Artifacts and pitfalls in diffusion MRI

Correction of vibration artifacts in DTI using phase-encoding reversal (COVIPER)

Diffusion tensor imaging is widely used in research and clinical applications, but still suffers from substantial artifacts. Here, we focus on vibrations induced by strong diffusion gradients in diffusion tensor imaging, causing an echo shift in k-space and consequential signal-loss. We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down). We implemented a correction of vibration artifacts in diffusion tensor imaging using phase-encoding reversal (COVIPER) by combining blip-up and blip-down images, each weighted by a function of its local tensor-fit error. COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps. COVIPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.

Combining diffusion-weighted MRI with Gd-EOB-DTPA-enhanced MRI improves the detection of colorectal liver metastases

OBJECTIVES

To compare the diagnostic accuracy of gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced MRI, diffusion-weighted MRI (DW-MRI) and a combination of both techniques for the detection of colorectal hepatic metastases.

METHODS

72 patients with suspected colorectal liver metastases underwent Gd-EOB-DTPA MRI and DW-MRI. Images were retrospectively reviewed with unenhanced T(1) and T(2) weighted images as Gd-EOB-DTPA image set, DW-MRI image set and combined image set by two independent radiologists. Each lesion detected was scored for size, location and likelihood of metastasis, and compared with surgery and follow-up imaging. Diagnostic accuracy was compared using receiver operating characteristics and interobserver agreement by kappa statistics.

RESULTS

417 lesions (310 metastases, 107 benign) were found in 72 patients. For both readers, diagnostic accuracy using the combined image set was higher [area under the curve (Az)=0.96, 0.97] than Gd-EOB-DTPA image set (Az=0.86, 0.89) or DW-MRI image set (Az=0.93, 0.92). Using combined image set improved identification of liver metastases compared with Gd-EOB-DTPA image set (p<0.001) or DW-MRI image set (p<0.001). There was very good interobserver agreement for lesion classification (κ=0.81-0.88).

CONCLUSIONS

Combining DW-MRI with Gd-EOB-DTPA-enhanced T(1) weighted MRI significantly improved the detection of colorectal liver metastases.

Automated delineation of white matter fiber tracts with a multiple region-of-interest approach

White matter fiber bundles of the brain can be delineated by tractography utilizing multiple regions-of-interest (MROI) defined by anatomical landmarks. These MROI can be used to specify regions in which to seed, select, or reject tractography fibers. Manual identification of anatomical MROI enables the delineation of white matter fiber bundles, but requires considerable training to develop expertise, considerable time to carry out and suffers from unwanted inter- and intra-rater variability. In a study of 20 healthy volunteers, we compared three methodologies for automated delineation of the white matter fiber bundles. Using these methodologies, fiber bundle MROI for each volunteer were automatically generated. We assessed three strategies for inferring the automatic MROI utilizing nonrigid alignment of reference images and projection of template MROI. We assessed the bundle delineation error associated with alignment utilizing T1-weighted MRI, fractional anisotropy images, and full tensor images. We confirmed the smallest delineation error was achieved using the full tensor images. We then assessed three projection strategies for automatic determination of MROI in each volunteer. Quantitative comparisons were made using the root-mean-squared error observed between streamline density images constructed from fiber bundles identified automatically and by manually drawn MROI in the same subjects. We demonstrate that a multiple template consensus label fusion algorithm generated fiber bundles most consistent with the manual reference standard.

A quality assurance protocol for diffusion tensor imaging using the head phantom from American College of Radiology

PURPOSE

To propose a quality assurance procedure for routine clinical diffusion tensor imaging (DTI) using the widely available American College of Radiology (ACR) head phantom.

METHODS

Analysis was performed on the data acquired at 1.5 and 3.0 T on whole body clinical MRI scanners using the ACR phantom and included the following: (1) the signal-to-noise ratio (SNR) at the center and periphery of the phantom, (2) image distortion by EPI readout relative to spin echo imaging, (3) distortion of high-b images relative to the b= 0 image caused by diffusion encoding, and (4) determination of fractional anisotropy (FA) and mean diffusivity (MD) measured with region-of-interest (ROI) and pixel-based approaches. Reproducibility of the measurements was assessed by five repetitions of data acquisition on each scanner.

RESULTS

The SNR at the phantom center was approximately half of that near the periphery at both 1.5 and 3 T. The image distortion by the EPI readout was up to 7 mm at 1.5 T and 10 mm at 3 T. The typical distortion caused by eddy currents from diffusion encoding was on the order of 0.5 mm. The difference between ROI-based and pixel-based MD quantification was 1.4% at 1.5 T and 0.3% at 3 T. The ROI-based MD values were in close agreement (within 2%) with the reference values. The ROI-based FA values were approximately a factor of 10 smaller than pixel-based values and less than 0.01. The measurement reproducibility was sufficient for quality assurance (QA) purposes.

CONCLUSIONS

This QA approach is simple to perform and evaluates key aspects of the scanner performance for DTI data acquisition using a widely available phantom.

