Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach

BACKGROUND AND PURPOSE

The imaging of inflammatory myelopathies has advanced significantly across time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D double inversion recovery (DIR) imaging for inflammatory myelopathies remains unassessed. This study aimed to compare the acquisition time, image quality, diagnostic confidence, and lesion detection rates among sagittal T2WI, standard DIR, and DL-reconstructed DIR in patients with inflammatory myelopathies.

MATERIALS AND METHODS

In this observational study, patients diagnosed with inflammatory myelopathies were recruited between June 2023 and March 2024. Each patient underwent sagittal conventional TSE sequences and standard 3D DIR (T2WI and standard 3D DIR were used as references for comparison), followed by an undersampled accelerated double inversion recovery deep learning (DIRDL) examination. Three neuroradiologists evaluated the images using a 4-point Likert scale (from 1 to 4) for overall image quality, perceived SNR, sharpness, artifacts, and diagnostic confidence. The acquisition times and lesion detection rates were also compared among the acquisition protocols.

RESULTS

A total of 149 participants were evaluated (mean age, 40.6 [SD, 16.8] years; 71 women). The median acquisition time for DIRDL was significantly lower than for standard DIR (298 seconds [interquartile range, 288-301 seconds] versus 151 seconds [interquartile range, 148-155 seconds]; P < .001), showing a 49% time reduction. DIRDL images scored higher in overall quality, perceived SNR, and artifact noise reduction (all P < .001). There were no significant differences in sharpness (P = .07) or diagnostic confidence (P = .06) between the standard DIR and DIRDL protocols. Additionally, DIRDL detected 37% more lesions compared with T2WI (300 versus 219; P < .001).

CONCLUSIONS

DIRDL significantly reduces acquisition time and improves image quality compared with standard DIR, without compromising diagnostic confidence. Additionally, DIRDL enhances lesion detection in patients with inflammatory myelopathies, making it a valuable tool in clinical practice. These findings underscore the potential for incorporating DIRDL into future imaging guidelines.

Reliability assessment of accelerated MR sequences for the estimation of structural and vascular markers in dementia

Estimating the volume of brain structures and white matter lesions has been demonstrated to be crucial as diagnostic markers of dementia. In this context, employing accelerated MR sequences could be particularly suitable for deploying comfortable imaging protocols. The aims of the study are twofold. First, the quality of accelerated structural and FLAIR sequences will be evaluated using conventional sequences as a comparative reference. Second, their concordance in the estimation of diagnostic markers will be assessed. Forty-six dementia patients underwent brain 3T MR protocol, including conventional and accelerated 3D T1w and 3D FLAIR sequences. The image quality was assessed using peak signal-to-noise ratio, structural similarity index measure, and mean squared error. The intraclass correlation and the Dice similarity coefficients were used to compare the markers. The quality assessment showed high agreement between accelerated and conventional sequences. Excellent concordance was demonstrated in the quantification of brain structures and white matter lesions. The results demonstrate the reliability of diagnostic markers of dementia derived from accelerated MR sequences, thereby supporting their use in clinical practice.

Imaging the cerebral vasculature at different scales: translational tools to investigate the neurovascular interfaces

The improvements in imaging technology opened up the possibility to investigate the structure and function of cerebral vasculature and the neurovascular unit with unprecedented precision and gaining deep insights not only on the morphology of the vessels but also regarding their function and regulation related to the cerebral activity. In this review, we will dissect the different imaging capabilities regarding the cerebrovascular tree, the neurovascular unit, the haemodynamic response function, and thus, the vascular-neuronal coupling. We will discuss both clinical and preclinical setting, with a final discussion on the current scenery in cerebrovascular imaging where magnetic resonance imaging and multimodal microscopy emerge as the most potent and versatile tools, respectively, in the clinical and preclinical context.

Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities

PURPOSE

To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation.

MATERIALS AND METHODS

We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR-FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR-FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR-FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values.

RESULTS

All three neuroradiologists evaluated DLR-FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (p < 0.001). All three neuroradiologists assigned significantly higher frontal lobe gray/white matter visibility scores for DLR-FLAIR than for acc-FLAIR (p < 0.001); two neuroradiologists attributed significantly higher scores for DLR-FLAIR than for std-FLAIR (p < 0.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR-FLAIR (p < 0.0001). DLR-FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR-FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (p < 0.0001).

