Image Quality Of MRI Research Articles

Design and Development of Contrast-Limited Adaptive Histogram Equalization Technique for Enhancing MRI Images by Improving PSNR, UIQI Parameters in Comparison with Median Filtering

Image enhancement is used to improve the quality of images and it enhances, sharpens image features, such as edges, boundaries, and contrast, to make a graphic display useful for display and analysis. In order to enhance the quality of MRI images, histogram-based image enhancement technique is developed in this work. Materials and Methods: In this research, a Contrast Limited Adaptive Histogram Equalization (CLAHE) based image enhancement technique is proposed and developed for MRI images and the proposed work is compared with another image enhancement technique called Median Filtering (MF) method. Input medical images (N=30) of both group were downloaded from standard medical database. The enrollment ratio is obtained as 1 with 95% confidence interval and a threshold value 0.05. Results: The performance of image enhancement is measured using two parameters namely, Peak Signal Noise Ratio (PSNR) and Universal Image Quality Index (UIQI). These parameters are calculated and evaluated to assess the proposed methods efficacy. High values of PSNR and UIQI indicate better enhancement. CLAHE provides mean PSNR values of 18.6968(dB), mean UIQI of 80.9220%, and median filtering method provides mean PSNR values of 14.2261(dB) and mean UIQI of 76.3463%. Conclusion: Based on the experiment's results, the CLAHE image enhancement technique significantly performed better than the MF technique.

Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: amethod to create accurate MRI-enhanced CT datasets.

Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens. Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3Drenderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed. Scanning protocol T1VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing afully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77-0.94 mm). Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations.

Basic Principles of and Practical Guide to Clinical MRI Radiofrequency Coils.

Radiofrequency (RF) coils are an essential MRI component used for transmission of the RF field to excite nuclear spins and for reception of the MRI signal. They play an important role in image quality in terms of signal-to-noise ratio, signal uniformity, and image resolution. However, they are also associated with potential image artifacts and RF heating that may lead to patient burns. Knowledge of the basic principles of RF coils-including coil designs commonly used in clinical MRI and the anatomy of RF receive coils-facilitates understanding of the use and safety issues of RF coils. Selection of suitable RF coils for individual applications and proper use of RF coils in particular MRI techniques such as parallel imaging are needed to achieve optimal image quality, prevent image artifacts, and reduce the risk of RF burns. The ability to correctly identify RF coil problems and distinguish them from other problems with image artifacts resembling those of RF coil problems allows effective handling of the problems and efficient clinical MRI operation. Quality control of RF coils is required to ensure consistent image quality for clinical MRI and avoid coil problems that may affect image diagnostic evaluation or interrupt patient imaging. There are different phantom test methods for RF coil quality control; the appropriate one to use depends on the coil design and MRI system. An invited commentary by Ohliger is available online. Online supplemental material is available for this article. ©RSNA, 2022.

Accelerated Pancreatobiliary MRI for Pancreatic Cancer Surveillance in Patients With Pancreatic Cystic Neoplasms.

Pancreatobiliary MRI is often recommended for patients at risk of developing pancreas cancer. But the surveillance MRI protocol has not yet been widely accepted. To establish an accelerated MRI protocol targeting the table time of 15 minutes for pancreatic cancer surveillance and test its performance in lesion characterization. Prospective. A total of 30 participants were enrolled, who were undergoing follow-up care for intraductal papillary mucinous neoplasms or newly diagnosed pancreatic cysts (≥10 mm) and were scheduled for or had recently undergone contrast-enhanced CT (CECT). A 3 T; heavily T2WI, 3D MRCP, DWI, dynamic T1WI, two-point Dixon. In-room time and table time were measured. Seven radiologists independently reviewed image quality of MRI and then the presence of high-risk stigmata and worrisome features in addition to diagnostic confidence for accelerated MRI, CECT, and the noncontrast part of accelerated MRI (NC-MRI). Fisher's exact test was used for categorical variables and either the Student's t-test or Mann-Whitney test was performed for continuous variables. The generalized estimated equation was used to compare the diagnostic performance of examinations on a per-patient basis. Interobserver agreement was evaluated via Fleiss kappa. A P value of <0.05 was considered to be statistically significant. The in-room time was 18.5 ± 2.6minutes (range: 13.7-24.9) and the table time was 13.9 ± 1.9minutes (range: 10.7-17.5). There was no significant difference between the diagnostic performances of the three examinations (pooled sensitivity: 75% for accelerated MRI and CECT, 68% for NC-MRI, P=0.95), with the highest significant diagnostic confidence for accelerated MRI (4.2 ± 0.1). With accelerated MRI, the interobserver agreement was fair to excellent for high-risk stigmata (κ=0.34-0.98). Accelerated MRI protocol affords a table time of 15 minutes, making it potentially suitable for cancer surveillance in patients at risk of developing pancreatic cancer. 2 TECHNICAL EFFICACY STAGE: 2.