Correlating magnetic resonance findings with neuropathology and clinical signs in dogs and cats

The histologic characteristics that are the basis for diagnosis of central nervous system conditions cannot be visualized directly using magnetic resonance (MR) methods, but clinical diagnosis may be based on the frequency and pattern of MR imaging signs, which represent predominantly the gross morphologic features of lesions. Additional quantitative MR measures of myelination, cell swelling, gliosis, and neuronal loss may also be used for more specific characterization of lesions. These measures include magnetization transfer ratio, apparent diffusion coefficient, and the concentrations or ratios of metabolites identified by spectroscopy. Confidence that an MR abnormality is responsible for the clinical signs depends primarily on the degree of correspondence between the site of the lesion and the neuroanatomical localization.

Determination of the cutoff level of apparent diffusion coefficient values for detection of prostate cancer

PURPOSE

The aim of this study was to determine the cutoff level of apparent diffusion coefficient (ADC) values for diagnosing prostate cancer.

MATERIALS AND METHODS

A total of 45 consecutive patients with prostate cancer who underwent diffusion-weighted magnetic resonance imaging (MRI) with ADC maps before radical prostatectomy were included in this retrospective study. MRI findings were correlated retrospectively with histopathological results of surgical specimens. Comparisons of ADC values between cancer and noncancer areas were performed with the two-tailed unequal variance t-test. The cutoff ADC level was determined in a way to achieve the best accuracy for detecting prostate cancer.

RESULTS

The mean ADC value of all the cancer lesions (n =60) was 1.04 ± 0.31 (×10(-3) mm(2)/s). In the peripheral zone, the mean ADC values of cancer lesions and noncancer areas were 1.07 ± 0.35 and 1.94 ± 0.31, respectively (P < 0.001). In the transition zone, the mean ADC values of cancer lesions and noncancer areas were 1.00 ± 0.22 and 1.56 ± 0.14, respectively (P<0.001). The cutoff level for the ADC value was determined to be 1.35×10(-3) mm(2)/s. It provided sensitivity, specificity, and accuracy of 88%, 96%, and 93%, respectively.

CONCLUSION

The cutoff ADC level determined on the basis of the results obtained from radical prostatectomy specimens can help differentiate malignant from nonmalignant lesions.

Diffusion-weighted imaging of the abdomen at 3 T: image quality comparison with 1.5-T magnet using 3 different imaging sequences

OBJECTIVE

This study aimed to perform comparisons between diffusion-weighted imaging (DWI) sequences at 3 T with 1.5 T.

METHODS

Thirteen healthy volunteers underwent abdominal DWI on both 3- and 1.5-T magnets using 3 sequences including breath hold without parallel imaging (PI), breath hold with PI, and free breathing with PI at b50 and b1000. Artifacts and subjective image quality scores, signal intensity, and apparent diffusion coefficient were compared.

RESULTS

For breath hold without PI, higher artifact was noted at 3 T b50 compared with 1.5 T (P < 0.0001). For b50 and b1000 breath hold with PI, artifacts were not different between the magnets, but image quality was better at 3 T (P = 0.04 and P = 0.02, respectively). For b50 and b1000 free breathing sequences, artifact and image quality scores were significantly better at 1.5 T. For breath hold acquisitions, the signal-to-noise ratio of gallbladder, kidneys, and pancreas was generally higher and that of the liver was lower on 3 T. Imaging at 3 T showed significantly higher image quality and lower artifacts for breath hold with PI compared with free breathing. Most apparent diffusion coefficients were not significantly different between the 2 magnets (P > 0.05).

CONCLUSIONS

Three-tesla magnets can provide good images using breath hold with PI sequence.

Diffusion tractography: methods, validation and applications in patients with neurosurgical lesions

Diffusion tensor imaging (DTI) tractography is increasingly used in presurgical mapping in tumors located in eloquent areas since it is the only non invasive technique that permits in vivo dissection of white matter tracts. Concordance between the DTI tracts and subcortical electrical intraoperative mapping is high, and DTI tractography has proven useful to guide surgery. However, it presents limitations due to the technique and the tumor, which must be known before using the images in the operative room. This review focuses on the possibilities and limits of DTI imaging in intraoperative tumoral mapping and presents an overview of current knowledge.

Diffusion imaging with prospective motion correction and reacquisition

A major source of artifacts in diffusion-weighted imaging is subject motion. Slow bulk subject motion causes misalignment of data when more than one average or diffusion gradient direction is acquired. Fast bulk subject motion can cause signal dropout artifacts in diffusion-weighted images and results in erroneous derived maps, e.g., fractional anisotropy maps. To address both types of artifacts, a fully automatic method is presented that combines prospective motion correction with a reacquisition scheme. Motion correction is based on the prospective acquisition correction method modified to work with diffusion-weighted data. The images to reacquire are determined automatically during the acquisition from the imaging data, i.e., no extra reference scan, navigators, or external devices are necessary. The number of reacquired images, i.e., the additional scan duration can be adjusted freely. Diffusion-weighted prospective acquisition correction corrects slow bulk motion well and reduces misalignment artifacts like image blurring. Mean absolute residual values for translation and rotation were <0.6 mm and 0.5°. Reacquisition of images affected by signal dropout artifacts results in diffusion maps and fiber tracking free of artifacts. The presented method allows the reduction of two types of common motion related artifacts at the cost of slightly increased acquisition time.