CONCLUSIONS

DLR-FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR-FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.

Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging

OBJECTIVE

To assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.

METHODS

A total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.

RESULTS

No significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07-0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).

CONCLUSION

The combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.

CRITICAL RELEVANCE STATEMENT

SyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.

KEY POINTS

Synthetic double inversion recovery (SyDIR) and T2WI have comparable image quality. SyDIR provides rectal cancer anatomical features for extramural infiltration detections. The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma

Background and objective

Malignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors.

Methods

In this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance.

Results

Several measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %.

Conclusions

The research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

DTI-MR fingerprinting for rapid high-resolution whole-brain T1 , T2 , proton density, ADC, and fractional anisotropy mapping

PURPOSE

This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time.

METHODS

To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design.

RESULTS

The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence.

CONCLUSION

The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

Fast Quantitative Magnetic Resonance Imaging Evaluation of Hydrocephalus Using 3-Dimensional Fluid-Attenuated Inversion Recovery: Initial Experience

OBJECTIVE

This study aimed to demonstrate the initial experience of using fast quantitative magnetic resonance imaging (MRI) to evaluate hydrocephalus.

METHODS

A total of 109 brain MRI volumetry examinations (acquisition time, 7 minutes 30 seconds) were performed in 72 patients with hydrocephalus. From the measured ventricular system and brain volumes, ventricle-brain volume percentage was calculated to standardize hydrocephalus severity (processing time, <5 minutes). The obtained values were categorized into no, mild, and severe based on the fronto-occipital horn ratio (FOHR) and the ventricle-brain volume percentages reported in the literature. The measured volumes and percentages were compared between patients with mild hydrocephalus and those with severe hydrocephalus. The diagnostic performance of brain hydrocephalus MRI volumetry was evaluated using receiver operating characteristic curve analysis.

RESULTS

Ventricular volumes and ventricle-brain volume percentages were significantly higher in in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 352.6 ± 165.6 cm 3 vs 149.1 ± 78.5 cm 3 , P < 0.001, and 26.8% [20.8%-33.1%] vs 12.1% ± 6.0%, P < 0.001; percentage-based severity: 359.5 ± 143.3 cm 3 vs 137.0 ± 62.9 cm 3 , P < 0.001, and 26.8% [21.8%-33.1%] vs 11.3% ± 4.2%, P < 0.001, respectively), whereas brain volumes were significantly lower in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 878.1 ± 363.5 cm 3 vs 1130.1 cm 3 [912.1-1244.2 cm 3 ], P = 0.006; percentage-based severity: 896.2 ± 324.6 cm 3 vs 1142.3 cm 3 [944.2-1246.6 cm 3 ], P = 0.005, respectively). The ventricle-brain volume percentage was a good diagnostic parameter for evaluating the degree of hydrocephalus (area under the curve, 0.855; 95% confidence interval, 0.719-0.990; P < 0.001).

CONCLUSIONS

Brain MRI volumetry can be used to evaluate hydrocephalus severity and may provide guide interpretation because of its rapid acquisition and postprocessing times.

Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.

Improved detection of juxtacortical lesions using highly accelerated double inversion-recovery MRI in patients with multiple sclerosis

PURPOSE

The purpose of this study was to compare a highly-accelerated double inversion recovery (fast-DIR) sequence using a recent parallel imaging technique (CAIPIRINHA) with a conventional DIR (conv-DIR) sequence for image quality and the detection of juxtacortical and infratentorial multiple sclerosis (MS) lesions.

MATERIALS AND METHODS

A total of 38 patients with MS who underwent brain MRI at 3 T between 2020 and 2021 were included. There were 27 women and 12 men with a mean age of 40 ± 12.8 (standard deviation) years (range: 20-59 years). All patients underwent conv-DIR sequence and fast-DIR sequence. Fast-DIR was obtained with a T2-preparation module to improve contrast and an iterative denoising algorithm to compensate noise enhancement. Two blinded readers reported the number of juxtacortical and infratentorial MS lesions for fast-DIR and conv-DIR, confirmed by further consensus reading that was used as the standard of reference. Image quality and contrast were evaluated for fast-DIR and conv-DIR sequences. Comparisons between fast-DIR and conv-DIR sequences were performed using Wilcoxon test and Lin concordance correlation coefficient.

