Contrast-to-noise Ratio Values Research Articles

Image quality of the non-contrast BOOST cardiac magnetic resonance sequence to visualize the pulmonary veins in patients with atrial fibrillation

Abstract Background In patients with atrial fibrillation (AF), imaging examinations have an important role in planning ablation by imaging the pulmonary veins (PV). The PV anatomy is conventionally assessed before ablation using computer tomography (CT) or 3 dimensional (3D) contrast-enhanced cardiac magnetic resonance (CMR) imaging. The new 3-dimensional, free-breathing BOOST (Bright-blood and black-blOOd phase SensiTive inversion recovery) cardiac magnetic resonance (CMR) sequence is suitable for imaging the left atrium without the addition of contrast agent. Purpose The aim of our study was to investigate the BOOST sequence for the imaging of PVs, and the effect of heart rate and rhythm on image quality. Methods Forty-seven patients underwent and BOOST CMR before ablation, 25 of them had also left atrial CT angiography (CTA). The BOOST sequence was performed using T2 preparation pre-pulse (T2prep) and magnetization transfer preparation (MTC) techniques. The image quality was analyzed using a subjective Likert scale and quantitatively by determining the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Heart rate and rhythm were assessed by 12-lead ECG. Results MTC-BOOST images were suitable for PVs analysis in all cases, whereas the assessment of PVs on T2prep-BOOST images was inadequate in the majority of cases due to artifacts. CTA showed the same PV anatomy as assessed with BOOST CMR. Based on the operating electrophysiologists’ subjective opinion, the quality of the CTA scans was better compared with BOOST sequence. Still, they found the CMR images appropriate for procedural planning in 43 out of 47 cases (91%). SNR and CNR values of the MTC-BOOST bright-blood images were higher if patients had sinus rhythm. We found a significant or nearly significant negative correlation between heart rate and the SNR and CNR values of MTC-BOOST bright-blood images. Conclusions MTC-BOOST images are suitable for visualisation of PVs, producing diagnostic image quality in 100% of cases. In the majority of cases, MTC-BOOST images were appropriate for procedural planning before ablation. Image quality is affected by the patients' heart rate and frequency. Figure 1: MTC BOOST bright blood image of the left atrium and the right pulmonary veins (arrows)

Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.

This study aimedto investigate the potential benefit of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) angiography in visualization of abdominal arteries in comparison to standard-reconstruction (SR) images of virtual monoenergetic images (VMI) at low kiloelectron volt (keV).We prospectively included 47 and 47 participants to undergo contrast-enhanced abdominal CT scans within UHR mode on a PCD-CT systemusing full-dose (FD) and low-dose (LD) protocols, respectively. The data were reconstructed into six series of images: FD_UHR_Br48, FD_UHR_Bv56, FD_UHR_Bv60, FD_SR_Bv40, LD_UHR_Bv48, and LD_SR_Bv40. The UHR reconstructions were performed with three kernels (Bv48, Bv56, and Bv60) within 0.2mm. The SR were virtual monoenergetic imaging reconstruction with Bv40 kernel at 40-keV within 1mm. Each series of axial images were reconstructed into coronal and volume-rendered images. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of seven arteries were measured. Three radiologists assessed the image quality, and visibility of nine arteries on all the images.SNR and CNR values of SR images were significantly higher than those of UHR images (P < 0.001). The SR images have higher ratings in image noise (P < 0.001), but the FD_UHR_Bv56 and FD_UHR_Bv60 images has higher rating in vessel sharpness (P < 0.001). The overall quality was not significantly different among FD_VMI_40keV, LD_VMI_40keV, FD_UHR_Bv48, and LD_UHR_Bv48 images (P > 0.05) but higher than those of FD_UHR_Bv56 and FD_UHR_Bv60 images (P < 0.001). There is no significant difference of nine abdominal arteries among six series of images of axial, coronal and volume-rendered images (P > 0.05).To conclude, 1-mm SR image of VMI at 40-keV is superior to 0.2-mm UHR regardless of which kernel is used to visualize abdominal arteries, while 0.2-mm UHR image using a relatively smooth kernel may allow similar image quality and artery visibility when thinner slice image is warranted.

Contrast-to-noise ratio comparison between X-ray fluorescence emission tomography and computed tomography.

