A simple method for the correction of endorectal surface coil inhomogeneity in prostate imaging

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter- and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, bias-corrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.

Intensity correction for multichannel hyperpolarized 13C imaging of the heart

PURPOSE

Develop and test an analytic correction method to correct the signal intensity variation caused by the inhomogeneous reception profile of an eight-channel phased array for hyperpolarized (13) C imaging.

THEORY AND METHODS

Fiducial markers visible in anatomical images were attached to the individual coils to provide three dimensional localization of the receive hardware with respect to the image frame of reference. The coil locations and dimensions were used to numerically model the reception profile using the Biot-Savart Law. The accuracy of the coil sensitivity estimation was validated with images derived from a homogenous (13) C phantom. Numerical coil sensitivity estimates were used to perform intensity correction of in vivo hyperpolarized (13) C cardiac images in pigs.

RESULTS

In comparison to the conventional sum-of-squares reconstruction, improved signal uniformity was observed in the corrected images.

CONCLUSION

The analytical intensity correction scheme was shown to improve the uniformity of multichannel image reconstruction in hyperpolarized [1-(13) C]pyruvate and (13) C-bicarbonate cardiac MRI. The method is independent of the pulse sequence used for (13) C data acquisition, simple to implement and does not require additional scan time, making it an attractive technique for multichannel hyperpolarized (13) C MRI.

Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI

Background

Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations.

Methods

In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.

Results

SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches.

Discussion

Experimental results using both phantom and patient data showed that ACER provided strong performance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing approaches.

Conclusions

A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.

Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma

Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.

Monte Carlo bias field correction in endorectal diffusion imaging

Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multiparametric magnetic resonance imaging (MRI) has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci, which is required to plan the treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multiparametric datasets. An endorectal receiver coil is often used to improve signal-to-noise ratio and aid in detection of smaller cancer foci. Moreover, computed high b-value diffusion-weighted imaging show improved delineation of tumors but is subject to strong bias fields near the coil. Here, a nonparametric approach to bias field correction for endorectal diffusion imaging via Monte Carlo sampling is introduced. It will be shown that the delineation between the prostate gland and the background and intensity inhomogeneity may be improved using the proposed approach. High b-value generated results also show improved visualization of tumor regions. The results suggest that Monte Carlo bias correction may have potential as a preprocessing tool for endorectal diffusion images for the prostate cancer detection and localization or segmentation.

Intensity correction method customized for multi-animal abdominal MR imaging with 3T clinical scanner and multi-array coil

PURPOSE

Simultaneous magnetic resonance (MR) imaging of multiple small animals in a single session increases throughput of preclinical imaging experiments. Such imaging using a 3-tesla clinical scanner with multi-array coil requires correction of intensity variation caused by the inhomogeneous sensitivity profile of the coil. We explored a method for correcting intensity that we customized for multi-animal MR imaging, especially abdominal imaging.

METHOD

Our institutional committee for animal experimentation approved the protocol. We acquired high resolution T₁-, T₂-, and T₂*-weighted images and low resolution proton density-weighted images (PDWIs) of 4 rat abdomens simultaneously using a 3T clinical scanner and custom-made multi-array coil. For comparison, we also acquired T₁-, T₂-, and T₂*-weighted volume coil images in the same rats in 4 separate sessions. We used software created in-house to correct intensity variation. We applied thresholding to the PDWIs to produce binary images that displayed only a signal-producing area, calculated multi-array coil sensitivity maps by dividing low-pass filtered PDWIs by low-pass filtered binary images pixel by pixel, and divided uncorrected T₁-, T₂-, or T₂*-weighted images by those maps to obtain intensity-corrected images. We compared tissue contrast among the liver, spinal canal, and muscle between intensity-corrected multi-array coil images and volume coil images.

RESULTS

Our intensity correction method performed well for all pulse sequences studied and corrected variation in original multi-array coil images without deteriorating the throughput of animal experiments. Tissue contrasts were comparable between intensity-corrected multi-array coil images and volume coil images.

CONCLUSION

Our intensity correction method customized for multi-animal abdominal MR imaging using a 3T clinical scanner and dedicated multi-array coil could facilitate image interpretation.

Intensity Inhomogeneity Correction of Magnetic Resonance Images using Patches

This paper presents a patch-based non-parametric approach to the correction of intensity inhomogeneity from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency coil, is usually manifested on the reconstructed MR image as a smooth shading effect. This artifact can significantly deteriorate the performance of any kind of image processing algorithm that uses intensities as a feature. Most of the current inhomogeneity correction techniques use explicit smoothness assumptions on the inhomogeneity field, which sometimes limit their performance if the actual inhomogeneity is not smooth, a problem that becomes prevalent in high fields. The proposed patch-based inhomogeneity correction method does not assume any parametric smoothness model, instead, it uses patches from an atlas of an inhomogeneity-free image to do the correction. Preliminary results show that the proposed method is comparable to N3, a current state of the art method, when the inhomogeneity is smooth, and outperforms N3 when the inhomogeneity contains non-smooth elements.