RESULTS

Thirty-eight patients were analyzed. Fast-DIR imaging allowed detection of 289 juxtacortical lesions vs. 238 with conv-DIR, corresponding to a significant improved detection rate with fast-DIR (P < 0.001). Conversely, 117 infratentorial lesions were detected with conv-DIR sequence vs. 80 with fast-DIR sequence (P < 0.001). Inter-observer agreement for lesion detection with fast-DIR and conv-DIR was very high (Lin concordance correlation coefficient ranging between 0.86 and 0.96).

CONCLUSION

Fast-DIR improves the detection of juxtacortical MS lesions, but is limited for the detection of infratentorial MS lesions.

Diagnostic performance of double inversion recovery MRI sequence for synovitis of the wrist joints in rheumatoid arthritis

PURPOSE

To assess the diagnostic accuracy of double inversion recovery (DIR) magnetic resonance imaging (MRI) sequences for synovitis of the wrist joints in patients with rheumatoid arthritis (RA).

MATERIAL AND METHODS

Participants with newly diagnosed RA were enrolled between November 2019 and November 2020. MRI examinations of the wrist joints were performed using a contrast-enhanced T1-weighted imaging sequence (CE-T1WI) and DIR sequence. We measured synovitis score, number of synovial areas, synovial volume, mean synovium-to-bone signal ratio (SBR), and synovial contrast-to-noise ratio (SNR). The inter-reviewer agreement rated on a four-point scale was evaluated by calculating the weighted k statistics. Two MRI sequences were assessed using Bland-Altman analyses, and the diagnostic performance of DIR images was calculated using the chi-square test.

RESULTS

A total of 47 participants were evaluated, and 282 joint regions in 5076 images were reviewed by two readers. There was no significant difference in synovitis scores (P = 0.67), number of synovial areas (P = 0.89), and synovial volume (P = 0.086) between the two MRI sequences. DIR images showed better SBR and SNR (all P < 0.01). There was good agreement between the two reviewers in terms of synovitis distribution (κ = 0.79). The synovitis was well agreed upon by the two readers according to Bland-Altman analyses. Using CE-T1WI as the reference standard, DIR imaging demonstrated a sensitivity of 94.1% and a specificity of 84.6% at the patient level.

CONCLUSION

The non-contrast DIR sequence showed good consistency with CE-T1WI and potential for evaluating synovitis in patients with RA.

Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging

BACKGROUND AND PURPOSE

Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination.

MATERIALS AND METHODS

Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed.

RESULTS

Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.

CONCLUSIONS

Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.

Improved detection of multiple sclerosis lesions with T2-prepared double inversion recovery at 3T

BACKGROUND AND PURPOSE

Double inversion recovery (DIR) imaging is used in multiple sclerosis (MS) clinical protocols to improve the detection of cortical and juxtacortical gray matter lesions by nulling confounding signals originating from the cerebrospinal fluid and white matter. Achieving a high isotropic spatial resolution, to depict the neocortex and its typically small lesions, is challenged by the reduced signal-to-noise ratio (SNR) determined by multiple tissue signal nulling. Here, we evaluate both conventional and optimized DIR implementations to improve tissue contrast (TC), SNR, and MS lesion conspicuity.

METHODS

DIR images were obtained from MS patients and healthy controls using both conventional and prototype implementations featuring a T2-preparation module (T2P), to improve SNR and TC, as well as an image reconstruction routine with iterative denoising (ID). We obtained quantitative measures of SNR and TC, and evaluated the visibility of MS cortical, cervical cord, and optic nerve lesions in the different DIR images.

RESULTS

DIR implementations adopting T2P and ID enabled improving the SNR and TC of conventional DIR. In MS patients, 34% of cortical, optic nerve, and cervical cord lesions were visible only in DIR images acquired with T2P, and not in conventional DIR images. In the studied cases, image reconstruction with ID did not improve lesion conspicuity.

CONCLUSIONS

DIR with T2P should be preferred to conventional DIR imaging in protocols studying MS patients, as it improves SNR and TC and determines an improvement in cortical, optic nerve, and cervical cord lesion conspicuity.

Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction

BACKGROUND

 Recently introduced MRI techniques offer improved image quality and facilitate examinations of patients even when artefacts are expected. They pave the way for novel diagnostic imaging strategies in neuroradiology. These methods include improved 3D imaging, movement and metal artefact reduction techniques as well as Dixon techniques.

METHODS

 Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered.