The use of diffusion tensor imaging to evaluate the spinal cord in normal and abnormal dogs

Diffusion tensor imaging (DTI) is a specialized magnetic resonance sequence to determine the direction of water molecule motion. Our hypothesis was that information derived from DTI will be significantly different in dogs with a spinal cord lesion compared with a normal dog. Eleven normal dogs and six dogs with a spinal cord lesions were imaged. DTI was performed along with standard T1- and T2-weighted sequences in transverse and sagittal planes. Fractional anisotrophy and apparent diffusion coefficient (ADC) were obtained using regions of interests centered on the cranial aspect, middle cranial, middle caudal, and caudal aspects of the spinal cord. In normal dogs, the DTI sequence was characterized by normal fiber tracking with no statistical difference between the four sections of spinal cord (P>0.05). In the dogs with a spinal cord lesion, there was a significant difference in fractional anisotropy between the two groups (P=0.0003) and the ADC analysis statistical significance (P=0.048) at the caudal most site. Based on these findings, DTI is a potentially useful method to evaluate the spinal cord in dogs.

The challenges of integrating molecular imaging into the optimization of cancer therapy

We review novel, in vivo and tissue-based imaging technologies that monitor and optimize cancer therapeutics. Recent advances in cancer treatment centre around the development of targeted therapies and personalisation of treatment regimes to individual tumour characteristics. However, clinical outcomes have not improved as expected. Further development of the use of molecular imaging to predict or assess treatment response must address spatial heterogeneity of cancer within the body. A combination of different imaging modalities should be used to relate the effect of the drug to dosing regimen or effective drug concentration at the local site of action. Molecular imaging provides a functional and dynamic read-out of cancer therapeutics, from nanometre to whole body scale. At the whole body scale, an increase in the sensitivity and specificity of the imaging probe is required to localise (micro)metastatic foci and/or residual disease that are currently below the limit of detection. The use of image-guided endoscopic biopsy can produce tumour cells or tissues for nanoscopic analysis in a relatively patient-compliant manner, thereby linking clinical imaging to a more precise assessment of molecular mechanisms. This multimodality imaging approach (in combination with genetics/genomic information) could be used to bridge the gap between our knowledge of mechanisms underlying the processes of metastasis, tumour dormancy and routine clinical practice. Treatment regimes could therefore be individually tailored both at diagnosis and throughout treatment, through monitoring of drug pharmacodynamics providing an early read-out of response or resistance.

An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets

Diffusion tensor (DT) imaging and related multifiber reconstruction algorithms allow the study of in vivo microstructure and, by means of tractography, structural connectivity. Although reconstruction algorithms are promising imaging tools, high-quality diffusion-weighted imaging (DWI) datasets for verification and validation of postprocessing and analysis methods are lacking. Clinical in vivo DWI is limited by, for example, physiological noise and low signal-to-noise ratio. Here, we performed a series of DWI measurements on postmortem pig brains, which resemble the human brain in neuroanatomical complexity, to establish an ex vivo imaging pipeline for generating high-quality DWI datasets. Perfusion fixation ensured that tissue characteristics were comparable to in vivo conditions. There were three main results: (i) heat conduction and unstable tissue mechanics accounted for time-varying artefacts in the DWI dataset, which were present for up to 15 h after positioning brain tissue in the scanner; (ii) using fitted DT, q-ball, and persistent angular structure magnetic resonance imaging algorithms, any b-value between ∼2,000 and ∼8,000 s/mm(2) , with an optimal value around 4,000 s/mm(2) , allowed for consistent reconstruction of fiber directions; (iii) diffusivity measures in the postmortem brain tissue were stable over a 3-year period. On the basis of these results, we established an optimized ex vivo pipeline for high-quality and high-resolution DWI. The pipeline produces DWI data sets with a high level of tissue structure detail showing for example two parallel horizontal rims in the cerebral cortex and multiple rims in the hippocampus. We conclude that high-quality ex vivo DWI can be used to validate fiber reconstruction algorithms and to complement histological studies.

Correlating quantitative MR imaging with histopathology in X-linked adrenoleukodystrophy

BACKGROUND AND PURPOSE

Quantitative MR imaging techniques may improve the pathologic specificity of MR imaging regarding white matter abnormalities. Our purposes were to determine whether ADC, FA, MTR, and MRS metabolites correlate with the degree of white matter damage in patients with X-ALD; whether differences in ADC, FA, and MTR observed in vivo are retained in fresh and formalin-fixed postmortem brain tissue; and whether the differences predict histopathology.

MATERIALS AND METHODS

MRS metabolites, MTR, ADC, and FA, were determined in 7 patients with X-ALD in 3 white matter areas (NAWM, active demyelination, and complete demyelination) and were compared with values obtained in 14 controls. MTR, ADC, and FA were assessed in postmortem brains from 15 patients with X-ALD and 5 controls. Values were correlated with the degree of astrogliosis and density of myelin, axons, and cells. Equations to estimate histopathology from MR imaging parameters were calculated by linear regression analysis.

RESULTS

MRS showed increased mIns, Lac, and Cho and decreased tNAA in living patients with X-ALD; the values depended on the degree of demyelination. MTR, ADC, and FA values were different in postmortem than in vivo white matter, but differences related to degrees of white matter damage were retained. ADC was high and FA and MTR were low in abnormal white matter. Correlations between histopathologic findings and MR imaging parameters were strong. A combination of ADC and FA predicted pathologic parameters best.

