Jul 2026· Sriwijaya Journal of Radiology and Imaging Research· Vol 3, pp. 75-82· 0 citations· 22 references
TL;DR
A deep-learning framework for image normalisation and noise reduction to elevate 0.35T brain MRI toward high-field standards with almost-perfect reader agreement is developed and validated and is a low-cost route to more equitable neuroimaging.
Abstract
Background: Low-field MRI (LF-MRI) widens neuroimaging access in resource-limited settings but suffers low signal-to-noise ratio (SNR), reduced resolution and artefacts. We developed and validated a deep-learning framework for image normalisation and noise reduction to elevate 0.35T brain MRI toward high-field quality, and tested whether it improves clinically significant lesion detection.
Methods: In a multicentre retrospective diagnostic-accuracy study (STARD 2015), 450 adults underwent non-contrast 0.35T brain MRI (T1W, T2W, FLAIR) across three private tertiary centres in Palembang, Indonesia. Images were enhanced with a CycleGAN incorporating Vision-Transformer blocks. Three blinded neuroradiologists scored a 5-point Likert scale and recorded lesion presence; paired 1.5T MRI was the reference standard. Sensitivity, specificity, AUC and likelihood ratios were computed with 95% CIs; tests compared by McNemar and DeLong; agreement by Fleiss kappa.
Results: AI enhancement improved all quality metrics (e.g., T1W PSNR 22.15 to 28.45 dB; SSIM 0.71 to 0.89; all p<0.001). For lesion detection, AI-enhanced LF-MRI achieved sensitivity 93.9% (95% CI 89.4-96.6), specificity 91.1% (87.1-94.0) and AUC 0.94 (0.91-0.97) versus 78.3%, 81.1% and 0.81 for original images (DeLong p<0.001; McNemar p<0.001). LR+ rose to 10.56 and LR- fell to 0.067. Inter-reader agreement was almost perfect (Fleiss kappa 0.78-0.85).
Conclusions: A CycleGAN-with-transformer framework substantially improved objective quality and diagnostic performance of 0.35T brain MRI toward high-field standards with almost-perfect reader agreement. Pending prospective and external validation, AI enhancement is a low-cost route to more equitable neuroimaging.
BACKGROUND
Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. Higher doses of GBCAs can improve lesion sensitivity but may have safety implications, particularly in light of recent findings on gadolinium retention and deposition.
PURPOSE
To evaluate the clinical performance of an FDA-cleared deep-learning (DL)-based contrast boosting algorithm in routine clinical brain MRI exams.
STUDY TYPE
Retrospective.
POPULATION
One hundred ten patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) with clinical contrast-enhanced brain MR studies.
FIELD STRENGTH AND SEQUENCES
T1 weighted pre-contrast and post-contrast brain MRI sequences at 0.3 T, 1.5 T, and 3 T.
ASSESSMENT
A multi-center database of contrast-enhanced brain MR images was used to evaluate a DL-based contrast boosting algorithm. Pre-contrast and standard post-contrast (SC) images were processed with the algorithm to obtain contrast boosted (CB) images. CB images were compared to SC images in terms of contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Three board-certified radiologists with 9, 15, and 18 years of experience reviewed CB and SC images side-by-side for qualitative evaluation and rated them on a 4-point Likert scale for lesion contrast enhancement, border delineation, internal morphology, overall image quality, presence of artifacts, and changes in vessel conspicuity. The presence, cause, and severity of any false lesions was recorded.
STATISTICAL TESTS
Wilcoxon signed rank test. A p value < 0.05 was considered significant.
RESULTS
CB images had significantly superior quantitative performance than SC images in terms of CNR (729.17% ± 1576.92%), LBR (87.91% ± 72.12%), and CEP (165.81% ± 42%). In the qualitative assessment, CB images showed significantly better lesion visualization (3.73 vs. 3.16) and had significantly better image quality (3.55 vs. 3.07).
DATA CONCLUSION
In this multi-center, multi-reader study, deep learning-based contrast boosting demonstrates robust improvements in lesion visualization and image quality without increasing contrast dosage.
EVIDENCE LEVEL
4.
TECHNICAL EFFICACY
Stage 3.
Srivathsa Pasumarthi Venkata, Thomas Campbell Arnold, Sonia Colombo Serra et al.· Journal of Magnetic Resonanc...· 0 citations
PURPOSE
A deep learning algorithm for contrast amplification in brain MRI, trained exclusively on adult data, was tested for cross-population generalization to pediatric patients, including subjects aged 0-2 years.