Intensity correction with a pair of spoiled gradient recalled echo images

Intensity inhomogeneities in magnetic resonance images (MRI) are a frequently occurring artefact, and result in the same tissue class to have vastly different intensities within an image. These inhomogeneities can be modelled by a slowly varying field, which is also called the bias field. Previous phantom-, image- or sequence based approaches suffer from long scan times, post-processing times or do not sufficiently remove the intensity variations. These intensity variations cause problems for quantitative image analysis algorithms (segmentation, registration) as well as clinicians (e.g. by complicating the visual assessment). This paper presents a novel technique (COIN, correction of intensity inhomogeneities) that uses two calibration images (fast spoiled gradient echo) to map a parameter containing the bias field, which is specific to the patient during a particular exam. This parametric map can then be used to correct any other images acquired during the same exam, regardless of the sequence employed. By using a short repetition time (less than 5 ms) for the calibration scans, the additional scan time is reduced to 60 s (max). The subsequent post-processing time is approximately 60 s per 20 slices. We successfully validate our approach on simulated brain MRI as well as real liver and spinal images. These images were acquired with a number of different coils, sequences and weightings. A comparison of our method with an existing, commercially available algorithm by radiologists shows that COIN is superior.

Correction of inhomogeneities in Magnetic Resonance Images

This paper presents a method by which two fast spoiled gradient echo image volumes can be used to estimate the slowly varying intensity variation frequently observed in Magnetic Resonance Images (MRI). We first present results for simulated brain MRI, where we compare the performance of a segmentation algorithm on uncorrected, corrected and artifact free images. Next, we present the results for a real uniform phantom and then for clinical spinal images, where our algorithm reduces the intensity variation within a tissue class from a factor of 30 to a factor of 2. The technique and algorithm has been successfully tested on over 1000 images acquired with different coils, sequences and anatomies.

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information

A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented. This approach is based on the simultaneous use of spatial and gray-level histogram information. Spatial information about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed correction method is illustrated using both physical phantom and human brain images. The results are consistent with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems. They are all the more significant as the ground truth on intensity nonuniformity is unknown in clinical images.

MR Imaging and MR Spectroscopy in Prostate Cancer Management

Currently, endorectal coil MR imaging has the ability to improve accuracy in staging of localized prostate cancer. The addition of MR spectroscopic imaging has further improved the sensitivity of MR imaging for intraprostatic tumor localization. Additional refinements and techniques are expected to further improve the performance of MR imaging for prostate cancer imaging and to aid in patient management. Further studies are required to identify the ideal role for MR imaging in the diagnosis and management of prostate cancer.

Intensity non-uniformity correction in MRI: existing methods and their validation

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

Comparison of altered signal intensity, position, and morphology of the TMJ disc in MR images corrected for variations in surface coil sensitivity

OBJECTIVE

The purpose of this study is to evaluate the corrections of signal intensity of the temporomandibular joint (TMJ) disc caused by variations in sensitivity of the magnetic resonance imaging (MRI) surface coil, to compare the modified signal intensities of the posterior and anterior bands, and then to evaluate the relationship of the signal intensity difference to altered disc position and morphology in a group of TMJ patients.

STUDY DESIGN

MRI was performed on 96 joints. All patients underwent imaging in axial, coronal, and sagittal planes using fast-spin echo sequences (FSE). The images were taken in the closed, partially opened, and maximum opened mouth positions in 2 sequences. Classifications were made according to the position and morphology of the disc. TMJs were divided into normal, anterior disc displacement with reduction (ADDwR), anterior disc displacement without reduction (ADDwoR), and partial anterior disc displacement with reduction (PDDwR). Disc morphology was subdivided as biconcave, lengthened, biconvex, thick posterior band, and others (defined as folded and rounded). The correction of the inhomogeneous sensitivity of the surface coil was done with the original software. The signal intensities (SI) of the posterior band and anterior band of TMJ discs were measured. The correlations among the groups of TMJs and disc morphologies and SI were statistically analyzed by using Bonferroni/Dunn multicomparison method test.

RESULTS

Of the total number of joints studied with the help of MRI, 37 were normal, 12 exhibited ADDwR, 32 ADDwoR, and 9 PDDwR. The corrected MR images indicated that SI of the posterior bands were higher than the anterior band of the discs. It can also be concluded that the SI of the posterior bands increased significantly in the following order: normal, PDDwR, ADDwR, and ADDwoR, while there is no statistical difference in the SI of the anterior band of the discs. In ADDwR and ADDwoR, thick posterior band is the most common shape. In normal TMJ, the biconcave shape is identified as the most frequently encountered shape.

CONCLUSIONS

It was demonstrated that the SI of the posterior bands increase with the progress of internal derangement, and was found to be higher than that of the anterior band of the discs. It appears that disc degeneration starts from the posterior band of the disc.

MR imaging of the prostate at 3 Tesla: comparison of an external phased-array coil to imaging with an endorectal coil at 1.5 Tesla

RATIONALE AND OBJECTIVES

To qualitatively compare the image quality of torso phased-array 3-Tesla (3T) imaging of the prostate with that of endorectal 1.5-Tesla imaging.