CONCLUSIONS

Changes in quantitative MR imaging parameters, present in living patients and related to the severity of white matter pathology, are retained in postmortem brain tissue. MR imaging parameters predict white matter histopathologic parameters.

Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies.

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.

A fast random walk algorithm for computing the pulsed-gradient spin-echo signal in multiscale porous media

A new method for computing the signal attenuation due to restricted diffusion in a linear magnetic field gradient is proposed. A fast random walk (FRW) algorithm for simulating random trajectories of diffusing spin-bearing particles is combined with gradient encoding. As random moves of a FRW are continuously adapted to local geometrical length scales, the method is efficient for simulating pulsed-gradient spin-echo experiments in hierarchical or multiscale porous media such as concrete, sandstones, sedimentary rocks and, potentially, brain or lungs.

Effect of intravenous extracellular gadolinium based contrast medium on renal diffusion weighted images

RATIONALE AND OBJECTIVES

The aim of this study was to compare precontrast and postcontrast renal diffusion-weighted images for signal intensity (SI), apparent diffusion coefficient (ADC), and lesion conspicuity.

MATERIALS AND METHODS

In 62 patients (mean age, 54 ± 29; 29 men, 33 women) precontrast and postcontrast (0.1 mmol/kg of extracellular gadolinium-based contrast medium; mean, 3.3 ± 0.9 minutes], diffusion-weighted images at b values of 50 and 400 s/mm² were compared (3 T). The SI, signal-to-noise ratio, and ADC of the renal cortex, medulla, and lesions were measured. Lesion contrast-to-noise ratios (against the medulla and cortex) were calculated.

RESULTS

Postcontrast medullary SI decreased by 50% and cortical SI decreased by 33% and 39% on images at b = 50 s/mm² and b = 400 s/mm², respectively (P < .0001). The SI and signal-to-noise ratio of lesions did not change significantly after contrast, but lesion-medullary contrast-to-noise ratio was increased by 50% at both b = 50 s/mm² and b = 400 s/mm² (P < .005 and P = .0005, respectively) following contrast. Qualitative postcontrast lesion conspicuity was improved, with average scores of 2.8 ± 0.9 for all lesions (κ = 0.7 ± 0.08) and 3.2 ± 0.9 for solid lesions (κ = 0.82 ± 0.1). The ADC of renal cortex decreased (P = .03), but the ADC of renal medulla or renal lesions did not significantly change.

CONCLUSION

Postcontrast diffusion-weighted imaging causes a significant decrease in renal parenchymal signal without a significant change in lesion signal, resulting in increased lesion conspicuity.

Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors

OBJECTIVE

The purpose of our study was to evaluate the diagnostic value of an imaging protocol that combines dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with suspicious breast lesions and to determine if additional information provided by DWI improves the diagnostic value of breast MRI.

MATERIALS AND METHODS

Eighty-four patients with breast tumors (37 benign, 47 malignant) underwent DCE-MRI and DWI before biopsy. Morphologic and kinetic analyses were performed on DCE-MRI and findings were classified according to the BI-RADS lexicon. Apparent diffusion coefficient (ADC) values were calculated from the DWI. The ADCs of the benign and malignant lesions were compared. For the combined MRI protocol, morphologic kinetic features and ADCs were evaluated together. Diagnostic values of DCE-MRI, DWI, and combined MRI were calculated.

RESULTS

Median ADCs of the benign and malignant lesions were 1.26 × 10(-3) mm(2)/s and 0.75 × 10(-3) mm(2)/s, respectively. Cutoff value of 0.92 × 10(-3) mm(2)/s for ADC provided 91.5% sensitivity and 86.5% specificity. DCE-MRI alone showed 97.9% sensitivity and 75.7% specificity. The combination of DCE-MRI with DWI provided 95.7% sensitivity and 89.2% specificity. The specificity of breast MRI improved by 13.5% (p = 0.063) without a significant decrease in the sensitivity (p = 1.000).

CONCLUSION

The combination of DWI and DCE-MRI has the potential to increase the specificity of breast MRI.

Spinal cord - MR of rodent models

Different MR techniques, such as relaxation times, diffusion, perfusion, and spectroscopy have been employed to study rodent spinal cord. In this chapter, a description of these methods is given, along with examples of normal metrics that can be derived from the MR acquisitions, as well as examples of applications to pathology.

PCATMIP: enhancing signal intensity in diffusion-weighted magnetic resonance imaging

Diffusion-weighted MRI studies generally lose signal intensity to physiological motion, which can adversely affect quantification/diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In this article, PCATMIP, a combined principal component analysis and temporal maximum intensity projection approach, is developed to address this problem. Data are first acquired for a fixed number of repetitions. Assuming that physiological fluctuations of image intensities locally are likely temporally correlated unlike random noise, a local moving boxcar in the spatial domain is used to reconstruct low-noise images by considering the most relevant principal components in the temporal domain. Subsequently, a temporal maximum intensity projection yields a high signal-intensity image. Numerical and experimental studies were performed for validation and to determine optimal parameters for increasing signal intensity and minimizing noise. Subsequently, a combined principal component analysis and temporal maximum intensity projection approach was used to analyze diffusion-weighted porcine liver MRI scans. In these scans, the variability of apparent diffusion coefficient values among repeated measurements was reduced by 59% relative to averaging, and there was an increase in the signal intensity with higher intensity differences observed at higher b-values. In summary, a combined principal component analysis and temporal maximum intensity projection approach is a postprocessing approach that corrects for bulk motion-induced signal loss and improves apparent diffusion coefficient measurement reproducibility.

