Readout-segmented echo-planar imaging in diffusion-weighted mr imaging in breast cancer: comparison with single-shot echo-planar imaging in image quality

Objective: The purpose of this study was to compare the image quality of standard single-shot echo-planar imaging (ss-EPI) and that of readout-segmented EPI (rs-EPI) in patients with breast cancer. Materials and Methods: Seventy-one patients with 74 breast cancers underwent both ss-EPI and rs-EPI. For qualitative comparison of image quality, three readers independently assessed the two sets of diffusion-weighted (DW) images. To evaluate geometric distortion, a comparison was made between lesion lengths derived from contrast enhanced MR (CE-MR) images and those obtained from the corresponding DW images. For assessment of image parameters, signal-to-noise ratio (SNR), lesion contrast, and contrast-to-noise ratio (CNR) were calculated. Results: The rs-EPI was superior to ss-EPI in most criteria regarding the qualitative image quality. Anatomical structure distinction, delineation of the lesion, ghosting artifact, and overall image quality were significantly better in rs-EPI. Regarding the geometric distortion, lesion length on ss-EPI was significantly different from that of CE-MR, whereas there were no significant differences between CE-MR and rs-EPI. The rs-EPI was superior to ss-EPI in SNR and CNR. Conclusion: Readout-segmented EPI is superior to ss-EPI in the aspect of image quality in DW MR imaging of the breast

Image quality of coronary CT angiography (CCTA) using 640-slice scanner: qualitative and quantitative assessments of coronary arteries visibility

The purpose of this study was to evaluate the image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA) using 640-slice scanner. Advancement of multidetector computed tomography (MDCT) technology with higher spatial, temporal resolution, and increasing detector array have improved the image quality and diagnostic accuracy of CCTA. A total of 25 patients (12 men and 13 women) underwent CCTA was chosen and data was acquired by 640-slice scanner. All 16 segments of coronary arteries were evaluated by two reviewers using a 4-likert scale for qualitative assessment. In quantitative assessment, the evaluation of 4 main coronary arteries were analysed in terms of signal intensity (SI), image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). All 25 patients with a mean age of 52.88 ± 14.75 years old and body mass index (BMI) of 24.24 ± 3.28 kg/m2 were analysed. In qualitative assessment, from the total of 400 segments, 379 segments (95 %) have diagnostic value while 21 segments do not have diagnostic value, which means 5 % artefact was detected. In quantitative assessment, there was no statistical differences in gender, race, and BMI (p>0.05). Overall evaluation showed that higher SI at the left main artery (LM) at 393.7 ± 47.19. Image noise was higher at right coronary artery (RCA) at 39.01 ± 13.97. SNR and CNR showed higher at left anterior descending (LAD) with 12.73 ± 5.17 and LM 9.14 ± 4.2, respectively. In conclusion, this study indicates that 640-slice MDCT has higher diagnostic value in CCTA examination with 95 % vessel visibility with 5 % artefact detection

Deep learning image reconstruction algorithm. impact on image quality in coronary computed tomography angiography

PurposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).Material and methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.ResultsDLIR algorithm did not impact vascular attenuation (P >= 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P <= 0.021).DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P >= 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P <= 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).ConclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD

AQMI: Software for assessing the quality of mammographic images

Objective: AQMI - “Assessment of the quality of mammographic images” was developed to support the quality control (QC) of digital mammographic images. Materials and Methods: The software was implemented in the Python programming language via the Streamlit library, which involved content structuring and environmental planning. The experimental data that were selected from a public domain repository [19]. From the selected database, relevant information that was present in the DICOM file was studied to perform the image quality test. Then, from searching the literature, indicators that measure image quality were found, such as the signal-to-noise ratio, the contrast-to-noise ratio, figure of merit and image histogram. Results: AQMI assists in analyzing the image quality test established in IN 92 by the Agência Nacional de Vigilância Sanitária [8]. It also has quality addition indicators, trend graphs, and the image assessment history. Conclusion: For the functionalities of this work, the developed software is a promising tool for use in clinical practice, since it consists of a free, friendly, and easy-to-use interface

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification

BACKGROUND Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP. METHODS One hundred patients were consecutively enrolled. Image quality-associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1-99 Agatston score (mildly increased risk), Agatston 100-299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk). RESULTS In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%. CONCLUSION DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment. KEY POINTS • In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise. • Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score. • Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance

