Automated patient-specific optimization of three-dimensional double-inversion recovery magnetic resonance imaging

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patients neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.

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.

Rapid whole-brain gray matter imaging using single-slab three-dimensional dual-echo fast spin echo: A feasibility study

PURPOSE

To achieve rapid, high resolution whole-brain gray matter (GM) imaging by developing a novel, single-slab three-dimensional dual-echo fast-spin-echo pulse sequence and GM-selective reconstruction.

METHODS

Unlike conventional GM imaging that uses time-consuming double-inversion-recovery preparation, the proposed pulse sequence was designed to have two split portions along the echo train, in which the first half was dedicated to yield short inversion recovery (IR)-induced white matter suppression and variable-flip-angle-induced two-step GM signal evolution while the second half cerebrospinal fluid-only signals. Multi-step variable-flip-angle schedules and sampling reordering were optimized to yield high GM signals while balancing cerebrospinal fluid signals between ECHOes. GM-selective images were then reconstructed directly from the weighted subtraction between ECHOes by solving a sparse signal recovery problem. In vivo studies were performed to validate the effectiveness of the proposed method over conventional double-inversion-recovery.

RESULTS

The proposed method, while achieving one millimeter isotropic, whole-brain GM imaging within 5.5 min, showed superior performance than conventional double-inversion-recovery in producing GM-only images without apparent artifacts and noise.

CONCLUSION

We successfully demonstrated the feasibility of the proposed method in achieving whole-brain GM imaging in a clinically acceptable imaging time. The proposed method is expected to be a promising alternative to conventional double-inversion-recovery in clinical applications. Magn Reson Med 78:1691-1699, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Synthetic MRI in the Detection of Multiple Sclerosis Plaques

BACKGROUND AND PURPOSE

Synthetic MR imaging enables the creation of various contrast-weighted images including double inversion recovery and phase-sensitive inversion recovery from a single MR imaging quantification scan. Here, we assessed whether synthetic MR imaging is suitable for detecting MS plaques.

MATERIALS AND METHODS

Quantitative and conventional MR imaging data on 12 patients with MS were retrospectively analyzed. Synthetic T2-weighted, FLAIR, double inversion recovery, and phase-sensitive inversion recovery images were produced after quantification of T1 and T2 values and proton density. Double inversion recovery images were optimized for each patient by adjusting the TI. The number of visible plaques was determined by a radiologist for a set of these 4 types of synthetic MR images and a set of conventional T1-weighted inversion recovery, T2-weighted, and FLAIR images. Conventional 3D double inversion recovery and other available images were used as the criterion standard. The total acquisition time of synthetic MR imaging was 7 minutes 12 seconds and that of conventional MR imaging was 6 minutes 29 seconds The lesion-to-WM contrast and lesion-to-WM contrast-to-noise ratio were calculated and compared between synthetic and conventional double inversion recovery images.

RESULTS

The total plaques detected by synthetic and conventional MR images were 157 and 139, respectively (P = .014). The lesion-to-WM contrast and contrast-to-noise ratio on synthetic double inversion recovery images were superior to those on conventional double inversion recovery images (P = .001 and < 0.001, respectively).

CONCLUSIONS

Synthetic MR imaging enabled detection of more MS plaques than conventional MR imaging in a comparable acquisition time. The contrast for MS plaques on synthetic double inversion recovery images was better than on conventional double inversion recovery images.

Patient-specific 3D FLAIR for enhanced visualization of brain white matter lesions in multiple sclerosis

PURPOSE

To improve the conspicuity of white matter lesions (WMLs) in multiple sclerosis (MS) using patient-specific optimization of single-slab 3D fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Sixteen MS patients were enrolled in a prospective 3.0T MRI study. FLAIR inversion time and echo time were automatically optimized for each patient during the same scan session based on measurements of the relative proton density and relaxation times of the brain tissues. The optimization criterion was to maximize the contrast between gray matter (GM) and white matter (WM), while suppressing cerebrospinal fluid. This criterion also helps increase the contrast between WMLs and WM. The performance of the patient-specific 3D FLAIR protocol relative to the fixed-parameter protocol was assessed both qualitatively and quantitatively.

RESULTS

Patient-specific optimization achieved a statistically significant 41% increase in the GM-WM contrast ratio (P < 0.05) and 32% increase in the WML-WM contrast ratio (P < 0.01) compared with fixed-parameter FLAIR. The increase in WML-WM contrast ratio correlated strongly with echo time (P < 10^-11 ). Two experienced neuroradiologists indicated substantially higher lesion conspicuity on the patient-specific FLAIR images over conventional FLAIR in 3-4 cases (intrarater correlation coefficient ICC = 0.72). In no case was the image quality of patient-specific FLAIR considered inferior to conventional FLAIR by any of the raters (ICC = 0.32).

CONCLUSION

Changes in proton density and relaxation times render fixed-parameter FLAIR suboptimal in terms of lesion contrast. Patient-specific optimization of 3D FLAIR increases lesion conspicuity without scan time penalty, and has potential to enhance the detection of subtle and small lesions in MS.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:557-564.

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.

Juxtacortical Lesions in Multiple Sclerosis: Assessment of Gray Matter Involvement Using Phase Difference-enhanced Imaging (PADRE)

Purpose:

In multiple sclerosis (MS), a juxtacortical lesion at the border between the gray matter (GM) and subcortical white matter (WM) may often involve the GM. A recently developed, phase-weighted magnetic resonance imaging (MRI) technique “phase difference enhanced imaging (PADRE)” can delineate the GM and WM clearly due to the difference in myelin concentration. We evaluated whether PADRE is useful for the detection of GM involvement in the juxtacortical MS lesions.

Methods:

One neuroradiologist reviewed the conventional MRI in 13 MS patients and selected 48 juxtacortical lesions. At the first reading session with the conventional MRI alone (T₂-weighted imaging, and two-dimensional and three-dimensional fluid-attenuated inversion recovery), two other neuroradiologists classified the lesions into three patterns according to their anatomical locations: (a) subcortical WM lesions involving the subcortical WM alone; (b) intracortical (IC) lesions involving the GM alone; (c) mixed GM/subcortical WM (mixed) lesions involving the both subcortical WM and GM. We defined the subcortical WM as a WM within a distance of 10 mm from inner edge of the GM. For the analyses, we excluded the white matter lesions further than 10 mm from inner edge of the GM. At the second reading session MRI and PADRE were available and the radiologists re-evaluated their prior classification.

Results:

At the first reading session, 27 lesions were classified as (a), 1 as (b), and 20 as (c). Therefore, a total of 21 lesions (44%) were judged to involve the GM. At the second reading session, the classification of 15 (31%) lesions changed; all 15 lesions were judged to involve the GM on the PADRE. Interobserver agreement (kappa value) was 0.84 for the first- and 0.95 for the second reading session.

Conclusion:

PADRE is useful for detecting GM involvement of the juxtacortical MS lesions.