Prostate MRI using a rigid two-channel phased-array endorectal coil: comparison with phased array coil acquisition at 3\u2009T

BackgroundTo compare image quality, lesion detection and patient comfort of 3T prostate MRI using a combined rigid two-channel phased-array endorectal coil and an external phased-array coil (ERC-PAC) compared to external PAC acquisition in the same patients.MethodsThirty three men (mean age 65.3y) with suspected (n = 15) or biopsy-proven prostate cancer (PCa, n = 18) were prospectively enrolled in this exploratory study. 3T prostate MRI including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) was performed using an ERC-PAC versus PAC alone, in random order. Image quality, lesion detection and characterization (biparametric PI-RADSv2.1) were evaluated by 2 independent observers. Estimated signal-to-noise ratio (eSNR) was measured in identified lesions and the peripheral zone (PZ). Patient comfort was assessed using a questionnaire. Data were compared between sequences and acquisitions. Inter/intra-observer agreement for PI-RADS scores was evaluated.ResultsTwenty four prostate lesions (22 PCa) were identified in 20/33 men. Superior image quality was found for ERC-PAC compared to PAC for T2WI for one observer (Obs.1, p < 0.03) and high b-value DWI for both observers (p < 0.05). The sensitivity of PI-RADS for lesion detection for ERC-PAC and PAC acquisitions was 79.2 and 75% for Obs.1, and 79.1 and 66.7%, for Obs.2, without significant difference for each observer (McNemar p-values ≥0.08). Inter−/intra-observer agreement for PI-RADS scores was moderate-to-substantial (kappa = 0.52–0.84). Higher eSNR was observed for lesions and PZ for T2WI and PZ for DWI using ERC-PAC (p < 0.013). Most patients (21/33) reported discomfort at ERC insertion.ConclusionDespite improved image quality and eSNR using the rigid ERC-PAC combination, no significant improvement in lesion detection was observed, therefore not supporting the routine use of ERC for prostate MRI.

Non-rigid motion-corrected free-breathing 3D myocardial Dixon LGE imaging in a clinical setting