RESULTS AND CONCLUSIONS

 3D FLAIR is an example of a versatile 3D Turbo Spin Echo sequence with broad applicability in routine brain protocols. It facilitates detection of smaller lesions and more precise measurements for follow-up imaging. It also offers high sensitivity for extracerebral lesions. 3D techniques are increasingly adopted for imaging arterial vessel walls, cerebrospinal fluid spaces and peripheral nerves. Improved hybrid-radial acquisitions are available for movement artefact reduction in a broad application spectrum. Novel susceptibility artefact reduction techniques for targeted application supplement previously established metal artefact reduction sequences. Most of these techniques can be further adapted to achieve the desired diagnostic performances. Dixon techniques allow for homogeneous fat suppression in transition areas and calculation of different image contrasts based on a single acquisition.

KEY POINTS

  · 3D FLAIR can replace 2 D FLAIR for most brain imaging applications and can be a cornerstone of more precise and more widely applicable protocols.. · Further 3D TSE sequences are increasingly replacing 2D TSE sequences for specific applications.. · Improvement of artefact reduction techniques increase the potential for effective diagnostic MRI exams despite movement or near metal implants.. · Dixon techniques facilitate homogeneous fat suppression and simultaneous acquisition of multiple contrasts..

CITATION FORMAT

· Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8692.

Imaging cortical multiple sclerosis lesions with ultra-high field MRI

BACKGROUND

Cortical lesions are abundant in multiple sclerosis (MS), yet difficult to visualize in vivo. Ultra-high field (UHF) MRI at 7 T and above provides technological advances suited to optimize the detection of cortical lesions in MS.

PURPOSE

To provide a narrative and quantitative systematic review of the literature on UHF MRI of cortical lesions in MS.

METHODS

A systematic search of all literature on UHF MRI of cortical lesions in MS published before September 2020. Quantitative outcome measures included cortical lesion numbers reported using 3 T and 7 T MRI and between 7 T MRI sequences, along with sensitivity of UHF MRI towards cortical lesions verified by histopathology.

RESULTS

7 T MRI detected on average 52 ± 26% (mean ± 95% confidence interval) more cortical lesions than the best performing image contrast at 3 T, with the largest increase in type II-IV intracortical lesion detection. Across all studies, the mean cortical lesion number was 17 ± 6 per patient. In progressive MS cohorts, approximately four times more cortical lesions were reported than in CIS/early RRMS, and RRMS. Yet, there was no difference in lesion type ratio between these MS subtypes. Furthermore, superiority of one MRI sequence over another could not be established from available data. Post-mortem lesion detection with UHF MRI agreed only modestly with pathological examinations. Mean pro- and retrospective sensitivity was 33 ± 6% and 71 ± 10%, respectively, with the highest sensitivity towards type I and type IV lesions.

CONCLUSION

UHF MRI improves cortical lesion detection in MS considerably compared to 3 T MRI, particularly for type II-IV lesions. Despite modest sensitivity, 7 T MRI is still capable of visualizing all aspects of cortical lesion pathology and could potentially aid clinicians in diagnosing and monitoring MS, and progressive MS in particular. However, standardization of acquisition and segmentation protocols is needed.

Role of Double Inversion Recovery Sequence in Neuro-imaging on 3 Tesla MRI

Double Inversion Recovery (DIR) is a robust sequence designed to suppress fat and water signals using two 180° inversion pulses to produce prominent gray matter contrast with high spatial resolution. It has proven to be more sensitive in delineating white matter signal abnormalities than conventional MR techniques. In our study, the highest image contrast with lesion load was observed using DIR over FLAIR and T2 weighted imaging. DIR is evidently valuable for the detection of demyelinating lesions observed in multiple sclerosis (MS), malignancies, epileptogenic foci, and cortical anomalies. Hence this pictorial review is intended to assess the diagnostic efficacy of DIR modality in clinical Neuro-imaging.

Comparative study between double inversion recovery (DIR) and fluid-attenuated inversion recovery (FLAIR) MRI sequences for detection of cerebral lesions in multiple sclerosis

Background

Multiple sclerosis (MS) is a common chronic inflammatory demyelinating disorder more common in young adults. MS is characterized mainly with white matter (WM) affection; however, considerable gray matter (GM) involvement is also noted in many patients. MRI is used for diagnosis and follow up of the disease using different pulse sequences; FLAIR imaging provides the highest sensitivity in the detection of supratentorial, juxtacortical, and the periventricular lesions but is less sensitive in the posterior fossa. A double inversion recovery (DIR) pulse sequence was recently introduced to improve the visibility of GM lesions and especially cortical lesions. The aim of this study is to assess the role of DIR sequence in the detection of brain lesions in patients with MS compared to FLAIR sequence.

