50 beats per minute (bpm), 70bpm, and 90bpm. Five types of stents (4.0mm, 3.5mm, 3.0mm, 2.75mm, and 2.5mm diameter) were scanned at three different locations and reconstructed VMI at 70keV. In addition, 50% stenosis was assessed for 3.0mm and 4.0mm stents. To assess in-stent stenosis, 40keV, 70keV, and 100keV images were compared. Measurable lumen and contrast to noise ratio (CNR) from lumen to stenosis were evaluated quantitatively. A-4-point scale was used for the qualitative image quality of in-stent stenosis. The measurable lumen had no significant differences among heart rates in patent stents (p = 0.55). In-stent stenosis, the residual lumen was significantly larger in 40keV [27.5% (20.8-32.3%)] than in 70keV [11.5% (10.0-23.0%), p < 0.05] and 100keV [0% (0-5.2%), p < 0.05]. The CNR was higher in 40keV [12.5 (7.5-18.2)] than in 70keV [5.3 (2.9-7.7), p < 0.05] and 100keV [1.3 (0.5-2.7), p < 0.05]. The image quality was better in 40keV (3.4 ± 0.7) than in 70keV [(2.6 ± 0.8), p < 0.05] and 100keV [(1.3 ± 0.4), p < 0.05]. Dual-source PCD-CT maintains a measurable lumen even at high heart rates. Adjusting the VMI can be helpful in visualizing the in-stent stenosis.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BackgroundProstate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.MethodsWe conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.ResultsVirtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).ConclusionsVirtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

PCD-CT enables contrast media reduction in abdominal imaging compared to an individualized kV-adapted contrast media injection protocol on EID-CT

ObjectivesThis study aims to demonstrate reduced iodine contrast media (CM) in routine abdominal CT scans in portal venous phase (PVP) using a photon-counting detector CT (PCD-CT) compared to total body weight (TBW) and kV-adapted CM injection protocols on a state-of-the-art energy-integrating detector CT (EID-CT) while maintaining sufficient image quality (IQ). Materials and MethodsConsecutive contrast-enhanced abdominal PVP CT scans from an EID-CT (Nov 2022-March 2024) and a PCD-CT (Sep 2023-Dec 2023) were compared. CM parameters (total iodine load (TIL), iodine delivery rate (IDR) and dosing factor (DF)) were reported. An individualized acquisition and CM injection protocol based on TBW and kV was applied for the EID-CT and a TBW adapted CM injection protocol was used for the PCD-CT. Objective IQ was evaluated with mean attenuation (Hounsfield Units, HU), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)). Subjective IQ was assessed via a 5-point Likert scale by 2 expert readers based on diagnostic confidence. ResultsBased on 91 EID-CT scans and 102 PCD-CT scans a TIL reduction of 20.1 % was observed for PCD-CT. PCD-CT demonstrated significantly higher SNR (9.9 ± 1.7 vs. 9.1 ± 1.8, p < 0.001) and CNR (5.1 ± 1.7 vs. 4.3 ± 1.3, p < 0.001) compared to EID-CT. Subjective IQ assessment showed that all scans had sufficient diagnostic IQ. ConclusionsPCD-CT allows for CM reduction while providing higher SNR and CNR compared to EID-CT, using clinical individualized scan and CM injection protocols.

Transcranial ultrafast ultrasound Doppler imaging: A phantom study

Ultrafast ultrasound Doppler imaging facilitates the assessment of cerebral hemodynamics with high spatio-temporal resolution. However, the significant acoustic impedance mismatch between the skull and soft tissue results in phase aberrations, which can compromise the quality of transcranial imaging and introduce biases in velocity and direction quantification of blood flow. This paper proposed an aberration correction method that combines deep learning-based skull sound speed modelling with ray theory to realize transcranial plane-wave imaging and ultrafast Doppler imaging. The method was validated through phantom experiments using a linear array with a center frequency of 6.25 MHz, 128 elements, and a pitch of 0.3 mm. The results demonstrated an improvement in the imaging quality of intracranial targets when using the proposed method. After aberration correction, the average locating deviation decreased from 1.40 mm to 0.27 mm in the axial direction, from 0.50 mm to 0.20 mm in the lateral direction, and the average full-width-at-half-maximum (FWHM) decreased from 1.37 mm to 0.97 mm for point scatterers. For circular inclusions, the average contrast-to-noise ratio (CNR) improved from 8.1 dB to 11.0 dB, and the average eccentricity decreased from 0.36 to 0.26. Furthermore, the proposed method was applied to transcranial ultrafast Doppler flow imaging. The results showed a significant improvement in accuracy and quality for blood Doppler flow imaging. The results in the absence of the skull were considered as the reference, and the average normalized root-mean-square errors of the axial velocity component on the five selected axial profiles were reduced from 17.67% to 8.02% after the correction.

Bone Imaging of the Knee Using Short-Interval Delta Ultrashort Echo Time and Field Echo Imaging.

