FLAIR2: A Combination of FLAIR and T2 for Improved MS Lesion Detection

Routine MR Imaging Protocol and Standardization in Central Nervous System Demyelinating Diseases

Standardized MR imaging protocols are important for the diagnosis and monitoring of patients with multiple sclerosis (MS) and the appropriate use of MR imaging in routine clinical practice. Advances in using MR imaging to establish an earlier diagnosis of MS, safety concerns regarding intravenous gadolinium-based contrast agents, and the value of spinal cord MR imaging for diagnostic, prognostic, and monitoring purposes suggest a changing role of MR imaging for the management and care of MS patients. The MR imaging protocol emphasizes 3 dimensional acquisitions for optimal comparison over time.

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning

BACKGROUND AND PURPOSE

Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy.

MATERIALS AND METHODS

We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.

RESULTS

The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.

CONCLUSIONS

The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.

Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.

Identification of Children's Tuberous Sclerosis Complex with Multiple-contrast MRI and 3D Convolutional Network

Identifying rare tuberous sclerosis complex (TSC) children is valuable and crucial. Magnetic resonance imaging (MRI) is used for rare TSC diagnoses. In this work, T2w and FLAIR were combined as a new modality named FLAIR3 to maximize the contrast between TSC lesions and normal-appearing brain tissues. After that, for the first time, we propose to use two different 3D CNN combined with late fusion strategies to diagnose TSC. A total of 520 children were enrolled in the study, including 260 health and 260 TSC children. The experiments had shown that the FLAIR3 could effectively improve the conspicuity of TSC lesions and classification performance. And the results showed the proposed late fusion method can improve the classification performance and achieve the state-of-the-art performance of the AUC of 0.994 and the accuracy of 0.971, which could be treated as an effective computer-aided diagnostic tool to help clinical radiologists diagnose TSC children. Clinical Relevance- Our deep learning method can be a non-invasive, efficient, and reliable way to help clinical radiologists to identify TSC patients. FLAIR3 can provide clinicians with a new modality to accurately localize TSC lesions in TSC patients.

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols

BACKGROUND

Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR²) results in isovoxel images with increased contrast-to-noise ratio, increased white-gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR² in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions.

OBJECTIVE

To compare lesion detection of 3D-FLAIR² with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images.

METHODS

We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR² scans using the ITK-SNAP tool.

RESULTS

LC lesion visibility was significantly improved by 3D-FLAIR² in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR² vs. 3D-FLAIR, 3D-FLAIR² detected on average 3.2 more cortical lesions (95% CI - 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR² detected on average 3.7 more LC lesions (95% CI 3.3-10.7).

CONCLUSIONS

3D-FLAIR² is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR².

Using Convolutional Encoder Networks to Determine the Optimal Magnetic Resonance Image for the Automatic Segmentation of Multiple Sclerosis

Multiple Sclerosis (MS) is a neuroinflammatory demyelinating disease that affects over 2,000,000 individuals worldwide. It is characterized by white matter lesions that are identified through the segmentation of magnetic resonance images (MRIs). Manual segmentation is very time-intensive because radiologists spend a great amount of time labeling T1-weighted, T2-weighted, and FLAIR MRIs. In response, deep learning models have been created to reduce segmentation time by automatically detecting lesions. These models often use individual MRI sequences as well as combinations, such as FLAIR2, which is the multiplication of FLAIR and T2 sequences. Unlike many other studies, this seeks to determine an optimal MRI sequence, thus reducing even more time by not having to obtain other MRI sequences. With this consideration in mind, four Convolutional Encoder Networks (CENs) with different network architectures (U-Net, U-Net++, Linknet, and Feature Pyramid Network) were used to ensure that the optimal MRI applies to a wide array of deep learning models. Each model had used a pretrained ResNeXt-50 encoder in order to conserve memory and to train faster. Training and testing had been performed using two public datasets with 30 and 15 patients. Fisher’s exact test was used to evaluate statistical significance, and the automatic segmentation times were compiled for the top two models. This work determined that FLAIR is the optimal sequence based on Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). By using FLAIR, the U-Net++ with the ResNeXt-50 achieved a high DSC of 0.7159.

