Subtraction quality assessment of hepatic dynamic contrast enhanced MRI by using non-rigid 3D-registration for subtraction technique

ObjectivesThis study was aimed at comparing the mechanical parameters of ceramics made using the addition and subtraction technique. MethodsA search was performed on four search engines on 5th April 2023. Quality assessment was performed using study type-specific scales. Where possible, a meta-analysis was performed. SourcesData were extracted from four search engines: PubMed, PubMed Central, Embase, Web of Science, Scopus. Study selectionThe search strategy identified 686 potential articles. 19 papers were subject to qualitative analysis, and data from 11 papers were meta-analysed. The included studies were of high or medium quality. All included papers were in-vitro studies. No clinical trials were found in the literature. SignificanceCeramics made in the additive technology in terms of mechanical parameters can compete with ceramics made in the milling technology. There are no clinical studies yet that would indicate the use of this type of material for permanent restorations in patients. Studies presented in the literature vary greatly in terms of study design and reporting of results. The research did not receive external funding.

We have applied image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced (DCE) MRI data. This approach consists of 3D non-rigid image registration of the kidneys and fuzzy C-mean classification of kidney tissues. The proposed registration method reduced motion artifacts in the dynamic images and improved the analysis of kidney compartments (cortex, medulla, and cavities). The dynamic intensity curves show the successive transition of the contrast agent through kidney compartments. The proposed method for motion correction and kidney compartment classification may be used to improve the validity and usefulness of further model-based pharmacokinetic analysis of kidney function.

BackgroundThe diagnostic performance of dynamic susceptibility contrast (DSC) MR perfusion in discriminating treatment‐related changes from recurrence in irradiated brain metastases is currently not completely clear.PurposeTo systematically review the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy.Study TypeSystematic review and meta‐analysis.SubjectsMEDLINE and Embase were searched for original studies investigating the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy. Ten studies, comprising a total of more than 271 metastases, were included.Field Strength/Sequence1.5T or 3.0T, DSC MR perfusion.AssessmentQuality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies‐2 tool.Statistical TestsSensitivity and specificity were pooled with a bivariate random‐effects model. Heterogeneity was assessed by a chi‐squared test. Potential sources for heterogeneity were explored by subgroup analyses.ResultsIn seven studies the diagnostic criterion was not prespecified. In eight studies it was unclear whether the reference standard was interpreted blindly. In seven studies it was unclear whether DSC MR perfusion results influenced which reference standard was used. Pooled sensitivity and specificity were 81.6% (95% confidence interval [CI]: 70.6%, 89.1%) and 80.6% (95% CI: 64.2%, 90.6%), respectively. There was significant heterogeneity in both sensitivity (P = 0.005) and specificity (P < 0.001). There were no significant differences in relative diagnostic odds ratio according to publication year, country of origin, study size, and DSC MR perfusion interpretation method (visual analysis of cerebral blood volume [CBV] map vs. relative CBV measurement) (P > 0.2). Due to insufficiently detailed reporting, it was not possible to investigate the influence of primary tumor origin on accuracy.Data ConclusionOur results suggest that the accuracy of DSC MR perfusion in diagnosing recurrent brain metastases after radiotherapy is fairly high. However, these findings should be interpreted with caution because of methodological quality concerns and heterogeneity between studies. Level of Evidence: 3 Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2020;51:524–534.

The aim of this work was to assess HCC cases after trans-arterial chemoembolization by subtraction dynamic contrast enhanced MRI to detect its accuracy, sensitivity and specifity in detecting residual tumor and assess the need for further treatment. Patients and methods: This was a retrospective comparative study will be conducted on 35 patients hepatocellular carcinoma (HCC); to assess HCC cases after trans-arterial chemoembolization (TACE) by subtraction dynamic contrast enhanced MRI to detect its accuracy, sensitivity and specificity in detecting residual tumor and assess the need for further treatment. We found that; the mean age of all patients was (62.31 ± 7.14) years. Regarding gender of the patients, the majority (88.6%) of patients were males; while only (11.4%) were females. Regarding residence, the majority (77.1%) of patients live in rural areas, while only (22.9%) live in urban areas. Comparative study between D-MRI and DS-MRI assessments revealed; highly significant increase in disease detection rate, sensitivity, and NPV in favor of DS-MRI in HCC patients; with highly significant difference (p &amp;lt; 0.01 respectively). Comparative study between D-MRI and DS-MRI assessments revealed; non-significant difference in specificity and PPV in HCC patients; with non-significant difference (p &amp;gt; 0.05). We found a moderate agreement between D-MRI and DS-MRI assessments of reactivity among HCC patients (kappa =0.44). Conclusion: Dynamic MRI is valuable in detecting recurrent lesions however, this value is augmented by the addition of subtraction technique especially in lesions having high signal before administration of contrast medium. So we recommend adding the subtraction technique in the protocol of MRI in the follow up after transarterial chemoembolization as it increases the diagnostic confidence. This may help to facilitate the appropriate clinical management of patients including the need for re-treatment sessions.