METHODS
A retrospective monocentric dataset (n = 22 cases) comprising pediatric patients (0-17 years old) diagnosed with various brain tumors was used to evaluate the algorithm, which takes T1-weighted pre- and standard post-contrast images as input and generates an output image with amplified contrast, further post-processed with an HDR algorithm. Quantitative comparisons between standard and amplified images were performed using contrast-to-noise ratio (CNR), contrast enhancement percentage (CEP), and lesion-to-background ratio (LBR). Three neuroradiologists performed qualitative assessment using a 4-point Likert scale, focusing on lesion contrast and delineation. Anatomical similarity was assessed using SSIM and log-Jacobian range. Statistical significance was evaluated using two-tailed paired t-tests.
RESULTS
Compared to standard-dose images, contrast-amplified images showed significantly higher values for CNR (+ 186.5%), LBR (+ 61.9%), and CEP (+ 110.4%). Qualitative assessments demonstrated comparable lesion visualization, with improvements observed in selected cases. Reader 1 preferred the contrast-amplified image in 12 of 22 cases (54.5%), reader 2 favored it in 18 of 22 cases (81.8%) and reader 3 in 13/22 cases (59.1%). One reader reported improved overall image quality (mean score: 3.95 vs. 3.73). The average SSIM between amplified and standard-dose images was 0.98, and any significant anatomical differences were highlighted by the log-Jacobian range (p-value = 0.556).
CONCLUSION
An algorithm for contrast amplification based on deep learning, trained with adult data, significantly enhances quantitative contrast metrics in images from pediatric patients. It is preferred over standard-dose images in the majority of cases when used for pediatric brain MRI, indicating its promising application for cross-population applicability.
Anna Macula, G. Morana, Fiorenza Coppola et al.· Neuroradiology· 0 citations
Objectives: Neonatal imaging is particularly challenging because newborns are prone to head motion, which can degrade image quality and complicate interpretation. Improving brain magnetic resonance imaging (MRI) image quality may help reduce diagnostic uncertainty and facilitate the nuanced assessment of early myelinating structures in the neonatal brain. Although deep learning reconstruction algorithms designed to improve MRI image quality have been evaluated in pediatric imaging, they have not been specifically studied in exclusively neonatal populations. This pilot study sought to evaluate image quality improvement through the employment of a deep learning reconstruction algorithm in neonatal brain imaging. Materials and Methods: 3D T1-weighted brain MRIs were obtained in a small cohort of 15 neonates. A deep learning reconstruction algorithm was applied to the image sets using low, medium, and high levels of denoising. Three radiologists qualitatively rated image quality (signal-to-noise ratio [SNR], presence of artifacts, and overall clarity) on a 4-point scale of eight early myelinating structures. Objective apparent SNR (aSNR) and apparent contrast-to-noise ratio (aCNR), based on signal intensities of white and gray matter, were measured across all three denoising levels. Results: Evaluation by radiologists indicated an overall increase in all image quality categories and increased conspicuity of the early myelinating structures as the level of denoising increased. Objective aSNR and aCNR values also increased progressively with denoising, with significant differences observed for nearly all pairwise comparisons. Conclusion: Our findings suggest improvement in image quality with the use of the deep learning reconstruction algorithm in 3D T1-weighted neonatal brain MRI, though the small sample size and pilot design limit generalizability. Diagnostic accuracy and clinical outcomes were not evaluated and warrant future investigation.
Z. Alvi, E. P. Reis, M. Esmeraldo et al.· Journal of Clinical Imaging...· 0 citations
OBJECTIVES
To evaluate the diagnostic performance of low-field 0.55 T knee magnetic resonance imaging (MRI) with and without deep learning (DL)-based image denoising techniques compared to conventional 3 T MRI.
METHODS
This retrospective study included 33 knee MRIs from 26 patients (10 women, mean age 52 years; ±16.9 years) with knee pain (mean duration 2.7 years; ±4.8 years) who underwent both 0.55 T and 3 T MRI. DL-based denoising (ImT-MRD) was applied to all 0.55 T datasets. Four radiologists assessed image quality, anatomical landmark visibility (4-point Likert scale), and diagnostic confidence for pathology detection (10-point scale). Diagnostic accuracy, including sensitivity and specificity, was calculated using consensus reading as standard of reference. Agreement was performed using Lin's concordance correlation coefficient (CCC).