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.

A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.

Use of Multiplied, Added, Subtracted and/or FiTted Inversion Recovery (MASTIR) pulse sequences

The group of Multiplied, Added, Subtracted and/or fiTted Inversion Recovery (MASTIR) pulse sequences in which usually two or more inversion recovery (IR) images of different types are combined is described, and uses for this type of sequence are outlined. IR sequences of different types can be multiplied, added, subtracted, and/or fitted together to produce variants of the MASTIR sequence. The sequences provide a range of options for increasing image contrast, demonstrating specific tissues and fluids of interest, and suppressing unwanted signals. A formalism using the concept of pulse sequences as tissue property filters is used to explain the signal, contrast and weighting of the pulse sequences with both univariate and multivariate filter models. Subtraction of one magnitude reconstructed IR image from another with a shorter TI can produce very high T₁ dependent positive contrast from small increases in T₁. The reverse subtracted IR sequence can provide high positive contrast enhancement with gadolinium chelates and iron deposition which decrease T₁. Additional contrast to that arising from increases in T₁ can be produced by supplementing this with contrast arising from concurrent increases in ρ_m and T₂, as well as increases or decreases in diffusion using subtraction IR with echo subtraction and/or diffusion subtraction. Phase images may show 180º differences as a result of rotating into the transverse plane both positive and negative longitudinal magnetization. Phase images with contrast arising in this way, or other ways, can be multiplied by magnitude IR images to increase the contrast of the latter. Magnetization Transfer (MT) and susceptibility can be used with IR sequences to improve contrast. Selective images of white and brown adipose tissue lipid and water components can be produced using different TIs and in and out-of-phase TEs. Selective images of ultrashort and short T₂ tissue components can be produced by nulling long T₂ tissue components with an inversion pulse and subtraction of images with longer TEs from images with ultrashort TEs. The Double Echo Sliding IR (DESIRE) sequence provides images with a wide range of TIs from which it is possible to choose values of TI to achieve particular types of tissue and/or fluid contrast (e.g., for subtraction with different TIs, as described above, and for long T₂ tissue signal nulling with UTE sequences). Unwanted tissue and fluid signals can be suppressed by addition and subtraction of phase-sensitive (ps) and magnitude reconstructed images. The sequence also offers options for synergistic use of the changes in blood and tissue ρ_m, T₁, T₂/T₂*, D* and perfusion that can be seen with fMRI of the brain. In-vivo and ex-vivo illustrative examples of normal brain, cartilage, multiple sclerosis, Alzheimer's disease, and peripheral nerve imaged with different forms of the MASTIR sequence are included.

Image processing approaches to enhance perivascular space visibility and quantification using MRI

Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

Chronic inflammation in multiple sclerosis - seeing what was always there

Activation of innate immune cells and other compartmentalized inflammatory cells in the brains and spinal cords of people with relapsing-remitting multiple sclerosis (MS) and progressive MS has been well described histopathologically. However, conventional clinical MRI is largely insensitive to this inflammatory activity. The past two decades have seen the introduction of quantitative dynamic MRI scanning with contrast agents that are sensitive to the reduction in blood-brain barrier integrity associated with inflammation and to the trafficking of inflammatory myeloid cells. New MRI imaging sequences provide improved contrast for better detection of grey matter lesions. Quantitative lesion volume measures and magnetic resonance susceptibility imaging are sensitive to the activity of macrophages in the rims of white matter lesions. PET and magnetic resonance spectroscopy methods can also be used to detect contributions from innate immune activation in the brain and spinal cord. Some of these advanced research imaging methods for visualization of chronic inflammation are practical for relatively routine clinical applications. Observations made with the use of these techniques suggest ways of stratifying patients with MS to improve their care. The imaging methods also provide new tools to support the development of therapies for chronic inflammation in MS.

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.

Optimizing 3D FLAIR to detect MS lesions: pushing past factory settings for precise results

BACKGROUND

To assess the diagnostic value of three 3D FLAIR sequences with differing repetition-times (TR) at 3-Tesla when detecting multiple sclerosis (MS) lesions.