ObjectivesTo investigate the efficacy of an in-line non-rigid motion-compensated reconstruction (NRC) in an image-navigated high-resolution three-dimensional late gadolinium enhancement (LGE) sequence with Dixon water–fat separation, in a clinical setting.MethodsForty-seven consecutive patients were enrolled prospectively and examined with 1.5 T MRI. NRC reconstructions were compared to translational motion-compensated reconstructions (TC) of the same datasets in overall and different sub-category image quality scores, diagnostic confidence, contrast ratios, LGE pattern, and semiautomatic LGE quantification.ResultsNRC outperformed TC in all image quality scores (p < 0.001 to 0.016; e.g., overall image quality 5/5 points vs. 4/5). Overall image quality was downgraded in only 23% of NRC datasets vs. 53% of TC datasets due to residual respiratory motion. In both reconstructions, LGE was rated as ischemic in 11 patients and non-ischemic in 10 patients, while it was absent in 26 patients. NRC delivered significantly higher LGE-to-myocardium and blood-to-myocardium contrast ratios (median 6.33 vs. 5.96, p < 0.001 and 4.88 vs. 4.66, p < 0.001, respectively). Automatically detected LGE mass was significantly lower in the NRC reconstruction (p < 0.001). Diagnostic confidence was identical in all cases, with high confidence in 89% and probable in 11% datasets for both reconstructions. No case was rated as inconclusive.ConclusionsThe in-line implementation of a non-rigid motion-compensated reconstruction framework improved image quality in image-navigated free-breathing, isotropic high-resolution 3D LGE imaging with undersampled spiral-like Cartesian sampling and Dixon water–fat separation compared to translational motion correction of the same datasets. The sharper depictions of LGE may lead to more accurate measures of LGE mass.Key Points• 3D LGE imaging provides high-resolution detection of myocardial scarring.• Non-rigid motion correction provides better image quality in cardiac MRI.• Non-rigid motion correction may lead to more accurate measures of LGE mass.

Bowel preparation in MRI for detection of endometriosis: Comparison of the effect of an enema, no additional medication and intravenous butylscopolamine on image quality

PurposeTo compare the effect of three different patient preparation strategies for reducing bowel motion on image quality in pelvic MRI. MethodsRetrospective study in which 95 consecutive patients undergoing pelvic MRI were subdivided based on preparation type for reduction of bowel motion: group 1 (N = 31) fasted 4 h and applied an enema (Bisacodyl 10 mg); group 2 (N = 32) received no medication; group 3 (N = 32) received intravenous butylscopolamine (Buscopan® 50 mg). Image quality was reviewed by visual assessment of delineation (3-point-scale) of pelvic structures: uterus, adnexa, bladder, rectum, sigmoid, uterosacral ligaments, round ligaments and small bowel. As secondary endpoint the presence of rectal wall edema was evaluated. Interobserver agreement was calculated, as well as relative diagnostic odds ratios (RDOR) for the protocols to provide an outcome in the best delineation category. ResultsInterobserver agreement proportions varied from 0.48 to 1.00. The rectum and sigmoid colon respectively have a 5.4 and 2.6 RDOR when butylscopolamine is applied compared to Bisacodyl (P = 0.051; P = 0.008), and a 4.2 and 5.7 times higher RDOR with Bisacodyl preparation compared to no medical preparation (P = 0.006; P < 0.01). Small bowel delineation was significantly better with butylscopolamine compared to Bisacodyl (P = 0.007). There was no significant difference in delineation of the other structures between protocols. There is a significant higher chance of observing rectal wall edema with Bisacodyl compared to the other protocols (both P < 0.001). ConclusionsButylscopolamine provides better delineation of the small bowel and rectosigmoid compared to Bisacodyl, which in turn, provides better delineation of the rectosigmoid compared to no medication. Moreover, Bisacodyl causes rectal wall edema in the majority of cases.

Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging.