MATERIALS AND METHODS

Twenty cases of torso phased-array prostate imaging performed at 3-Tesla with FSE T2 weighted images were evaluated by two readers independently for visualization of the posterior border (PB), seminal vesicles (SV), neurovascular bundles (NVB), and image quality rating (IQR). Studies were performed at large fields of view(FOV) (25 cm) (14 cases) (3TL) and smaller FOV (14 cm) (19 cases) (3TS). A comparison was made to 20 consecutive cases of 1.5-T endorectal evaluation performed during the same time period.Results. 3TL produced a significantly better image quality compared with the small FOV for PB (P = .0001), SV (P =.0001), and IQR (P = .0001). There was a marginally significant difference within the NVB category (P = .0535). 3TL produced an image of similar quality to image quality at 1.5 T for PB (P = .3893), SV (P = .8680), NB (P = .2684), and IQR (P = .8599).

CONCLUSION

Prostate image quality at 3T with a torso phased-array coil can be comparable with that of endorectal 1.5-T imaging. These findings suggest that additional options are now available for magnetic resonance imaging of the prostate gland.

A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction

We evaluate a wavelet-based algorithm to estimate the coil sensitivity modulation from surface coils. This information is used to improve the image homogeneity of magnetic resonance imaging when a surface coil is used for reception, and to increase image encoding speed by reconstructing images from under-sampled (aliased) acquisitions using parallel magnetic resonance imaging (MRI) methods for higher spatiotemporal image resolutions. The proposed algorithm estimates the spatial sensitivity profile of surface coils from the original anatomical images directly without using the body coil for additional reference scans or using coil position markers for electromagnetic model-based calculations. No prior knowledge about the anatomy is required for the application of the algorithm. The estimation of the coil sensitivity profile based on the wavelet transform of the original image data was found to provide a robust method for removing the slowly varying spatial sensitivity pattern of the surface coil image and recovering full FOV images from two-fold acceleration in 8-channel parallel MRI. The results, using bi-orthogonal Daubechies 97 wavelets and other members in this family, are evaluated for T1-weighted and T2-weighted brain imaging.

Endoscopically inserted endoluminal receiver coil for high-resolution magnetic resonance imaging of the pancreas: Initial results in an animal model

BACKGROUND

This study assessed the feasibility of high-resolution magnetic resonance imaging of the pancreas by means of an endoscopically inserted endoluminal magnetic resonance receiver coil.

METHOD

A 0.032-inch diameter internal magnetic resonance imaging receiver coil was endoscopically inserted into the pancreatic duct in 4 pigs through the accessory channel of a standard duodenoscope to obtain high-resolution magnetic resonance images by using T1- and T2-weighted sequences.

RESULTS

The pig anatomy precluded the usual transoral approach; however, transgastric access allowed endoscopic transpapillary insertion of a receiver coil into the pancreatic duct in all animals without the need for sphincterotomy. The small swine pancreas could then be visualized by magnetic resonance imaging with a 0.3 x 0.3-mm in-plane resolution.

CONCLUSION

High-resolution pancreas magnetic resonance imaging is feasible by using an endoscopically inserted endoluminal receiver coil. The smaller stomach and larger pancreatic duct diameter in humans will facilitate clinical application of the imaging procedure.

A simple method to improve image nonuniformity of brain MR images at the edges of a head coil

One of the major sources of image nonuniformity in the high field MR scanners is the radiofrequency (RF) coil inhomogeneity. It degrades conspicuity of lesion(s) in the MR images of the brain and surrounding tissues and reduces accuracy of image postprocessing particularly at the edges of the coil. In this investigation, we have devised and tested a simple method to correct for nonuniformity of MR images of the brain at the edges of the RF head coil. Initially, a cylindrical oil phantom, which fit exactly in the head coil, was scanned on a 1.5 T imager. Then, a correction algorithm identified a reference pixel value in the phantom at the most homogeneous region of the RF coil. Next, every pixel inside the phantom was normalized relative to this reference value. The resulting set of coefficients or "correction matrices" was obtained for different types of MR contrast agent. Finally, brain MR images of normal subjects and multiple sclerosis patients were acquired and processed by the corresponding correction matrices obtained with different pulse sequences. Application of correction matrices to brain MR images showed a gain in pixel intensity particularly in the slices at the edge of the coil.

Fast magnetic resonance imaging techniques

This article reviews fast magnetic resonance (MR) techniques currently used for body imaging. Improvements in gradient performance have made very short repetition and echo times on clinical scanners feasible, thus enabling subsecond image acquisition. The article provides a fundamental overview of the technical aspects from the concept of k-space and k-space segmentation technique, fast MR imaging techniques including fast spin echo, fast gradient echo with or without magnetization preparation to echo planar and hybrid techniques. The article also addresses the use of different fat suppression techniques in MR imaging of the body and improvements in coil technology to obtain faster images and higher signal-to-noise.