Optimization of MR protocols: a statistical decision analysis approach

Task-driven assessment of experimental designs in diffusion MRI: A computational framework

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline's task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline's advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method's predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

Enhancing the precision of quantitative water content mapping by optimizing sequence parameters

This article describes a methodology for determining the precision of a recently published method for quantitative water content mapping using MRI. A framework for calculating the random error component as a function of sequence parameters is provided and predictions are compared with experimental results obtained in a phantom. The experimental and theoretical results are in good agreement and demonstrate that the precision of quantitative water content mapping can be significantly increased by a proper and objective choice of acquisition parameters.

Feature space analysis: effects of MRI protocols

We present a method for exploring the relationship between the image segmentation results obtained by an optimal feature space method and the MRI protocols used. The steps of the work accomplished are as follows. (1) Patients with brain tumors were imaged on a 1.5 T General Electric Signa MRI System using multiple protocols (T1 and T2-weighted fast spin-echo and FLAIR). T1-weighted images were acquired before and after gadolinium injection. (2) Image volumes were co-registered, and images of a slice through the center of the tumor were selected for processing. (3) For each patient, several image sets were defined by selecting certain MR images (e.g., 4T2's+ IT1, 4T2's+FLAIR, 2T2's+ 1T1). (4) Using each image set, the optimal feature space was generated and images were segmented into normal tissues and different tumor zones. (5) Segmentation results obtained using the different MRI sets were compared. Based on the analysis results from 27 image sets, we found that the locations of the clusters for the tumor zones and their corresponding regions in the image domain changed as a function of the MR images (MRI protocols) used. However, the segmentation results for the total lesion and normal tissues remained relatively unchanged.

Accurate determination of spin-density and T1 in the presence of RF-field inhomogeneities and flip-angle miscalibration

A method is presented for the accurate extraction of relative spin-density (rho0) and spin-lattice relaxation time (T1) in the presence of RF-field inhomogeneities and flip-angle miscalibration. The method requires collecting images at several flip-angles with a three-dimensional, spoiled steady-state, gradient-echo imaging sequence. Results show that the predominant effect of an overestimated flip-angle is to shift the T1 estimate to a higher value, whereas reductions in the normalized RF-field from unity cause rho0 and T1 distributions to be skewed toward lower values. Phantom and in vivo results demonstrate that the proposed method overcomes both of these systematic errors. The method was shown to be valid for up to a 50% reduction in RF sensitivity. A self-consistency argument was used to validate the absence of systematic errors in the extracted rho0 and T1 values over a large number of voxels. This made it possible to obtain a very precise estimate of muscle T1 at 1.5 T, yielding a 95% confidence interval of (1077.7 +/- 3.5) ms.

Feature space analysis of MRI

This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre- and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.

Optimal linear transformation for MRI feature extraction

This paper presents development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. A three-dimensional (3-D) feature space representation of the data is generated in which normal tissues are clustered around prespecified target positions and abnormalities are clustered elsewhere. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, clusters are identified and regions of interest (ROI's) for normal and abnormal tissues are defined. These ROI's are used to estimate signature (prototype) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction and scene segmentation. Its relationship with discriminant analysis is discussed. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.

Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering

The eigenimage filter generates a composite image in which a desired feature is segmented from interfering features. The signal-to-noise ratio (SNR) of the eigenimage equals its contrast-to-noise ratio (CNR) and is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo (MSE); spin-echo (SE); inversion recovery (IR); and gradient-echo (GE). The authors use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function (normalized SNR of the eigenimage) in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints define a multidimensional nonlinear constrained optimization problem, which the authors solve by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. The authors show that the optimal pulse sequence parameters for a sequence of four MSE and one IR images almost doubles the smallest normalized SNR of the brain eigenimages, as compared to the conventional brain protocol.

Multispectral analysis of uterine corpus tumors in magnetic resonance imaging

The goal of this prospective study was to evaluate multispectral analysis techniques for automatic recognition of uterine cancer in magnetic resonance (MR) imaging. The first part of this study was an open training phase in which the statistical parameters of the various normal and pathological tissue types were estimated. This was followed by a test phase that was done as a blind experiment. Results from an extensive pathological examination of the surgically removed organs served as the reference for the diagnosis and various geometric measurements of the lesions. A radiological examination of the MR images was also performed. All malignant test tumors were correctly or close to correctly classified. However, parts of normal endometrium and other mucosal linings were also classified as adenocarcinomas. In addition, parts of some of the malignant tumors were classified as normal endometrium. The geometrical extension of the tumor and its relationship to the surroundings were slightly better predicted than those obtained by the radiologist. The results indicate that it is possible to differentiate and determine the local extension of some types of uterine malignancies based on the information present in MR images.

A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences

The performance of the eigenimage filter is compared with those of several other filters as applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. The conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.

Target-point combination of MR images

A method is described for combining multiple magnetic resonance images of the same anatomic slice to produce a single image which incorporates the favorable contrast features of each of the original images. The target-point method is a general method that includes linear combination as a subset and is designed to deal with the clinical need to maximize the contrast-to-noise ratio between several pairs of tissue simultaneously. Although it is intrinsically a nonlinear method, noise propagates approximately uniformly into the combined image. In examples of brain images the target-point method produces images with higher mutual contrast than the first principal component weighted sun image.

Optimization of survey protocols for MRI

A method for evaluating the sensitivity of MRI protocols to changes in an arbitrary number of tissue parameters over a broad range of parameter values is presented. This analysis is useful for choosing an optimal basis set of images for either a "survey" protocol or tissue segmentation algorithms. A survey protocol is required when searching for a lesion of unknown type or location. Segmentation requires unique signal signatures for tissues that may cover a broad range of tissue parameter values. Data acquisition is modeled as a mapping of a domain of tissue parameter values into a signal manifold in a signal strength space defined by the MRI protocol. The efficacy of the protocol is evaluated by investigating the characteristics of the signal manifold. A figure of merit which maximizes the probability of discriminating each point in the domain of tissue parameters from all others is developed.

Spin locking for magnetic resonance imaging with application to human breast

The dependence of rotating frame spin-lattice relaxation, T1 rho on locking field frequency, f1, was measured for phantom materials and human breast tissues. These data were used to predict the relative signal strengths obtainable in a spin-locking imaging sequence. This imaging sequence was implemented on a 0.15-T imaging system and measurements of phantom and tissue signal strength for various imaging parameters agreed with predicted signal strengths. Compared to T1 and T2, T1 rho appears to have unique capability to distinguish tumor from normal fat and fibrous breast tissues. The applications of T1 rho to tissue characterization and imaging at high static field strengths are discussed.