Background Deep learning reconstruction (DLR) may improve image quality. However, its impact on diffusion-weighted imaging (DWI) of the prostate has yet to be assessed. Purpose To determine whether DLR can improve image quality of diffusion-weighted MRI at b values ranging from 1000 sec/mm2 to 5000 sec/mm2 in patients with prostate cancer. Materials and Methods In this retrospective study, images of the prostate obtained at DWI with a b value of 0 sec/mm2, DWI with a b value of 1000 sec/mm2 (DWI1000), DWI with a b value of 3000 sec/mm2 (DWI3000), and DWI with a b value of 5000 sec/mm2 (DWI5000) from consecutive patients with biopsy-proven cancer from January to June 2020 were reconstructed with and without DLR. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) from region-of-interest analysis and qualitatively assessed using a five-point visual scoring system (1 [very poor] to 5 [excellent]) for each high-b-value DWI sequence with and without DLR. The SNR, CNR, and visual score for DWI with and without DLR were compared with the paired t test and the Wilcoxon signed rank test with Bonferroni correction, respectively. Apparent diffusion coefficients (ADCs) from DWI with and without DLR were also compared with the paired t test with Bonferroni correction. Results A total of 60 patients (mean age, 67 years; age range, 49-79 years) were analyzed. DWI with DLR showed significantly higher SNRs and CNRs than DWI without DLR (P < .001); for example, with DWI1000 the mean SNR was 38.7 ± 0.6 versus 17.8 ± 0.6, respectively (P < .001), and the mean CNR was 18.4 ± 5.6 versus 7.4 ± 5.6, respectively (P < .001). DWI with DLR also demonstrated higher qualitative image quality than DWI without DLR (mean score: 4.8 ± 0.4 vs 4.0 ± 0.7, respectively, with DWI1000 [P = .001], 3.8 ± 0.7 vs 3.0 ± 0.8 with DWI3000 [P = .002], and 3.1 ± 0.8 vs 2.0 ± 0.9 with DWI5000 [P < .001]). ADCs derived with and without DLR did not differ substantially (P > .99). Conclusion Deep learning reconstruction improves the image quality of diffusion-weighted MRI scans of prostate cancer with no impact on apparent diffusion coefficient quantitation with a 3.0-T MRI system. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data

To test whether deep learning can be used to effectively reduce artifacts in MR images of the brain. In this study, a large set of images with and without motion artifacts is needed for training. It is difficult to collect training data from clinical images because it requires a lot of effort and time. We have created motion artifact images of the brain by computer simulation. As an experimental study, we obtained original images for deep learning from 20 volunteers. These original images were used to create various images of different artifacts by computer simulation and these were used the input images for deep learning. The same method was used to create test images and these images were used to compare the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the input images and output images using the three denoising methods. The network models used were U-shaped fully convolutional network (U-Net), denoising convolutional neural network (DnCNN) and wide inference network and 5 layers Residual learning and batch normalization (Win5RB). U-Net was the most effective model for reducing motion artifacts. The SSIM and PSNR were 0.978 and 32.5 dB. This is an effective method to reduce artifacts without degrading the image quality of brain MRI images.

Single point imaging with radial acquisition and compressed sensing.

To accelerate the Pointwise Encoding Time Reduction with Radial Acquisition(PETRA) sequence using compressed sensing while preserving the image quality for high-resolution MRI of tissue with ultra-short values. Compressed sensing was introduced in the PETRA sequence(csPETRA) to accelerate the time-consuming single point acquisition of the k-space center data. Random undersampling was applied to achieve acceleration factors up to Acc=32. Phantom and in vivo images of the knee joint of six volunteers were measured at 3T using csPETRA sequence with Acc=4, 8, 12, 16, 24, and 32. Images were compared against fully sampled PETRA data(Acc=1) for structural similarity and normalized-mean-square-error. Qualitative and semi-quantitative analyses were performed to assess the effect of the acceleration on image artifacts, image quality, and delineation of anatomical structures at the knee. Even at high acceleration factors of Acc=16 no aliasing artifacts were observed, and the anatomical details were preserved compared with the fully sampled data. The normalized-mean-square-error was less than 1% for Acc=16, in which single point imaging acquisition time was reduced from 165 to 10s, reducing the total scan time from 7.8 to 5.2min. Semi-quantitative analyses suggest that Acc=16 yields comparable diagnostic quality as the fully sampled data for knee imaging at a scan time of 5.2min. csPETRA allows for ultra-short imaging of the knee joint in clinically acceptable scan times while maintaining the image quality of original non-accelerated PETRA sequence.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values. Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value. The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB. In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB). Numerical results show that the algorithm can be extended to normal images and medical images.

Spine Medical Image Segmentation Based on Deep Learning.

The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened (P < 0.05). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm (P < 0.05). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

Retrospective Assessment of the Impact of Primary Language Video Instructions on Image Quality of Abdominal MRI.