We provide a comparison of X-ray fluorescence emission tomography (XFET) and computed tomography (CT) for detecting low concentrations of gold nanoparticles (GNPs) in soft tissue and characterize the conditions under which XFET outperforms energy-integrating CT (EICT) and photon-counting CT (PCCT). We compared dose-matched Monte Carlo XFET simulations and analytical fan-beam EICT and PCCT simulations. Each modality was used to image a numerical mouse phantom and contrast-depth phantom containing GNPs ranging from 0.05% to 4% by weight in soft tissue. Contrast-to-noise ratios (CNRs) of gold regions were compared among the three modalities, and XFET's detection limit was quantified based on the Rose criterion. A partial field-of-view (FOV) image was acquired for the phantom region containing 0.05% GNPs. For the mouse phantom, XFET produced superior CNR values ( , 21.6, and 3.4) compared with CT images obtained with both energy-integrating ( , 4.6, and 1.5) and photon-counting ( , 7.7, and 2.0) detection systems. More generally, XFET outperformed CT for superficial imaging depths ( ) for gold concentrations at and above 0.5%. XFET's surface detection limit was quantified as 0.44% for an average phantom dose of 16mGy compatible with in vivo imaging. XFET's ability to image partial FOVs was demonstrated, and 0.05% gold was easily detected with an estimated dose of to a localized region of interest. We demonstrate a proof of XFET's benefit for imaging low concentrations of gold at superficial depths and the feasibility of XFET for in vivo metal mapping in preclinical imaging tasks.

Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study

BackgroundDual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom.MethodsA CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric.ResultsOn average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively.ConclusionOne system outperformed others in DES algorithms, providing higher CNR at lower doses.Relevance statementThis phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose.Key PointsDES images, obtained by recombining LE and HE images, have a major role in CEM.Differences in radiation dose among CEM systems were between 8.0% and 49.6%.One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose.Graphical

Use of ultra-low contrast dose CT aortography for the management of aortic aneurysmal disease

PurposeComputed tomography aortography (CTA) is used in the assessment of aortic pathologies and planning of surgical intervention. However, its dependence on iodinated contrast can result in development of contrast-induced acute kidney injury (CI-AKI). The potential concern of CI-AKI has spurred research into the potential of administration of low contrast volumes in CTA investigations while maintaining overall diagnostic appeal. Several studies have shown that CTA using contrast volumes as low as 30 mL (equivalent to 10.5 g of iodine) can still yield scans of diagnostic quality. We present a retrospective pilot study to evaluate the feasibility of utilising an ultra-low volume of iodinated intravenous contrast in a population of patients with severe renal insufficiency with referral from our vascular surgery unit for CTA evaluation of the thoracic and abdominal aorta.MethodsThis retrospective pilot study examined 12 CTA scans performed with 20 mLs of iodinated contrast and assessed image quality with both quantitative and qualitative markers. All scans were performed on a Siemens SOMATOM Force dual-source CT scanner. Quantitative assessment values were measured via attenuation values at eight aortoiliac locations and used to calculate a signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at each location. Qualitative analysis of image quality and viability for surgical intervention was obtained from subjective clinical assessment by an interventional radiologist and vascular surgeon.ResultsObtained quantitative assessment values included mean attenuation 189.9 HU, mean SNR 9.6 and mean CNR 8.0. All 12 scans demonstrated individual mean SNR values above predetermined quality thresholds while only five scans produced individual mean CNR values above threshold. Eleven of 12 scans were determined to be of sufficient quality for diagnosis and planning of surgical intervention.ConclusionsOur results suggest that CTA utilising an ultra-low contrast dose of 20 mLs (6 g iodine) yields scans of diagnostic quality for therapeutic decision-making in vascular surgical intervention.

Green Synthesis of Silver Nanoparticles Using &lt;i&gt;Citrus sinensis&lt;/i&gt; Peels Assisted by Microwave Irradiation as a Contrast Agents for Computed Tomography Imaging

Contrast agents are extensively used to enhance the quality of images in computed tomography (CT) scans for brain exams, vascular imaging, and full-body imaging. Recent data indicate that iodine-based contrast agents have brief durations of blood circulation and may lead to harmful toxicity effects. This study aims to produce silver nanoparticles using environmentally friendly synthesis techniques facilitated by microwaves. The characteristics of nanoparticles have been studied using UV-vis, FTIR, XRD, and TEM techniques. The TEM analysis reveals that the silver nanoparticle has an average diameter of 9 nm and exhibits a spherical shape. The contrast-to-noise ratio (CNR) values of silver nanoparticles at 100, 150, and 200 mg/L concentrations are 35.79, 48.16, and 74.66, respectively. In comparison, iodine exhibits CNR values of 28.57, 34.69, and 48.56 at the same concentrations. The CNR values for tube currents of 140, 160, and 180 mA are 37.83, 44.98, and 48.26, respectively. In contrast, the CNR values in silver nanoparticles are 63.64, 75.32, and 81.67. The results obtained from the different concentrations and tube currents clearly demonstrate that silver nanoparticles have a higher CNR than iopamidol. Hence, silver nanoparticles have significant potential as contrast agents.