Removal of olefinic fat chemical shift artifact in diffusion MRI

Diffusion-weighted (DW) MRI has emerged as a key tool for assessing the microstructure of tissues in healthy and diseased states. Because of its rapid acquisition speed and insensitivity to motion, single-shot echo-planar imaging is the most common DW imaging technique. However, the presence of fat signal can severely affect DW-echo planar imaging acquisitions because of the chemical shift artifact. Fat suppression is usually achieved through some form of chemical shift-based fat saturation. Such methods effectively suppress the signal originating from aliphatic fat protons, but fail to suppress the signal from olefinic protons. Olefinic fat signal may result in significant distortions in the DW images, which bias the subsequently estimated diffusion parameters. This article introduces a method for removing olefinic fat signal from DW images, based on an echo time-shifted acquisition. The method is developed and analyzed specifically in the context of single-shot DW-echo-planar imaging, where image phase is generally unreliable. The proposed method is tested with phantom and in vivo datasets, and compared with a standard acquisition to demonstrate its performance.

The effect of gadoxetic acid enhancement on lesion detection and characterisation using T₂ weighted imaging and diffusion weighted imaging of the liver

OBJECTIVES

To evaluate the effect of gadoxetic acid enhancement on the detection and characterisation of focal hepatic lesions on T(2) weighted and diffusion weighted (DW) images.

METHODS

A total of 63 consecutive patients underwent T(2) weighted and DW imaging before and after gadoxetic acid enhancement. Two blinded readers independently identified all of the focal lesions using a five-point confidence scale and characterised each lesion using a three-point scale: 1, non-solid; 2, indeterminate; and 3, solid. For both T(2) weighted and DW imaging, the accuracies for detecting focal lesions were compared using the free-response receiver operating characteristic analysis; the accuracies for lesion characterisation were compared using the McNemar test between non-enhanced and gadoxetic acid-enhanced image sets. For hepatic lesions ≥ 1 cm, the lesion-to-liver contrast-to-noise ratio (CNR) and the apparent diffusion coefficient (ADC) were compared in the non-enhanced and enhanced image sets using the generalised estimating equations.

RESULTS

For both T(2) weighted and DW images, the accuracies for detecting focal lesions (p ≥ 0.52) and those for lesion characterisation (p ≥ 0.63) did not differ significantly between the non-enhanced and enhanced image sets. The lesion-to-liver CNR was significantly higher on enhanced DW images than on non-enhanced DW images (p=0.02), although the difference was not significant for T(2) weighted imaging (p=0.65). The mean ADC values of lesions did not differ significantly on enhanced and non-enhanced DW imaging (p=0.75).

CONCLUSION

The acquisition of T(2) weighted and DW images after administration of gadoxetic acid has no significant effect on the detection or characterisation of focal hepatic lesions, although it improves the lesion-to-liver CNR on DW images.

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.

Analyzing diffusion tensor images with ghosting artifacts: the effects of direct and indirect normalization

The current study aims to assess the applicability of direct or indirect normalization for the analysis of fractional anisotropy (FA) maps in the context of diffusion-weighted images (DWIs) contaminated by ghosting artifacts. We found that FA maps acquired by direct normalization showed generally higher anisotropy than indirect normalization, and the disparities were aggravated by the presence of ghosting artifacts in DWIs. The voxel-wise statistical comparisons demonstrated that indirect normalization reduced the influence of artifacts and enhanced the sensitivity of detecting anisotropy differences between groups. This suggested that images contaminated with ghosting artifacts can be sensibly analyzed using indirect normalization.

Love songs, bird brains and diffusion tensor imaging

The song control system of songbirds displays a remarkable seasonal neuroplasticity in species in which song output also changes seasonally. Thus far, this song control system has been extensively analyzed by histological and electrophysiological methods. However, these approaches do not provide a global view of the brain and/or do not allow repeated measurements, which are necessary to establish causal correlations between alterations in neural substrate and behavior. Research has primarily been focused on the song nuclei themselves, largely neglecting their interconnections and other brain regions involved in seasonally changing behavior. In this review, we introduce and explore the song control system of songbirds as a natural model for brain plasticity. At the same time, we point out the added value of the songbird brain model for in vivo diffusion tensor techniques and its derivatives. A compilation of the diffusion tensor imaging (DTI) data obtained thus far in this system demonstrates the usefulness of this in vivo method for studying brain plasticity. In particular, it is shown to be a perfect tool for long-term studies of morphological and cellular changes of specific brain circuits in different endocrine/photoperiod conditions. The method has been successfully applied to obtain quantitative measurements of seasonal changes of fiber tracts and nuclei from the song control system. In addition, outside the song control system, changes have been discerned in the optic chiasm and in an interhemispheric connection. DTI allows the detection of seasonal changes in a region analogous to the mammalian secondary auditory cortex and in regions of the 'social behavior network', an interconnected group of structures that controls multiple social behaviors, including aggression and courtship. DTI allows the demonstration, for the first time, that the songbird brain in its entirety exhibits an extreme seasonal plasticity which is not merely limited to the song control system as was generally believed.