To assess the impact of instructional videos in patients' primary language on abdominal MR image quality for whom English is a second language (ESL). Twenty-nine ESL patients viewed Spanish or Mandarin Chinese instructional videos (approximately 2.5 min in duration) in the preparation room before abdominal MRI (ESL-video group). Comparison groups included 50 ESL patients who underwent MRI before video implementation (ESL-no video group) and 81 English-speaking patients who were matched for age, sex, magnet strength, and history of prior MRI with patients in the first two groups. Three radiologists independently assessed respiratory motion and image quality on turbo spin-echo T2-weighted images (T2WI) and postcontrast T1-weighted images (T1WI) using 1 to 5 Likert scales. Groups were compared using Kruskal-Wallis tests as well as generalized estimating equations (GEEs) to adjust for possible confounders. For T2WI respiratory motion and T2WI overall image quality, Likert scores of the ESL-no video group (mean score across readers of 2.6 ± 0.1 and 2.6 ± 0.1) were lower (all P < .001) compared with English-speaking (3.3 ± 0.2 and 3.3 ± 0.1) and ESL-video (3.2 ± 0.1 and 3.0 ± 0.2) groups. In the GEE model, mean T2WI respiratory motion (both adjusted P < .001) and T2WI overall quality (adjusted P= .03 and .11) were higher in English and ESL-video groups compared with ESL-no video group. For T1WI respiratory motion and T1WI overall image quality, Likert scores were not different between groups (P > .05), including in the GEE model (adjusted P > .05). Providing ESL patients with an instructional video in their primary language before abdominal MRI is an effective intervention to improve imaging quality.

GAN‐segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation

AbstractIn this study, we present a novel automatic segmentation method using a neural network model named GAN‐segNet, which can not only identify brain tumors from MRI images but also accurately delineate intratumor regions. Since brain tumors with varying shapes and sizes can appear anywhere in the brain and image quality and contrast of MRI could be inadequate, automatic segmentation remains challenging despite its importance in the clinical workflow. The proposed GAN‐segNet is an innovative modification of the Generative Adversarial Network (GAN) and can efficiently and accurately segment brain tumors. One key innovation of our GAN model is an autoencoder learning representation of input data that were added to the generative network of the above‐mentioned GAN. By doing so, information extracted through convolution operations can be meaningfully regularized. As a result, the scales of extracted features can be controlled by the added autoencoder to preserve detail. Additionally, we propose an innovative loss function based on the concept of focal loss to effectively mitigate the impact of label imbalance. The above‐mentioned combination enables the proposed GAN‐segNet model to improve the segmentation of small intratumor region(s). We demonstrate the proposed method using MRI data available from a public database, that is, Brain Tumor Segmentation Challenge 2018 database (BRATS 2018). Using the proposed GAN‐segNet model, the average Dice scores were 0.8280, 0.9022, and 0.814 for segmenting enhancing tumor core, whole tumor, and tumor core, respectively. Furthermore, positive predictive values for segmenting enhanced tumor core, whole tumor, and tumor core were 0.8496, 0.9270, and 0.8610, respectively.

Deep learning-accelerated T2-weighted imaging of the prostate: Impact of further acceleration with lower spatial resolution on image quality