Online Tree-Structure-Constrained RPCA for Background Subtraction of X-ray Coronary Angiography Images

Background and objectiveBackground subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging. MethodsThe current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames. ResultsThe proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure. ConclusionsThe proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions.

In Vivo visualization of white matter fiber tracts in the brainstem using low flip angle double echo 3D gradient echo imaging at 3T

BackgroundWhite matter (WM) fiber tracts in the brainstem communicate with various regions in the cerebrum, cerebellum, and spinal cord. Clinically, small lesions, malformations, or histopathological changes in the brainstem can cause severe neurological disorders. A direct and non-invasive assessment approach could bring valuable information about the intricate anatomical variations of the white matter fiber tracts and nuclei. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. Both high field MRI methods and ultrahigh-field MRI methods at 7T and 11.7 T have been used to enhance the contrast of WM fiber tracts. Despite their promising results, it is still challenging to achieve wide clinical adoption at 3T. In this study, we explored a clinically feasible method using a proton density weighted (PDW) 3D gradient echo (GRE) sequence to directly image the WM fiber tracts in the brainstem at 3T in vivo. MethodsWe optimized a 3D high resolution, double echo, short TR, PDW GRE sequence on 5 healthy volunteers using a clinical 3T scanner to visualize the complicated anatomy of WM fiber tracts in the brain stem. Tissue properties including T1, proton density and T2* from in vivo quantitative MRI data were used for simulations to determine the optimal flip angle for the sequence. The visualization of multiple WM fiber tracts in the brainstem was assessed qualitatively and quantitatively using relative contrast and contrast-to-noise ratio (CNR). To improve the CNR, the final images were created by averaging over all echoes from two consecutive scans at the optimal flip angle. The results were compared to anatomical atlases and histology sections to identify the major fiber tracts. All the identified major fiber tracts were labeled on axial, sagittal and coronal slices. ResultsThe WM fiber tracts were found to have distinct hypointense signal throughout the brainstem and most of the major WM fiber tracts, such as the corticospinal tract, medial lemniscus, medial longitudinal fasciculus, and central tegmental tract, in the brainstem up to and including the thalamus were identified in all subjects. Both qualitative and quantitative evaluations showed that the 3° scan offered the best contrast for WM fiber tracts for a TR of 20 ms. The average over the first two echo times and two consecutive 3° scans gave a CNR of 47.8 ± 6.2 for the pyramidal tracts in particular and CNRs values greater than 6.5 ± 2.4 for the rest of the fiber tracts. ConclusionsAll the major fiber tracts in the brainstem could be visualized. Given the reasonably short scan time of 10 min at 3T, double echo PDW GRE sequence is a very practical approach for clinical adoption.

Carotid artery assessment in dual-source photon-counting CT: impact of low-energy virtual monoenergetic imaging on image quality, vascular contrast and diagnostic assessability.

Preliminary dual-energy CT studies have shown that low-energy virtual monoenergetic (VMI) + reconstructions can provide superior image quality compared to standard 120kV CTA series. The purpose of this study is to evaluate the impact of low-energy VMI reconstructions on quantitative and qualitative image quality, vascular contrast, and diagnostic assessability of the carotid artery in patients undergoing photon-counting CTA examinations. A total of 122 patients (67 male) who had undergone dual-source photon-counting CTA scans of the carotid artery were retrospectively analyzed in this study. Standard 120kV CT images and low-keV VMI series from 40 to 100keV with an interval of 15keV were reconstructed. Quantitative analyses included the evaluation of vascular CT numbers, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). CT number measurements were performed in the common, external, and internal carotid arteries. Qualitative analyses were performed by three board-certified radiologists independently using five-point scales to evaluate image quality, vascular contrast, and diagnostic assessability of the carotid artery. Mean attenuation, CNR and SNR values were highest in 40keV VMI reconstructions (HU, 1362.32 ± 457.81; CNR, 33.19 ± 12.86; SNR, 34.37 ± 12.89) followed by 55-keV VMI reconstructions (HU, 736.94 ± 150.09; CNR, 24.49 ± 7.11; SNR, 26.25 ± 7.34); all three mean values at these keV levels were significantly higher compared with the remaining VMI series and standard 120kV CT series (HU, 154.43 ± 23.69; CNR, 16.34 ± 5.47; SNR, 24.44 ± 7.14) (p < 0.0001). The qualitative analysis showed the highest rating scores for 55keV VMI reconstructions followed by 40keV and 70keV VMI series with a significant difference compared to standard 120kV CT images series regarding image quality, vascular contrast, and diagnostic assessability of the carotid artery (all comparisons, p < 0.01). Low-keV VMI reconstructions at a level of 40-55keV significantly improve image quality, vascular contrast, and the diagnostic assessability of the carotid artery compared with standard CT series in photon-counting CTA.