[Diffusion-weighted MR imaging of the spine and cord]

Due to its excellent sensitivity, MR imaging is invaluable for the evaluation of lesions of the cord and spine. Several studies dedicated to diffusion-weighted MR evaluation of the cord and spine have been published. While diffusion-weighted MR imaging of the brain is routinely performed, it is seldom performed when imaging the spine due to serious limitations. While anatomical limitations may not be changed, the voxel size, phase-encoding direction, mode of k-space filling, and acceleration factor are all parameters that can be optimized in order to routinely obtain diffusion-weighted imaging of the spine on 1.5T and 3T scanners.

MR Imaging of Prostate Cancer: Diffusion Weighted Imaging and (3D) Hydrogen 1 (H) MR Spectroscopy in Comparison with Histology

Purpose. To evaluate retrospectively the impact of diffusion weighted imaging (DWI) and (3D) hydrogen 1 (¹H) MR-spectroscopy (MRS) on the detection of prostatic cancer in comparison to histological examinations. Materials and Methods: 50 patients with suspicion of prostate cancer underwent a MRI examination at a 1.5T scanner. The prostate was divided into sextants. Regions of interest were placed in each sextant to evaluate the apparent diffusion coefficient (ADC)-values. The results of the DWI as well as MRS were compared retrospectively with the findings of the histological examination. Sensitivity and specificity of ADC and metabolic ratio (MET)—both separately and in combination—for identification of tumor tissue was computed for variable discrimination thresholds to evaluate its receiver operator characteristic (ROC). An association between ADC, MET and Gleason score was tested by the non-parametric Spearman ρ-test. Results. The average ADC-value was 1.65 ± 0.32mm²/s × 10⁻³ in normal tissue and 0.96±0.24 mm²/s × 10⁻³ in tumor tissue (mean ± 1 SD). MET was 0.418 ± 0.431 in normal tissue and 2.010 ± 1.649 in tumor tissue. The area under the ROC curve was 0.966 (95%-confidence interval 0.941–0.991) and 0.943 (0.918–0.968) for DWI and MRS, respectively. There was a highly significant negative correlation between ADC-value and the Gleason score in the tumor-positive tissue probes (n = 62, ρ = −0.405, P = .001). MRS did not show a significant correlation with the Gleason score (ρ = 0.117, P = .366). By using both the DWI and MRS, the regression model provided sensitivity and specificity for detection of tumor of 91.9% and 98.3%, respectively. Conclusion. The results of our study showed that both DWI and MRS should be considered as an additional and complementary tool to the T2-weighted MRI for detecting prostate cancer.

Role of PROPELLER diffusion-weighted imaging and apparent diffusion coefficient in the evaluation of pituitary adenomas

OBJECTIVE

The relationship between tumor consistency and apparent diffusion coefficient (ADC) values is controversial. We evaluated the role of the ADC using an advanced diffusion-weighted imaging (DWI) technique. We employed periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) DWI acquired on a 3-T magnetic resonance imaging (MRI) scanner to assess the consistency of pituitary adenomas and examined the relationship between the ADC and the hormone secretion status of the tumors and their MIB-1 labeling index (MIB-1 LI).

MATERIALS AND METHODS

The study protocol was approved by our institutional review board. We retrospectively studied 24 operated patients with pituitary adenomas who had undergone PROPELLER DWI on a 3-T MRI scanner. Conventional MRI findings were expressed as the ratio of the signal intensity (SI) in the lesions to the SI of the normal white matter and the degree of contrast enhancement. Minimum-, mean-, and maximum ADC (ADCmin, ADCmean, ADCmax) values were calculated. The consistency of the tumors was determined by neurosurgeons. All surgical specimens were submitted for histological study to calculate the MIB-1 LI and the percent collagen content. Preoperative MRI-, intraoperative-, and histological findings were analyzed by a statistician.

RESULTS

Our study included 15 soft-, 5 fibrous-, and 4 hard tumors. Tumor consistency was strongly associated with the percent collagen content. However, neither the tumor consistency nor the percent collagen content was correlated with MRI findings or ADC values. The SI of growth hormone-producing adenomas on T2-WI was lower than of the other pituitary adenomas studied (p<0.01); no other significant difference was found in the ADC or on conventional MRI between pituitary adenomas with different secretory functions. The MIB-1 LI of pituitary adenomas was not correlated with their appearance on conventional MRI or their ADC values.

CONCLUSION

Using the PROPELLER DWI technique we confirmed that the ADC was not correlated with the consistency of pituitary adenomas. We also demonstrate that the ADC was not associated with the hormone-secreting status or the MIB-1 LI of pituitary adenomas.

Optimising diffusion-weighted imaging in the abdomen and pelvis: comparison of image quality between monopolar and bipolar single-shot spin-echo echo-planar sequences

OBJECTIVE

To compare geometric distortion, signal-to-noise ratio (SNR), apparent diffusion coefficient (ADC), efficacy of fat suppression and presence of artefact between monopolar (Stejskal and Tanner) and bipolar (twice-refocused, eddy-current-compensating) diffusion-weighted imaging (DWI) sequences in the abdomen and pelvis.