Background: Computed tomography (CT) is the preferred imaging modality for bone evaluation of the knee, while MRI of the bone is actively being developed. We present three techniques using short-interval delta ultrashort echo time (δUTE), field echo (FE), and FE with high resolution-deep learning reconstruction (HR-DLR) for direct bone MRI. Methods: Knees of healthy volunteers (n = 5, 3 females, 38 ± 17.2 years old) were imaged. CT-like images were generated by averaging images from multiple echoes and inverting. The bone signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were determined. Results: The δUTE depicted a cortical bone with high signal intensity but could not resolve trabeculae. In contrast, both the FE and FE HR-DLR images depicted cortical and trabecular bone with high signal. Quantitatively, while δUTE had a good bone SNR of ~100 and CNR of ~40 for the cortical bone, the SNR for the FE HR-DLR was significantly higher (p < 0.05), at over 400, and CNR at over 200. Conclusions: For 3D rendering of the bone surfaces, the δUTE provided better image contrast and separation of bone from ligaments and tendons than the FE sequences. While there still is no MRI technique that provides a perfect CT-like contrast, continued advancement of MRI techniques may provide benefits for specific use cases.

Real-time motion artifact suppression using convolution neural networks with penalty in fNIRS.

Removing motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural networks have found applications in diverse domains, such as voice and image processing, while their utility in signal processing remains limited. In this work, we introduce an innovative neural network-based approach for online fNIRS signals processing, tailored to individual subjects and requiring minimal prior experimental data. Specifically, this approach employs one-dimensional convolutional neural networks with a penalty network (1DCNNwP), incorporating a moving window and an input data augmentation procedure. In the training process, the neural network is fed with simulated data derived from the balloon model for simulation validation and semi-simulated data for experimental validation, respectively. Visual validation underscores 1DCNNwP's capacity to effectively suppress MAs. Quantitative analysis reveals a remarkable improvement in signal-to-noise ratio by over 11.08 dB, surpassing the existing methods, including the spline-interpolation, wavelet-based, temporal derivative distribution repair with a 1 s moving window, and spline Savitzky-Goaly methods. Contrast-to-noise ratio (CNR) analysis further demonstrated 1DCNNwP's ability to restore or enhance CNRs for motionless signals. In the experiments of eight subjects, our method significantly outperformed the other approaches (except offline TDDR, t < -3.82, p < 0.01). With an average signal processing time of 0.53 ms per sample, 1DCNNwP exhibited strong potential for real-time fNIRS data processing. This novel univariate approach for fNIRS signal processing presents a promising avenue that requires minimal prior experimental data and adapts seamlessly to varying experimental paradigms.

Compressed SENSitivity Encoding (SENSE): Qualitative and Quantitative Analysis.

This study aimed to qualitatively and quantitatively evaluate T1-TSE, T2-TSE and 3D FLAIR sequences obtained with and without Compressed-SENSE technique by assessing the contrast (C), the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR). A total of 142 MRI images were acquired: 69 with Compressed-SENSE and 73 without Compressed-SENSE. All the MRI images were contoured, spatially aligned and co-registered using 3D Slicer Software. Two radiologists manually drew 12 regions of interests on three different structures of CNS: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). C values were significantly higher in Compressed-SENSE T1-TSE compared to No Compressed-SENSE T1-TSE for three different structures of the CNS. C values were also significantly lower for Compressed-SENSE 3D FLAIR and Compressed-SENSE T2-TSE compared to the corresponding No Compressed-SENSE scans. While CNR values did not significantly differ in GM-WM between Compressed-SENSE and No Compressed-SENSE for the 3D FLAIR and T1-TSE sequences, the differences in GM-CSF and WM-CSF were always statistically significant. Compressed-SENSE for 3D T2 FLAIR, T1w and T2w sequences enables faster MRI acquisition, reducing scan time and maintaining equivalent image quality. Compressed-SENSE is very useful in specific medical conditions where lower SAR levels are required without sacrificing the acquisition of helpful diagnostic sequences.

Deep learning reconstruction for optimized bone assessment in zero echo time MR imaging of the knee

PurposeTo evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla. MethodsThis retrospective study included 48 consecutive exams of 46 patients (23 females) who underwent clinically indicated knee MRI at 1.5 Tesla. Standard imaging protocol comprised a sagittal prescribed, isotropic ZTE sequence. ZTE image reconstruction was performed with a standard-of-care (non-DL) and prototype DLRecon method. Exams were divided into subsets with and without osseous pathology based on the radiology report. Using a 4-point scale, two blinded readers qualitatively graded features of bone depiction including artifacts and conspicuity of pathology including diagnostic certainty in the respective subsets. Quantitatively, one reader measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone. Comparative analyses were conducted to assess the differences between the reconstruction methods. In addition, interreader agreement was calculated for the qualitative gradings. ResultsDLRecon significantly improved gradings for bone depiction relative to non-DL reconstruction (all, p < 0.05), while there was no significant difference with regards to artifacts (both, median score of 0; p = 0.058). In the subset with pathologies, conspicuity of pathology and diagnostic confidence were also scored significantly higher in DLRecon compared to non-DL (median 3 vs 2; p ≤ 0.03). Interreader agreement ranged from moderate to almost-perfect (κ = 0.54–0.88). Quantitatively, DLRecon demonstrated significantly enhanced CNR and SNR of bone compared to non-DL (p < 0.001). ConclusionZTE MRI with DLRecon improved bone depiction in the knee, compared to non-DL. Additionally, DLRecon increased conspicuity of osseous findings together with diagnostic certainty.