Ultrashort-Echo-Time MRI of the Disco-Vertebral Junction: Modulation of Image Contrast via Echo Subtraction and Echo Times.

The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

Comparison of virtual and true non-contrast images from dual-layer spectral detector computed tomography (CT) in patients with colorectal cancer.

Colorectal cancer (CRC) is commonly assessed by computed tomography (CT), but the associated radiation exposure is a major concern. This study aimed to quantitatively and qualitatively compare the image quality of virtual non-contrast (VNC) images reconstructed from arterial and portal venous phases with that of true non-contrast (TNC) images in patients with CRC to assess the potential of TNC images to replace VNC images, thereby reducing the radiation dose. A total of 69 patients with postoperative pathologically confirmed CRC at the West China Hospital of Sichuan University between May 2022 and April 2023 were enrolled in this cross-sectional study. The CT protocol included the acquisition of TNC images, arterial and portal venous phase images; the VNC images were reconstructed from the two postcontrast phase images. Several parameters, including the CT attenuation value, absolute attenuation error, imaging noise [standard deviation (SD)], signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured in multiple abdominal structures for both the TNC and VNC images. Two blinded readers assessed the subjective image quality using a five-point scale. Interobserver agreement was evaluated using interclass correlation coefficients (ICCs). The paired t-test and Wilcoxon signed-rank test were used to compare the objective and subjective results between the TNC and VNC images. Individual measurements of radiation doses for the TNC scan and contrast scan protocols were recorded. A total of 2,070 regions of interest (ROIs) of the 69 patients were analyzed. Overall, the VNC images exhibited significantly lower attenuation values and SD values than the TNC images in all tissues, except for the abdominal aorta, portal vein, and spleen. The mean absolute attenuation errors between the VNC and TNC images were all less than 10 Hounsfield units (HU). The percentages of absolute attenuation errors less than 5 and 10 HU in the VNC images from the arterial phase (VNCa) were 78.99% and 97.97%, respectively, while those from the portal venous phase (VNCp) were 81.59% and 96.96%, respectively. The absolute attenuation errors between the TNC and VNCa images were smaller than those between the TNC and VNCp images for tumors [VNCaerror: 2.77, interquartile range (IQR) 1.77-4.22; VNCperror: 3.27, IQR 2.68-4.30; P=0.002]. The SNR values and CNR values in the VNC images were significantly higher than those in the TNC images for all tissues, except for the portal vein and spleen. The image quality was rated as excellent (represented by a score of 5) in the majority of the TNC and VNC images; however, the VNC images scored lower than the TNC images. Eliminating the TNC phase resulted in a reduction of approximately 37.99% in the effective dose (ED). The VNC images provided accurate CT attenuation, good image quality, and lower radiation doses than the TNC images in CRC, and the VNCa images showed minimal differences in the CT attenuation of the tumors.

Comparative evaluation of gold nanoparticles as contrast agent in multimodality diagnostic imaging