MATERIALS AND METHODS

A semiquantitative distortion index (DI) was derived from the subtraction images with b = 0 and 1,000 s/mm(2) in a phantom and compared between the two sequences. Seven subjects were imaged with both sequences using four b values (0, 600, 900 and 1,050 s/mm(2)) and SNR, ADC for different organs and fat-to-muscle signal ratio (FMR) were compared. Image quality was evaluated by two radiologists on a 5-point scale.

RESULTS

DI was improved in the bipolar sequence, indicating less geometric distortion. SNR was significantly lower for all tissues and b values in the bipolar images compared with the monopolar (p < 0.05), whereas FMR was not statistically different. ADC in liver, kidney and sacrum was higher in the bipolar scheme compared to the monopolar (p < 0.03), whereas in muscle it was lower (p = 0.018). Image quality scores were higher for the bipolar sequence (p ≤ 0.025).

CONCLUSION

Artefact reduction makes the bipolar DWI sequence preferable in abdominopelvic applications, although the trade-off in SNR may compromise ADC measurements in muscle.

Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI

PURPOSE

To investigate the diagnostic performance of diffusion-weighted imaging (DWI) for mammographically and clinically occult breast lesions.

MATERIALS AND METHODS

The study included 91 women with 118 breast lesions (91 benign, 12 ductal carcinoma in situ [DCIS], 15 invasive carcinoma) initially detected on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and assigned BI-RADS category 3, 4, or 5. DWI was acquired with b = 0 and 600 s/mm(2). Lesion visibility was assessed on DWI. Apparent diffusion coefficient (ADC) values were compared between malignancies, benign lesions, and normal (no abnormal enhancement on DCE-MRI) breast tissue, and the diagnostic performance of DWI was assessed based on ADC thresholding.

RESULTS

Twenty-four of 27 (89%) malignant and 74/91 (81%) benign lesions were hyperintense on the b = 600 s/mm(2) diffusion-weighted images. Both DCIS (1.33 +/- 0.19 x 10(-3) mm(2)/s) and invasive carcinomas (1.30 +/- 0.27 x 10(-3)mm(2)/s) were lower in ADC than benign lesions (1.71 +/- 0.43 x 10(-3)mm(2)/s; P < 0.001), and each lesion type was lower in ADC than normal tissue (1.90 +/- 0.38 x 10(-3)mm(2)/s, P <or= 0.001). Receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.77, and sensitivity = 96%, specificity = 55%, positive predictive value (PPV) = 39%, and negative predictive value (NPV) = 98% for an ADC threshold of 1.60 x 10(-3)mm(2)/s.

CONCLUSION

Many mammographically and clinically occult breast carcinomas were visibly hyperintense on diffusion-weighted images, and ADC enabled differentiation from benign lesions. Further studies evaluating DWI while blinded to DCE-MRI are necessary to assess the potential of DWI as a noncontrast breast screening technique.

Temporal and regional changes after focal traumatic brain injury

Magnetic resonance imaging (MRI) is widely used to evaluate the consequences of traumatic brain injury (TBI) in both experimental and clinical studies. Improved assessment of experimental TBI using the same methods as those used in clinical investigations would help to translate laboratory research into clinical advances. Here our goal was to characterize lateral fluid percussion-induced TBI, with special emphasis on differentiating the contused cortex from the pericontusional subcortical tissue. We used both in vivo MRI and proton magnetic resonance spectroscopy ((1)H-MRS) to evaluate adult male Sprague-Dawley rats 24 h and 48 h and 7 days after TBI. T2 and apparent diffusion coefficient (ADC) maps were derived from T2-weighted and diffusion-weighted images, respectively. Ratios of N-acetylaspartate (NAA), choline compounds (Cho), and lactate (Lac) over creatine (Cr) were estimated by (1)H-MRS. T2 values were high in the contused cortex 24 h after TBI, suggesting edema development; ADC was low, consistent with cytotoxic edema. At the same site, NAA/Cr was decreased and Lac/Cr elevated during the first week after TBI. In the ipsilateral subcortical area, NAA/Cr was markedly decreased and Lac/Cr was elevated during the first week, although MRI showed no evidence of edema, suggesting that (1)H-MRS detected "invisible" damage. (1)H-MRS combined with MRI may improve the detection of brain injury. Extensive assessments of animal models may increase the chances of developing successful neuroprotective strategies.

HARDI DATA DENOISING USING VECTORIAL TOTAL VARIATION AND LOGARITHMIC BARRIER

In this work, we wish to denoise HARDI (High Angular Resolution Diffusion Imaging) data arising in medical brain imaging. Diffusion imaging is a relatively new and powerful method to measure the three-dimensional profile of water diffusion at each point in the brain. These images can be used to reconstruct fiber directions and pathways in the living brain, providing detailed maps of fiber integrity and connectivity. HARDI data is a powerful new extension of diffusion imaging, which goes beyond the diffusion tensor imaging (DTI) model: mathematically, intensity data is given at every voxel and at any direction on the sphere. Unfortunately, HARDI data is usually highly contaminated with noise, depending on the b-value which is a tuning parameter pre-selected to collect the data. Larger b-values help to collect more accurate information in terms of measuring diffusivity, but more noise is generated by many factors as well. So large b-values are preferred, if we can satisfactorily reduce the noise without losing the data structure. Here we propose two variational methods to denoise HARDI data. The first one directly denoises the collected data S, while the second one denoises the so-called sADC (spherical Apparent Diffusion Coefficient), a field of radial functions derived from the data. These two quantities are related by an equation of the form S = S(S)exp (-b · sADC) (in the noise-free case). By applying these two different models, we will be able to determine which quantity will most accurately preserve data structure after denoising. The theoretical analysis of the proposed models is presented, together with experimental results and comparisons for denoising synthetic and real HARDI data.