Results

DIR showed a significantly higher number of MS lesions in infratentorial region (2.9 ± 0.4 compared to 2.25 ± 0.3 in FLAIR) with a statistically significant difference (p = 0.002) and also in supratentorial periventricular regions (11.84 ± 8.07 in DIR and 11.31 ± 8.07 in FLAIR, p < 0.001). DIR imaging also demonstrated significantly more intracortical lesions (7.12 ± 1.2 compared to 1.4 ± 0.9 in FLAIR imaging) with a statistically significant difference (p < 0.001). On the other hand, corpus callosum lesions were significantly higher on FLAIR (0.84 ± 0.1) with respect to DIR imaging (0.68 ± 0.1) with a statistically significant difference in between (p = 0.025).

Conclusion

DIR is a powerful conventional MRI sequence for visualization of brain lesions in patients with MS and is superior to FLAIR sequence in detecting lesions in different locations, namely cortical, periventricular, and infratentorial regions; hence, DIR can be added to the MRI protocol of MS patients or even can replace FLAIR which would be of a good diagnostic value with only 80 s added to the scan time.

A novel ex vivo, in situ method to study the human brain through MRI and histology

BACKGROUND

MRI-histology correlation studies of the ex vivo brain mostly employ fresh, extracted (ex situ) specimens, aldehyde fixed by immersion, which has several disadvantages for MRI scanning (e.g. deformation of the organ). A minority of studies are done ex vivo-in situ (unfixed brain), requiring an MRI scanner readily available within a few hours of the time of death.

NEW METHOD

We propose a new technique, exploited by anatomists, for scanning the ex vivo brain: fixation by whole body perfusion, which implies fixation of the brain in situ. This allows scanning the brain surrounded by fluids, meninges, and skull, preserving the structural relationships of the brain in vivo. To evaluate the proposed method, five heads perfused-fixed with a saturated sodium chloride solution were employed. Three sequences were acquired on a 1.5 T MRI scanner: T1weighted, T2weighted-FLAIR, and Gradient-echo. Histology analysis included immunofluorescence for myelin basic protein and neuronal nuclei.

RESULTS

All MRIs were successfully processed through a validated pipeline used with in vivo MRIs. All cases exhibited positive antigenicity for myelin and neuronal nuclei.

COMPARISON WITH EXISTING METHODS

All scans registered to a standard neuroanatomical template in pseudo-Talairach space more accurately than an ex vivo-ex situ scan. The time interval to scan the ex vivo brain in situ was increased to at least 10 months.

CONCLUSIONS

MRI and histology study of the ex vivo-in situ brain fixed by perfusion is an alternative approach that has important procedural and practical advantages over the two standard methods to study the ex vivo brain.

IPEM Topical Report: A 2018 IPEM survey of MRI use for external beam radiotherapy treatment planning in the UK

The benefits of integrating MRI into the radiotherapy pathway are well published, however there is little consensus in guidance on how to commission or implement its use. With a view to developing consensus guidelines for the use of MRI in external beam radiotherapy (EBRT) treatment planning in the UK, a survey was undertaken by an Institute of Physics and Engineering in Medicine (IPEM) working-party to assess the current landscape of MRI use in EBRT in the UK. A multi-disciplinary working-party developed a survey to understand current practice using MRI for EBRT treatment planning; investigate how MRI is currently used and managed; and identify knowledge gaps. The survey was distributed electronically to radiotherapy service managers and physics leads in 71 UK radiotherapy (RT) departments (all NHS and private groups). The survey response rate was 87% overall, with 89% of NHS and 75% of private centres responding. All responding centres include EBRT in some RT pathways: 94% using Picture Archiving and Communication System (PACS) images potentially acquired without any input from RT departments, and 69% had some form of MRI access for planning EBRT. Most centres reporting direct access use a radiology scanner within the same hospital in dedicated (26%) or non-dedicated (52%) RT scanning sessions. Only two centres reported having dedicated RT MRI scanners in the UK, lower than reported in other countries. Six percent of radiotherapy patients in England (data not publically available outside of England) have MRI as part of their treatment, which again is lower than reported elsewhere. Although a substantial number of centres acquire MRI scans for treatment planning purposes, most centres acquire less than five patient scans per month for each treatment site. Commissioning and quality assurance of both image registration and MRI scanners was found to be variable across the UK. In addition, staffing models and training given to different staff groups varied considerably across the UK, reflecting the current lack of national guidelines. The primary barriers reported to MRI implementation in EBRT planning included costs (e.g. lack of a national tariff for planning MRI), lack of MRI access and/or capacity within hospitals. Despite these challenges, significant interest remains in increasing MRI-assisted EBRT planning over the next five years.