METHODS

In this prospective study, approved by the institutional review board, 27 patients with confirmed MS were prospectively included. One radiologist performed manual segmentations of all high-signal intensity lesions using three 3D FLAIR data sets with different TR of 4800 ms ("FLAIR₄₈₀₀"), 8000 ms ("FLAIR₈₀₀₀") and 10,000 ms ("FLAIR_10,000") and two radiologists double-checked it. The main judgment criterion was the overall number of lesions; secondary objectives were the assessment of lesion location, as well as measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A non-parametric Wilcoxon's test was used to compare the differing FLAIR.

RESULTS

The FLAIR₈₀₀₀ and FLAIR_10,000 detected significantly more overall lesions per patient as compared with the FLAIR₄₈₀₀ [116.1 (± 61.7) (p = 0.02) and 115.8 (± 56.3) (p = 0.03) versus 99.2 (± 66.9), respectively]. The FLAIR₈₀₀₀ and FLAIR_10,000 detected four and eight times more cortical or juxta-cortical lesions per patient as compared with FLAIR₄₈₀₀ [1.6 (± 2.2) (p = 0.001) and 4.1 (± 5.9) (p = 6 × 10^-5) versus 0.4 (± 1.1), respectively]. CNR was significantly correlated to the TR value. It was significantly higher with FLAIR_10,000 than it was with FLAIR₈₀₀₀ and FLAIR₄₈₀₀ [16.3 (± 3.5) versus 15 (± 2.4) (p = 0.01) and 12 (± 2.2) (p = 2 × 10^-6), respectively] CONCLUSION: An optimized 3D FLAIR with a long TR significantly improved both overall lesion detection and CNR in MS patients as compared to a 3D FLAIR with factory settings.

FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images

Background

Accurate segmentation of MS lesions on MRI is difficult and, if performed manually, time consuming. Automatic segmentations rely strongly on the image contrast and signal-to-noise ratio. Literature examining segmentation tool performances in real-world multi-site data acquisition settings is scarce.

Objective

FLAIR², a combination of T₂-weighted and fluid attenuated inversion recovery (FLAIR) images, improves tissue contrast while suppressing CSF. We compared the use of FLAIR and FLAIR² in LesionTOADS, OASIS and the lesion segmentation toolbox (LST) when applied to non-homogenized, multi-center 2D-imaging data.

Methods

Lesions were segmented on 47 MS patient data sets obtained from 34 sites using LesionTOADS, OASIS and LST, and compared to a semi-automatically generated reference. The performance of FLAIR and FLAIR² was assessed using the relative lesion volume difference (LVD), Dice coefficient (DSC), sensitivity (SEN) and symmetric surface distance (SSD). Performance improvements related to lesion volumes (LVs) were evaluated for all tools. For comparison, LesionTOADS was also used to segment lesions from 3 T single-center MR data of 40 clinically isolated syndrome (CIS) patients.

Results

Compared to FLAIR, the use of FLAIR² in LesionTOADS led to improvements of 31.6% (LVD), 14.0% (DSC), 25.1% (SEN), and 47.0% (SSD) in the multi-center study. DSC and SSD significantly improved for larger LVs, while LVD and SEN were enhanced independent of LV. OASIS showed little difference between FLAIR and FLAIR², likely due to its inherent use of T₂w and FLAIR. LST replicated the benefits of FLAIR² only in part, indicating that further optimization, particularly at low LVs is needed. In the CIS study, LesionTOADS did not benefit from the use of FLAIR² as the segmentation performance for both FLAIR and FLAIR² was heterogeneous.

Conclusions

In this real-world, multi-center experiment, FLAIR² outperformed FLAIR in its ability to segment MS lesions with LesionTOADS. The computation of FLAIR² enhanced lesion detection, at minimally increased computational time or cost, even retrospectively. Further work is needed to determine how LesionTOADS and other tools, such as LST, can optimally benefit from the improved FLAIR² contrast.

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FLAIR² improves automatic MS lesion segmentation with LesionTOADS compared to FLAIR.

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Segmentation similarity improves for higher lesion volumes, particularly for FLAIR².

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FLAIR² provides greater sensitivity independent of lesion volume than FLAIR alone.

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Other segmentation tools need further optimization to fully benefit from FLAIR².