Diffusion weighted imaging in the liver

Diffusion weighted magnetic resonance imaging (DWI) is an imaging technique which provides tissue contrast by the measurement of diffusion properties of water molecules within tissues. Diffusion is expressed in an apparent diffusion coefficient (ADC), which reflects the diffusion properties unique to each type of tissue. DWI has been originally used in neuroradiology. More recently, DWI has increasingly been used in addition to conventional unenhanced and enhanced magnetic resonance imaging (MRI) in other parts of the body. The reason for this delay was a number of technical problems inherent to the technique, making DWI very sensitive to artifacts, which had to be overcome. With assessment of ADC values, DWI proved to be helpful in characterization of focal liver lesions. However, DWI should always be used in conjunction to conventional MRI since there is considerable overlap between ADC values of benign and malignant lesions. DWI is useful in the detection of hepatocellular carcinoma in the cirrhotic liver and detection of liver metastases in oncological patients. In addition, DWI is a promising tool in the prediction of tumor responsiveness to chemotherapy and the follow-up of oncological patients after treatment, as DWI may be capable of detecting recurrent disease earlier than conventional imaging. This review focuses on the most common applications of DWI in the liver.

Technical aspects of MR diffusion imaging of the body

In diffusion-weighted magnetic resonance imaging (DWI), the intensity of the acquired magnetic resonance signal depends on the self-diffusion of the excited spins, i.e., on the microscopic stochastic Brownian molecular motion. Since the extent and orientation of molecular motion is influenced by the microscopic structure and organization of biological tissues, DWI can depict various pathological changes of organs or tissues. While DWI of the brain can be considered an established technique since the mid-1990s, significantly fewer studies have been published about DWI in body imaging, mainly because of the relatively low robustness of conventional DWI methods in non-neurological applications. Consequently, the image quality in such applications was rather limited. This situation, however, improved considerably in recent years due to better hardware as well as new pulse sequences, and several new applications of DWI (e.g., in the abdominal organs, in musculoskeletal applications, or in whole-body protocols) have been described. Unfortunately, DWI of the body is complicated by frequently low signal-to-noise ratios due to shorter transversal (T2) relaxation times and by strong variations of susceptibility. The latter result in severe distortion artifacts when standard echo-planar DWI techniques are applied. Hence, several alternative (non-echo-planar) diffusion-weighting pulse sequence types were proposed and evaluated for DWI applications in the body. In this review article, first the basics of molecular diffusion and of diffusion-weighted MRI are introduced and then several specific MRI techniques, which have been used for DWI of the body, are described. Finally, protocol recommendations for different DWI applications in the body are provided.

Preliminary results for characterization of pelvic lymph nodes in patients with prostate cancer by diffusion-weighted MR-imaging

OBJECTIVES

In this retrospective feasibility study diffusion-weighted magnetic resonance imaging (DWI) was evaluated as a potential tool for characterization of pelvic lymph nodes in patients with prostate cancer.

METHODS AND MATERIALS

Twenty-nine patients with prostate cancer underwent DWI of the pelvis at 1.5T by a non breath-hold SSEPI sequence using a body phased array coil with b values of 50, 300, and 600 s/mm(2) and an additional T2-weighted sequence. A total of 118 lymph nodes (>6 mm short axis) were analyzed by measuring the ADC-value with a polygon region of interest. Feasibility for ADC-measurement was assessed by comparing the ADC-value from the automatically created ADC-map (ADC(MR_unit)) with a manually calculated ACD-value (ADC(calculated)) and by using a linear-regression model for comparison with size and standard deviation of the ADC-value. Diagnostic performance was estimated by receiver operator characteristic analysis using histologic and/or clinical follow-up as standard of reference.

RESULTS

ADC(MR_unit) and ADC(calculated) showed a high correlation (r = 0.8999) with a mean percentual deviation of 6.33%. There was a highly significant difference between the mean ADC-value (x10(-3) mm(2)/s) of malignant (1.07 +/- 0.23) versus benign (1.54 +/- 0.25) lymph nodes, even in subgroup analysis for lymph nodes smaller versus larger than 10 mm. Receiver operator characteristic-analysis showed a good accuracy of the ADC-value (85.6% [101/118]; sensitivity: 86.0% [43/50]; specificity: 85.3% [53/68]) for differentiation of malignant and benign lymph nodes at a cutoff 1.30 x 10(-3) mm(2)/s. This was superior to a size-based analysis at a cutoff of 8 mm (accuracy: 66.1% [78/118]; sensitivity: 82.0% [41/50]; specificity: 54.4% [37/68]; P < 0.01).

CONCLUSIONS

DWI has the potential of being an accurate technique for analysis of pelvic lymph nodes. Moreover, our preliminary results suggest that the ADC-value might perform significantly superior to size criteria to discriminate between benign and malignant lymph nodes.