Common and Uncommon Artifacts in T1 FLAIR SAG Sequences of MRI Brain

OBJECTIVE

This study aims at identifying, classifying, and measuring the frequency the different artifacts that show up in the images of the Sagittal T1 Fluid Attenuated Inversion Recovery (FLAIR) sequence.

MATERIALS AND METHODS

A total of 101 subjects underwent brain magnetic resonance imaging examination with the following sequences: Axial T1 FLAIR, Axial T2-weighted imaging, Diffusion Weighted Imaging, 2D Multiple Echo Recombined Gradient Echo, Sagittal T1 FLAIR, Coronal T2 Turbo Spin Echo, Spin Echo T1-weighted imaging, and 3D Fast Spoiled Gradient-echo. In these images, we observed the following categories of artifacts: (a) ghost artifacts, (b) aliasing behind the occipital bone, (c) aliasing inside the sphenoid cavity, (d) susceptibility artifacts, and (e) pulsation artifacts. In order to recognize and verify the artifacts, we used not only the Sagittal T1 FLAIR sequence, but also Sagittal reconstructions from the 3-dimensional Fast Spoiled Gradient-echo sequence and the other routine sequences.

RESULTS

Aliasing artifacts and especially aliasing of nose are present in 41% of the cases. In 45% of these cases the uncommon aliasing artifacts, which took place into the brain parenchyma (sphenoid cavity, subarachnoid bay, or pituitary) originated from nose. In 33% of the subjects, ghost artifacts are presented, which stem from the nose, the orbits, or other pulsating structures (pulsation artifacts) or even from fat tissue. Moreover, susceptibility artifacts comprise 8% of all the artifacts. Finally, 19% of brains were presented without artifact.

CONCLUSIONS

We suggest in addition to T1 FLAIR, the application of Sagittal SE or TSE sequences in magnetic resonance imaging examination of brain, trying to include the nose in the square of FOV.

MRI in systems medicine

Magnetic resonance imaging (MRI) is one of the primary medical imaging modalities and a key component of the standard of care in modern healthcare systems. One of the factors that distinguishes MRI from other imaging methods is the ability to program the MRI system to reveal a wide range of imaging contrasts, where each type of contrast offers unique information about the biological sample of interest. This ability stems from the fact that both the amplitude and phase of the magnetization of the underlying tissue can be manipulated to highlight different biological phenomenon. The flexibility and capabilities offered by modern MRI systems have enabled the development of a myriad of techniques for characterizing anatomy, physiology, and function. These include methods to characterize gross anatomy, tissue microstructure, bulk blood flow, tissue perfusion, and functional changes in blood oxygenation. This article is categorized under: Laboratory Methods and Technologies > Imaging Translational, Genomic, and Systems Medicine > Diagnostic Methods.

A Comprehensive Atlas of E3 Ubiquitin Ligase Mutations in Neurological Disorders

Protein ubiquitination is a posttranslational modification that plays an integral part in mediating diverse cellular functions. The process of protein ubiquitination requires an enzymatic cascade that consists of a ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2) and an E3 ubiquitin ligase (E3). There are an estimated 600-700 E3 ligase genes representing ~5% of the human genome. Not surprisingly, mutations in E3 ligase genes have been observed in multiple neurological conditions. We constructed a comprehensive atlas of disrupted E3 ligase genes in common (CND) and rare neurological diseases (RND). Of the predicted and known human E3 ligase genes, we found ~13% were mutated in a neurological disorder with 83 total genes representing 70 different types of neurological diseases. Of the E3 ligase genes identified, 51 were associated with an RND. Here, we provide an updated list of neurological disorders associated with E3 ligase gene disruption. We further highlight research in these neurological disorders and discuss the advanced technologies used to support these findings.