Breast magnetic resonance imaging (MRI) has shown great potential as a diagnostic tool and is rapidly becoming part of the standard of care in breast cancer evaluation. Three major technological advancements have been responsible for the increased utilization that has effectively mainstreamed breast MRI into practice. The first advancement is in recent parallel imaging techniques with coil sensitivity encoding that now permit detailed spatial resolution with rapid temporal acquisition. Temporal resolution gained by parallel imaging is critical in determining dynamic contrast enhancement and washout curves that can aid in distinguishing benign from malignant neoplasms. Of critical importance is the application of bilateral breast imaging that permits contralateral comparison and symmetry evaluation between breasts. Higher field strength magnets (greater than or equal to 1.5 T) have made it possible to attain greater signal-to-noise ratios needed for the application of “parallel imaging.” Second, new shimming and fat saturation methods permit alignment of radio-frequency-pulse sequences €œoff resonance€ (spatially and chemically) to the spatial offset position (imaging €œaxis€) of the breast with improved fat saturation. Distinguishing fat from enhanced lesions is a critical challenge in breast MRI. While subtraction techniques without fat saturation have been used in the past as a method to gain time, the technique requires a nontrivial level of patient compliance because a small positional shift may degrade the readability in the €œsubtraction€ images. A misalignment in position between precontrast and postcontrast images that involves nonrigid spatial deformations can drastically limit diagnostic quality. Such deformations are not correctable even with the use of image processing methods due to nonseparable signal overlap. The third and final advancement has been the commercial availability of MRI biopsy devices that allow tissue-based confirmation of suspicious lesions seen only on MRI, particularly as MRI is known for its high degree of sensitivity to neoplastic lesion conspicuity. As a cross-sectional imaging modality, MRI also offers multiplanar capabilities and the ability to perform maximum intensity projections (MIP) that permit the radiologist to manipulate the images for precise tumor localization.

Clinical and physical assessments of image quality are compared and the correlation between the two derived. Clinical assessment has been made by a group of expert radiologists who evaluated the fulfillment of the European Image Criteria for chest and lumbar spine radiography; yielding the so-called Image Criteria Score, ICS. Physical measures of image quality were calculated using a Monte Carlo model of the complete imaging system. This model includes a voxelised male anatomy and calculates contrast and signal-to-noise ratio of various anatomical details and a measure of useful dynamic range. Correlations between the ICS and the physical image quality measures were sought. Four lumbar spine and 16 chest imaging systems were evaluated and simulated with the model. The most useful physical quantities for chest radiography were the dynamic range and contrast of blood vessels in the retro-cardiac area. In lumbar spine, it was the signal-to-noise ratio of trabecular structures. The significant correlation is encouraging and shows that clinical image quality can be predicted provided the imaging conditions are well known and that relevant measures of physical image quality are used to assess the quality of the image.

In order to measure the parameters of consumers' preferred image quality, this research suggests the following image quality assessment factors; dynamic range, color, and contrast. They have both physical image quality factors and psychological characteristics from the previous researches. For measuring the optimum parameter ranges of preferred images, we select 100 images from various websites by contents; portrait, landscape, and nightscape; therefore a total of 300 images are collected based on over 30 recommendations by appreciators. We try to expect the generality from user's recommendations and the diversity of contents from different scenes on the web. We discovered the specific ranges of preferred image quality metrics. Throughout this research, we are able to measure the preferred image quality metrics ranges. Also, we expect the practical and specific dynamic range, color, and contrast information of preferred image quality to positively influence product development.