A contrast agent is clinically employed to aid the visualization of organs and structures when imaged by radiation based diagnostic imaging modalities. Iodinated contrast agents have been used for decades, but they carry a heightened risk of adverse allergy reactions and contrast-induced nephropathy. This study investigated the contrast enhancement characteristics of gold nanoparticles (AuNPs) through diverse cancer diagnostic imaging modalities as a potential alternative to the iodine-based contrasts. Solutions of 1.9 nm and 15 nm AuNPs, ranging from 3 to 20 mM concentrations, were prepared, alongside a low osmolar non-ionic iodine contrast agent as a control. All samples were scanned and imaged using x-ray computed tomography scanner (CT-Scan), digital mammography, digital radiography, and fluoroscopy modalities. The contrast-to-noise ratio (CNR) was measured to study the relationship between the size, the concentrations, and the influence of the tube voltage over the image contrast enhancement efficacy of the agents. A lower concentration of AuNPs produced lower CNR values. In addition, the 1.9 nm size of AuNPs caused lower CNR than the 15 nm AuNPs when tested with the highest concentration of 20 mM. High CT's HU values for both sizes of AuNPs indicated substantial contrast enhancement compare to iodine. Although the 15 nm AuNPs might set off higher CNR, a better quality of image contrast enhancement was also observed compared to the 1.9 nm AuNPs. The findings also suggested that the contrast enhancement by AuNPs is highly dependent on the modalities' type and x-rays energy. In conclusion, AuNPs could be applied as a contrast agent in various diagnostic imaging modalities representing promising approach in cancer detection particularly in cases of patients having adverse reactions towards the iodine based contrast agents.

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.

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.

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.

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR)showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight≥90 kg) yielded similar findings to the overall study population.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction

PurposeTo evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). MethodsThis retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. ResultsIn the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). ConclusionModel-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.

Optimization of window settings for coronary arteries assessment using spectral CT-derived virtual monoenergetic imaging

PurposeTo determine the optimal window setting for virtual monoenergetic images (VMI) reconstructed from dual-layer spectral coronary computed tomography angiography (DE-CCTA) datasets.Material and methods50 patients (30 males; mean age 61.1 ± 12.4 years who underwent DE-CCTA from May 2021 to June 2022 for suspected coronary artery disease, were retrospectively included. Image quality assessment was performed on conventional images and VMI reconstructions at 70 and 40 keV. Objective image quality was assessed using contrast-to-noise ratio (CNR). Two independent observers manually identified the best window settings (B-W/L) for VMI 70 and VMI 40 visualization. B-W/L were then normalized with aortic attenuation using linear regression analysis to obtain the optimized W/L (O-W/L) settings. Additionally, subjective image quality was evaluated using a 5-point Likert scale, and vessel diameters were measured to examine any potential impact of different W/L settings.ResultsVMI 40 demonstrated higher CNR values compared to conventional and VMI 70. B-W/L settings identified were 1180/280 HU for VMI 70 and 3290/900 HU for VMI 40. Subsequent linear regression analysis yielded O-W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40. VMI 40 O-W/L received the highest scores for each parameter compared to conventional (all p < 0.0027). Using O-W/L settings for VMI 70 and VMI 40 did not result in significant differences in vessel measurements compared to conventional images.ConclusionOptimization of VMI requires adjustments in W/L settings. Our results recommend W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40.

Task-based assessment for radiation dose optimization in CT abdominal examinations: A phantom study

Recent advancements in Computed Tomography (CT) imaging technology have significantly enhanced healthcare delivery. However, public concern regarding the safety and health risks associated with increased exposure to ionizing radiation has escalated. In response, this study performs a quantitative analysis comparing the image quality of the standard CT abdomen protocol (P1 protocols) with alternative optimization techniques. Adjustments to standard CT scan parameters, including tube potential (P2 protocols), effective mAs value (P3), and pitch factor (P4, P5, P6 protocols), were made to evaluate the effectiveness of these optimization strategies. A 64-slice CT scanner (Canon, Japan) and a polymethylmethacrylate (PMMA) textural water phantom was utilized in this study. CT dose descriptors, volume weighted CT Dose Index (CTDIvol) and dose-length product (DLP) alongside evaluation parameters such as Hounsfield Unit (HU) number, noise levels, Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR) were measured and analysed. Furthermore, this research introduced a Figure of Merit (FOM) based on the ratio of image quality parameters (SNR and CNR) to the CTDIvol, establishing a quantitative measure for optimization. A notable reduction in radiation dose—34.85% below the standard protocol (P1)—was observed in protocol P6, attributed to an increased pitch factor. Conversely, increase the effective mAs value has improved SNR and CNR values. The findings reveal that protocols P1 and P2 deliver the highest FOM for soft tissue and fat imaging, respectively. Protocol P1, with its higher attenuation factor, is optimal for abdominal examinations targeting soft tissues. Meanwhile, protocol P2's increase effective mAs value enhances beam intensity, improving CT numbers for fat tissues with their high-density composition. The study also found a strong correlation between FOM based on SNR and CNR values. In conclusion, adjustments to primary CT parameters significantly influence image quality. The FOM serves as an effective tool for measuring the overall performance of image quality, facilitating post-optimization assessment and contributing to the development of safer, more efficient CT imaging protocols.