PurposeTo compare image quality in prostate MRI among standard T2-weighted imaging (T2-std), accelerated T2-weighted imaging (T2WI) with high resolution (T2-HR) and more accelerated T2WI with lower resolution (T2-LR) using both conventional reconstruction (C) and deep learning reconstruction (DL). Materials and methodsIn 46 consecutive patients, T2-std, T2-HR and T2-LR were acquired in 3:32 min, 1:06 min and 0.52 min, respectively. Both reconstruction techniques (C and DL) were applied to T2-HR and T2-LR. Five sets of images (T2-std, T2-HRC, T2-LRC, T2-HRDL, and T2-LRDL) for each patient were independently evaluated by two radiologists. Quantitative analysis including the signal-to-noise ratio (SNR) and contrast ratio (CR) and qualitative analysis with a 5-point scale for the sharpness of structures, ghosting or other artifacts, noise and overall image quality were performed. ResultsThe SNR was not different in either the peripheral zone (PZ) or transition zone (TZ) between T2-LRDL and T2-std with the median value of 21.7 versus 22.6 in PZ and 16.5 versus 17.3 in TZ, respectively. The CR between the prostate gland and muscle was significantly lower on T2-HRC and T2-LRC than on T2-std. Most of the evaluated factors showed significantly lower scores on T2-HRC and T2-LRC than on T2-std. Although noise and overall image quality on T2-HRDL and other artifacts on T2-LRDL were rated significantly lower than on T2-std (median value 4.0 versus 4.5, P < 0.001; 4.5 versus 5.0, P = 0.001; 4.5 versus 5.0, P = 0.006, respectively), other factors did not differ between T2-std and T2-HRDL or T2-LRDL. ConclusionDL is useful to improve image quality in accelerated T2WI of the prostate gland. Using DL, accelerated T2WI with lower spatial resolution than T2-std can be achieved with similar image quality in much shorter scan time (75.5% reduction in the acquisition time).

Comparison and prediction of artefact severity due to total hip replacement in 1.5 T versus 3 T MRI of the prostate

PurposeTo evaluate image quality and diagnostic value of multiparametric prostate MRI (mpMRI) in patients with total hip replacement (THR) at 1.5 and 3 Tesla. MethodsIn this retrospective multicenter cohort study patients with uni- or bilateral THR and 1.5 T or 3 T mpMRI were included. Seventy consecutive, standard-of-care examinations per field strength were evaluated regarding their diagnostic value. The overall diagnostic value and prostate imaging quality score (PI-QUAL) were assessed. Artifact severity in the localizer and mpMRI sequences (T2w, DWI, DCE) was scored on a 3-point scale. Correlation between diagnostic value and artifacts was analysed. Moreover, a subgroup analysis focussed on image quality at different 3 T scanner generations. Results140 consecutive patients (mean age 72, median PSA value 8.3 ng/ml) were included. When comparing 1.5 T to 3 T examinations, no significant differences were observed regarding the artifact severity of DWI and the localizer and the overall diagnostic value of the images. There was a strong correlation between the diagnostic value, PI-QUAL score, and artifact severity in the localizer and DWI. T2w and DCE sequences showed overall low artifacts. Significant improvement in image quality for 3 T at the latest scanner generation was observed, especially for DWI (p < 0.03). ConclusionsMpMRI of patients with THR can be conducted at both field strengths without significant differences in artifacts. The localizer might be useful as an early forecasting feature for diagnostic value and particularly for contrast medium application decision. Patients with THR could benefit from technically advanced scanner generation and rs/ptx-EPI DWI sequences.

The Importance of Quality in Prostate MRI

Abstract Advances in prostate multiparametric MRI (mpMRI) continue to improve diagnostic accuracy for detecting clinically significant prostate cancer. High-quality studies demonstrate utility of mpMRI for augmenting the traditional biopsy pathway of random transrectal ultrasound biopsy after elevated PSA or abnormal digital rectal exam. mpMRI utilization has become more widespread, and it has become a priority to standardize prostate MRI acquisition, interpretation, and reporting across institutions and fields. Poor image quality may lead to missed lesions or inappropriate lesion characterization leading to inaccurate targeted biopsy and risk stratification. In this review, we aim to discuss the importance of image quality in prostate MRI, the current minimal technical standards for MRI, trends in adherence to these standards, and emerging attempts to standardize prostate mpMRI quality.

Deep Learning-Based Denoised MRI Images for Correlation Analysis between Lumbar Facet Joint and Lumbar Disc Herniation in Spine Surgery.