In order to measure the parameters of consumers` preferred image quality, this research suggests image quality assessment factors; dynamic range, color, and contrast. They have both physical image quality factors and psychological characteristics from the previous researches. We found out the specific ranges of preferred image quality metrics. As a result, Digital Zone System meant for dynamic range generally shows 6~10 stop ranges in portrait, nightscape, and landscape. Total RGB mean values represent in portrait (67.2~215.2), nightscape (46~142), and landscape (52~185). Portrait total RGB averages have the widest range, landscape, and nightscape, respectively. Total scene contrast ranges show in portrait (196~589), nightscape (131~575), and landscape (104~767). Especially in portrait, skin tone RGB mean values are in ZONE V as the exposure standard, but practically image consumers` preferred skin tone level is in ZONE IV. Also, total scene versus main subject contrast ratio represents 1:1.2; therefore, we conclude that image consumers prefer the out-of-focus effect in portrait. Throughout this research, we can measure the preferred image quality metrics ranges. Also, we expect the practical and specific dynamic range, color, and contrast information of preferred image quality to positively influence product development.

Objective:Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR.Methods:1027 signal–time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen’s κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal–time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier.Results:Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89.Conclusion:The reviewers showed good agreement. Machine learning classifiers trained on signal–time course measures and QR can assess quality. Combining multiple measures reduces misclassification.Advances in knowledge:A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Can Clinically Significant Prostate Cancer Be Detected with Multiparametric Magnetic Resonance Imaging? A Systematic Review of the Literature

Diagnosing osteomyelitis in diabetic foot by diffusion-weighted imaging and dynamic contrast material-enhanced magnetic resonance imaging: a systematic review and meta-analysis

BackgroundTumor recurrence and pseudoprogression (PsP) have similar imaging manifestations in conventional magnetic resonance imaging (MRI), although the subsequent treatments are completely different. This study aimed to evaluate the value of perfusion-weighted imaging (PWI) in differentiating PsP from glioma recurrence.MethodsA comprehensive literature search was performed to evaluate clinical studies focused on differentiating recurrent glioma from PsP using PWI, including dynamic susceptibility contrast MRI (DSC-MRI), dynamic contrast enhanced MRI (DCE-MRI), and arterial spin labeling (ASL). Study selection and data extraction were independently completed by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was applied to evaluate the quality of the included studies. The software Stata 16.0 and Meta-Disc 1.4 were used for the meta-analysis. Meta-regression and subgroup analyses were applied to identify the sources of heterogeneity in the studies. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) prior to initiation (CRD42022304404).ResultsA total of 40 studies were included, including 27 English studies and 13 Chinese studies. There were 1,341 patients with glioma recurrence and 876 patients with PsP. The pooled sensitivity and specificity of DSC-MRI for differentiating glioma recurrence from PsP were 0.82 [95% confidence interval (CI): 0.78 to 0.86] and 0.87 (95% CI: 0.80 to 0.92), respectively. The pooled sensitivity and specificity of DCE-MRI were 0.83 (95% CI: 0.76 to 0.89) and 0.83 (95% CI: 0.78 to 0.87), respectively. The pooled sensitivity and specificity of ASL were 0.80 (95% CI: 0.73 to 0.86) and 0.86 (95% CI: 0.76 to 0.92), respectively.DiscussionThe DSC-MRI, DCE-MRI, and ASL perfusion techniques displayed high accuracy in distinguishing glioma recurrence from PsP, and DSC-MRI had a higher diagnostic performance than the other two techniques. However, due to the diversity of the parameters and threshold differences, further investigation and standardization are needed.

Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Picosecond laser ultrasonic imaging detection of near-surface micro defects using PCS and SAFT algorithm.

DSC-MRI examination is one of the best methods of diagnosis for brain diseases. For this purpose, the so-called perfusion parameters are defined, of which the most used are CBF, CBV, and MTT. There are many approaches to determining these parameters, but regardless of the approach, there is a problem with the quality assessment of methods. To solve this problem, this article proposes virtual DSC-MRI brain examination, which consists of two steps. The first step is to create curves that are typical for DSC-MRI studies and characteristic of different brain regions, i.e., the gray and white matter, and blood vessels. Using perfusion descriptors, the curves are classified into three sets, which give us the model curves for each of the three regions. The curves corresponding to the perfusion of different regions of the brain in a suitable arrangement (consistent with human anatomy) form a model of the DSC-MRI examination. In the created model, one knows in advance the values of the complex perfusion parameters, as well as basic perfusion descriptors. The shown model study can be disturbed in a controlled manner—not only by adding noise, but also by determining the location of disturbances that are characteristic of specific brain diseases.