RESULTS
0.55 T without DL showed a moderate sensitivity of 0.83, a specificity of 0.82, and an overall diagnostic accuracy of 0.83. For 3 T, sensitivity, specificity, and diagnostic accuracy were 0.95, 0.98, and 0.97, respectively. 0.55 T with DL improved sensitivity to 0.97 and diagnostic accuracy to 0.98. Agreement with 3 T was excellent for 0.55 T with DL (CCC: 0.94) and substantial for 0.55 T without DL (CCC: 0.78). Image quality was significantly higher for 0.55 T with DL compared to 0.55 T without DL (2.92vs.2.55;p = 0.005), yet 3 T outperformed both (3.44;p < 0.001). Diagnostic confidence was significantly higher for 0.55 T with DL compared to 0.55 T without DL (8.21vs.7.66;p < 0.001), with 3 T demonstrating the highest confidence levels overall (8.60; all p < 0.001).
CONCLUSION
DL-based denoising of 0.55 T knee MRI significantly enhances the diagnostic performance, demonstrating results comparable to conventional 3 T, which were not achieved with 0.55 T MRI alone.
S. Ulas, Madeline Hess, Zheren Zhu et al.· European Journal of Radiolog...· 0 citations
BACKGROUND AND PURPOSE
Artificial intelligence (AI) algorithms have been used to synthesize standard-dose images from low-dose images in brain MRI, but have less been evaluated to boost standard-dose contrast to approximate higher-dose effect. This study aims to evaluate the performance of a deep learning-based post-processing tool (AiMIFY) in enhancing contrast and improving lesion visualization on standard-dose contrast-enhanced brain MRI.
MATERIALS AND METHODS
In this retrospective, multicenter, multireader study, 86 adult patients who underwent standard-dose contrast-enhanced brain MRI were included. Pre-contrast and post-contrast three-dimensional T1-weighted images were processed using AiMIFY to generate contrast-boosting images. Three independent neuroradiologists performed blinded assessments. Quantitative metrics, including contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP), were measured. Subjective image quality (border delineation, internal morphology, and contrast enhancement) were evaluated using a Likert scale. The overall diagnostic preference was recorded. Comparisons between standard post-contrast and AiMIFY-processed images were performed using the Wilcoxon signed-rank test.
RESULTS
AiMIFY-processed images demonstrated significantly higher CNR, LBR, and CEP compared with standard-dose post-contrast images across all readers (all P < 0.001), with mean increases of 495.16%, 58.94%, and 160.55%, respectively. Subjective assessments showed significant improvements in border delineation, internal morphology, and contrast enhancement (all P < 0.05). Subgroup analysis of small lesions (< 10 mm) revealed consistently higher subjective scores for AiMIFY-processed images (all P < 0.05). AiMIFY-processed images were preferred in the majority of cases (84.9%, 29.1%, and 68.6% across readers; all P < 0.001).
CONCLUSION
Deep learning-based enhancement using AiMIFY significantly improves lesion conspicuity on standard-dose contrast-enhanced brain MRI, with notable benefits for small lesions. This approach may represent an adjuvant to standard-dose MRI to enhance diagnostic confidence while avoiding increased contrast agent exposure.
Yuantong Gao, Bin Chen, Caiyun Wen et al.· AJNR. American journal of ne...· 0 citations
PURPOSE
Deep learning (DL) denoising may improve cone-beam CT (CBCT) image quality for point-of-care stroke assessment in the interventional suite. The purpose of this study was to evaluate the impact of a DL-based denoising algorithm on objective and subjective image quality in brain CBCT using both standard circular and advanced dual-axis trajectories.
METHODS
We retrospectively analyzed 20 noncontrast brain CBCT acquisitions (Karolinska: 10 standard circular; St Michael's: 10 dual-axis). A DL-based denoising algorithm was applied at three strengths (Minimal, Medium, High) and compared to standard images with no additional denoising. Objective metrics (noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact indices) were measured using standardized ROIs. Six experts rated subjective image quality on 5-point Likert scales. Paired tests with Bonferroni correction were used for comparisons.
RESULTS
High-level DL denoising significantly improved all objective metrics (noise, SNR, CNR, artifact indices) for both thin and thick slices (all p<.001). It doubled gray-white matter CNR (thin slices: 2.31 vs. 1.08), reduced noise, and improved subcalvarial and posterior fossa artifact indices. Subjectively, high-level denoising yielded higher median ratings for noise, texture, sharpness, brain parenchyma visualization, CSF spaces, and confidence in assessing ischemia and hemorrhage (all p<.001). Improvements were consistent for both acquisition techniques, and perceived artifact severity did not differ (p>.99). Inter-reader agreement was substantial.
CONCLUSION
The DL-based denoising algorithm significantly improved objective and subjective brain CBCT image-quality; no difference in perceived artifact severity was detected. These findings support further evaluation of deep learning-enhanced CBCT denoising for brain imaging in the interventional suite.
F. Ståhl, N. Cancelliere, J. Kolloch et al.· Neuroradiology· 0 citations