This work aimed to explore the relationship between spine surgery lumbar facet joint (LFJ) and lumbar disc herniation (LDH) via compressed sensing algorithm-based MRI images to analyze the clinical symptoms of patients with residual neurological symptoms after LDH. Under weighted BM3D denoising, Epigraph method was introduced to establish the novel CSMRI reconstruction algorithm (BEMRI). 127 patients with LDH were taken as the research objects. The BEMRI algorithm was compared with others regarding peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Patients' bilateral LFJ angles were compared. The relationships between LFJ angles, lumbar disc degeneration, and LFJ degeneration were analyzed. It turned out that the PSNR and SSIM of BEMRI algorithm were evidently superior to those of other algorithms. The proportion of patients with grade IV degeneration was at most 31.76%. Lumbar disc grading was positively correlated with change grading of LFJ degeneration (P < 0.001). LFJ asymmetry was positively correlated with LFJ degeneration grade and LDH (P < 0.001). Incidence of residual neurological symptoms in patients aged 61–70 years was as high as 63.77%. The proportion of patients with severe urinary excretion disorders was 71.96%. Therefore, the BEMRI algorithm improved the quality of MRI images. Degeneration of LDH was positively correlated with degeneration of LFJ. Asymmetry of LFJ was notably positively correlated with the degeneration of LFJ and LDH. Patients aged 61–70 years had a high incidence of residual neurological symptoms after surgery, most of which were manifested as urinary excretion disorders.

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

Utility of Quantitative T2-Mapping Compared to Conventional and Advanced Diffusion Weighted Imaging Techniques for Multiparametric Prostate MRI in Men with Hip Prosthesis.

Diffusion weighted imaging (DWI) is fundamental for prostate cancer (PCa) detection with MRI; however, limited by susceptibility artifact from hip prosthesis. To evaluate image quality and ability to detect PCa with quantitative T2-mapping and DWI in men with hip prosthesis undergoing prostate MRI. Prospective, cross-sectional study. Thirty consecutive men with hip replacement (18 unilateral, 12 bilateral) undergoing prostate MRI from 2019 to 2021. 3-T; multiparametric MRI (T2W, DCE-MRI, echo-planar [EPI]-DWI), T2-mapping (Carr-Purcell-Meiboom-Gill), FOCUS-EPI-DWI, PROPELLER-DWI. Five blinded radiologists independently evaluated MRI image quality using a 5-point Likert scale. PI-RADS v2.1 scores were applied in four interpretation strategies: 1) T2W-FSE+DCE-MRI+EPI-DWI, 2) T2W-FSE+DCE-MRI+EPI-DWI+FOCUS-EPI-DWI, 3) T2W-FSE+DCE-MRI+EPI-DWI+PROPELLER-DWI, 4) T2W-FSE+DCE-MRI+EPI-DWI+T2-maps. Five-point confidence scores were recorded. ANOVA, Kruskal-Wallis with pair-wise comparisons by Wilcoxon sign-rank, and paired t-tests, P < 0.05 was considered significant. Cohen's Kappa (k) for PI-RADSv2.1 scoring and proportion of correctly classified lesions tabulated for pathology-confirmed cases with 95% confidence intervals (CIs). For all radiologists, T2-map image quality was significantly higher than EPI-DWI, FOCUS-EPI-DWI, and PROPELLER-DWI and similar (P=0.146-0.706) or significantly better (for two readers) than T2W-FSE and DCE-MRI. PI-RADS v2.1 agreement improved comparing strategy A (k=0.46) to strategy B (k=0.58) to strategy C (k=0.58) and was highest with strategy D which included T2-maps (k=1.00). Radiologists' confidence was significantly highest with strategy D. Strategies B and C had similar confidence (P=0.051-0.063) both significantly outperforming strategy A. Twelve men with 17 lesions had pathology confirmed diagnoses (13 PCa, 4 benign). Strategy D had the highest proportion of correctly classified lesions (76.5-82.4%) with overlapping 95% confidence intervals. T2-mapping may be a valuable adjunct to prostate MRI in men with hip replacement resulting in improved image quality, higher reader confidence, interobserver agreement, and accuracy in PI-RADS scoring. 1 TECHNICAL EFFICACY STAGE: 2.