[Purpose]Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).[Materials and methods]We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwentNM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1+dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for diferentiating PD from non-PD were also compared between NEX1 and NEX1+dDLR.[Results]Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1+dDLR were signifcantly higherthan in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1+dDLR were 0.87 and 0.75, respectively, which had no signifcant diference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.[Conclusion]Image quality for NEX1+dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD

Application of a High‑Resolution Computed Radiography System in Detecting an Iodinated X‑ray Contrast Agent: Small Animal Phantom Study

Tumor angiogenesis, the creation of new blood vessels, is the characteristic of solid tumors and crucial for their development. Iodinated contrast agents are used to increase the X‑ray detectability of zone of angiogenesis, and thus providing a means for tracking tumor growth. The overall objective of this project was to evaluate the performance of Kodak CR 7400, a high‑resolution compact computed radiography (CR) system in detection of Omnipaque‑240, an iodinated contrast agent, in a phantom mimicking small animal tumor model. The first phase of the project was dedicated to a comprehensive assessment of CR image quality by measuring presampled Modulation Transfer Function (MTF), Noise Power Spectrum (NPS), Relative Standard Deviation of Noise (RSD), Noise Equivalent Quanta (NEQ), and Detective Quantum Efficiency (DQE). Next, dual‑energy and temporal subtraction techniques were implemented to enhance the contrast of iodinated regions and suppress soft tissue background in the phantom. The underlying physics of each technique was discussed, including the design of the phantoms, the simulation and measurement of the Signal‑to‑Noise Ratio (SNR) in the final subtracted iodine image, and dose assessment. In the end, the results of both techniques were compared along with discussions about the advantages and limitations of implementing each technique. Overall, the study supported the potential of low‑cost CR 7400 in small animal study, particularly detecting iodinated contrast agents implementing temporal subtraction technique and provided a background for similar small animal studies using a CR system

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses

High-field MR imaging in pediatric congenital heart disease: Initial results

BackgroundComprehensive assessment of pediatric congenital heart disease (CHD) at any field strength mandates evaluation of both vascular and dynamic cardiac anatomy for which diagnostic quality contrast-enhanced magnetic resonance angiography (CEMRA) and cardiac cine are crucial.ObjectiveTo determine whether high-resolution (HR) CEMRA and steady-state free precession (SSFP) cine can be performed reliably at 3.0 T in children with CHD and to compare the image quality to similar techniques performed at 1.5 T.Materials and methodsTwenty-eight patients with a median age of 5 months and average weight 9.0 ± 7.8 kg with suspected or known CHD were evaluated at 3.0 T. SSFP cine (n = 86 series) and HR-CEMRA (n = 414 named vascular segments) were performed and images were scored for image quality and artifacts. The findings were compared to those of 28 patients with CHD of similar weight who were evaluated at 1.5 T.ResultsOverall image quality on HR-CEMRA was rated as excellent or good in 96% (397/414) of vascular segments at 3.0 T (k = 0.49) and in 94% (349/371) of vascular segments at 1.5 T (k = 0.36). Overall image quality of SSFP was rated excellent or good in 91% (78/86) of cine series at 3.0 T (k = 0.55) and in 81% (87/108) at 1.5 T (k = 0.47). Off-resonance artifact was common at both field strengths, varied over the cardiac cycle and was more prevalent at 3.0 T. At 3.0 T, off-resonance dark band artifact on SSFP cine was absent in 3% (3/86), mild in 69% (59/86), moderate in 27% (23/86) and severe in 1% (1/86) of images; at 1.5 T, dark band artifact was absent in 16% (17/108), mild in 69% (75/108), moderate in 12% (13/108) and severe in 3% (3/108) of cine images. The signal-to-noise ratio and contrast-to-noise ratio of both SSFP cine and HR-CEMRA images were significantly higher at 3.0 T than at 1.5 T (P &lt; 0.001).ConclusionSignal-to-noise ratio and contrast-to-noise ratio of high-resolution contrast-enhanced magnetic resonance angiography and SSFP cine were higher at 3.0 T than at 1.5 T. Artifacts on SSFP cine were cardiac phase specific and more prevalent at 3.0 T such that frequency-tuning was required in one-third of exams. In neonates, high spatial resolution CEMRA was highly reliable in defining extracardiac vascular anatomy