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FLAIR² provides immediate benefits at 1.5 T and visually improves segmentation at 3 T.

Improving Detection of Multiple Sclerosis Lesions in the Posterior Fossa Using an Optimized 3D-FLAIR Sequence at 3T

BACKGROUND AND PURPOSE

There is no consensus regarding the best MR imaging sequence for detecting MS lesions. The aim of our study was to assess the diagnostic value of optimized 3D-FLAIR in the detection of infratentorial MS lesions compared with an axial T2-weighted imaging, a 3D-FLAIR with factory settings, and a 3D double inversion recovery sequence.

MATERIALS AND METHODS

In this prospective study, 27 patients with confirmed MS were included. Two radiologists blinded to clinical data independently read the following sequences: axial T2WI, 3D double inversion recovery, standard 3D-FLAIR with factory settings, and optimized 3D-FLAIR. The main judgment criterion was the overall number of high-signal-intensity lesions in the posterior fossa; secondary objectives were the assessment of the reading confidence and the measurement of the contrast. A nonparametric Wilcoxon test was used to compare the MR images.

RESULTS

Twenty-two patients had at least 1 lesion in the posterior fossa. The optimized FLAIR sequence detected significantly more posterior fossa lesions than any other sequence: 7.5 versus 5.8, 4.8, and 4.1 (P values of .04, .03, and .03) with the T2WI, the double inversion recovery, and the standard FLAIR, respectively. The reading confidence index was significantly higher with the optimized FLAIR, and the contrast was significantly higher with the optimized FLAIR than with the standard FLAIR and the double inversion recovery.

CONCLUSIONS

An optimized 3D-FLAIR sequence improved posterior fossa lesion detection in patients with MS.

Three-dimensional MRI sequences in MS diagnosis and research

The most recent guidelines for magnetic resonance imaging (MRI) in multiple sclerosis (MS) recommend three-dimensional (3D) MRI sequences over their two-dimensional (2D) counterparts. This development has been made possible by advances in MRI scanner hardware and software. In this article, we review the 3D versions of conventional sequences, including T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR), as well as more advanced scans, including double inversion recovery (DIR), FLAIR2, FLAIR*, phase-sensitive inversion recovery, and susceptibility weighted imaging (SWI).

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid-Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T₂ fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T₂ FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape (P = 1.06 × 10⁻²) or the texture (P < 1 × 10⁻²⁰) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults.

Platform for Automated Real-Time High Performance Analytics on Medical Image Data

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

Noise reduction in FLAIR2 images using total generalized variation, Gaussian and Wiener filtering

PURPOSE

Multiplication of FLAIR and T2-weighted MRI scans results in images (called FLAIR²) with an improved contrast-to-noise ratio (CNR) for multiple sclerosis (MS) lesions but with a reduced signal-to-noise ratio (SNR). Denoising of these images may therefore further improve FLAIR² image quality. The purpose of this work is to present a systematic investigation of FLAIR² image denoising methods using Gaussian, Wiener and Total Generalized Variation (TGV) filtering approaches.

MATERIALS AND METHODS

T2-weighted and FLAIR data of four MS patients were used. For CNR and SNR measurements, each scan was performed up to three times. TGV, Gaussian and Wiener filtering was applied to T2, FLAIR and the FLAIR² data. FLAIR² images were afterwards additionally created using all combinations of input data (native, filtered T2 and filtered FLAIR). SNR and CNR measurements were performed using the subtraction method for all FLAIR² approaches (native and filtered input data) and for twenty MS lesions. Additionally, quantitative analysis of filtering based image blurring was performed on all data sets.

RESULTS

FLAIR² images denoised with TGV showed the highest SNR and CNR, while SNR values were similar for Gaussian and Wiener filtered images. The average CNR over 20 MS lesions within the native FLAIR² (32.99) achieved an improvement to 91.17, 82.33 and 56.07 corresponding to TGV, Wiener and Gaussian filtering. FLAIR multiplied with T2.denoised showed no improvement, while FLAIR.denoised multiplied with T2 showed an increase by a factor of two to the native, not filtered FLAIR². Blurring was most pronounced in Gaussian filtered images and similar in TGV and Wiener filtered images.

CONCLUSION

FLAIR images filtered with Wiener or TGV multiplied with the unfiltered T2 results in FLAIR² images with increased SNR and CNR and with minimal edge blurring.

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging

PURPOSE

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols.

METHODS

GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer.

RESULTS

GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast.

CONCLUSIONS

GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Optimal combination of FLAIR and T2-weighted MRI for improved lesion contrast in multiple sclerosis

PURPOSE

Postacquisition combination of three-dimensional T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images can improve the visualization of brain lesions in multiple sclerosis (MS). However, an optimal way to combine these images has not been described so far. The main objective of this study is to investigate an optimal combination of T2w and FLAIR to improve the conspicuity of MS lesions.

MATERIALS AND METHODS

We determined the parameters for a generalized multiplicative image combination which maximize the contrast-to-noise ratio (CNR) between lesions and normal-appearing brain tissue through simulations and verified experimentally. MRI data from 11 MS patients acquired at 3 Tesla were retrospectively analyzed using the proposed approach and compared with conventional FLAIR, and to images obtained by direct multiplication of T2w and FLAIR (FLAIR² ). Image quality was assessed by region-of-interest analysis. In addition, to evaluate the degree of cerebrospinal fluid (CSF) suppression, CSF-to-gray matter (CSF/GM) ratio was calculated. Reduction in global image contrast was assessed by computing the reduction in the contrast of mid-level intensity values.

RESULTS

An optimal combination was found to be the third order expression: FLAIR₃ = FLAIR^1.55 × T2w^1.45 . Compared with FLAIR, the lesion CNR was significantly increased by 1.9× (P < 0.005) and 2.5× (P < 0.001) using FLAIR² and FLAIR₃ , respectively. CSF/GM ratio was increased by 1.7× in FLAIR² (P < 0.001) compared with FLAIR, while it was reduced to 0.7× on FLAIR₃ (P < 0.05). The mid-intensity contrast was preserved on FLAIR² (P = 0.2), and decreased by 29% on FLAIR₃ (P < 0.001).

CONCLUSION

These results show that the optimized combination of FLAIR and T2w can improve MS lesion conspicuity. J. Magn. Reson. Imaging 2016;44:1293-1300.

A Prospective Pilot Investigation of Brain Volume, White Matter Hyperintensities, and Hemorrhagic Lesions after Mild Traumatic Brain Injury

Traumatic brain injury (TBI) is among the most common neurological disorders. Hemorrhagic lesions and white matter hyperintensities (WMH) are radiological features associated with moderate and severe TBI. Brain volume reductions have also been observed during the months following injury. In concussion, no signs of injury are observed on conventional magnetic resonance imaging (MRI), which may be a true feature of concussion or merely due to the limited sensitivity of imaging techniques used so far. Moreover, it is not known whether volume reductions are due to the resolution of trauma-related edema or a true volume loss. Forty-five collegiate-level ice hockey players (20 females) and 15 controls (9 females), 40 players underwent 3-T MRI for hemorrhages [multi-echo susceptibility-weighted imaging (SWI)], WMH (three-dimensional fluid-attenuated inversion recovery), and brain volume at the beginning and the end of the hockey season. Concussed athletes underwent additional imaging and neuropsychological testing at 3 days, 2 weeks, and 2 months after injury. At the end of the hockey season, brain volume was reduced compared to controls by 0.32% (p < 0.034) in the whole cohort and by 0.26% (p < 0.09) in the concussed athletes. Two weeks and 2 months after concussion, brain volume was reduced by −0.08% (p = 0.027) and −0.23% (p = 0.035), respectively. In athletes, the WMH were significantly closer to the interface between gray matter and white matter compared to controls. No significant changes in the number of WMH over the duration of the study were found in athletes. No microhemorrhages were detected as a result of concussion or playing a season of ice hockey. We conclude that mild TBI does not lead to transient increases in brain volume and no new microbleeds or WMH are detectable after concussion. Brain volume reductions appear by 2 weeks after concussion and persist until at least 2 months after concussion. Brain volume is reduced between the beginning and the end of the ice hockey season.