Classification of breast lesions pre-contrast injection using water resonance lineshape analysis

Characterization of Effects of Compressed Sensing on High Spectral and Spatial Resolution (HiSS) MRI with Comparison to SENSE

High Spectral and Spatial resolution (HiSS) MRI shows high diagnostic performance in the breast. Acceleration methods based on k-space undersampling could allow stronger T2*-based image contrast and/or higher spectral resolution, potentially increasing diagnostic performance. An agar/oil phantom was prepared with water-fat boundaries perpendicular to the readout and phase encoding directions in a breast coil. HiSS MRI was acquired at 3T, at sensitivity encoding (SENSE) acceleration factors R of up to 10, and the R = 1 dataset was used to simulate corresponding compressed sensing (CS) accelerations. Image quality was evaluated by quantifying noise and artifact levels. Effective spatial resolution was determined via modulation transfer function analysis. Dispersion vs. absorption (DISPA) analysis and full width at half maximum (FWHM) quantified spectral lineshape changes. Noise levels remained constant with R for CS but amplified with SENSE. SENSE preserved the spatial resolution of HiSS MRI, while CS reduced it in the phase encoding direction. SENSE showed no effect on FWHM or DISPA markers, while CS increased FWHM. Thus, CS might perform better in noise-limited or geometrically constrained applications, but in geometric configurations specific to breast MRI, spectral analysis might be compromised, decreasing the diagnostic performance of HiSS MRI.

High spectral and spatial resolution MRI of prostate cancer: a pilot study

PURPOSE

High spectral and spatial resolution (HiSS) MRI is a spectroscopic imaging method focusing on water and fat resonances that has good diagnostic utility in breast imaging. The purpose of this work was to assess the feasibility and potential utility of HiSS MRI for the diagnosis of prostate cancer.

METHODS

HiSS MRI was acquired at 3 T from six patients who underwent prostatectomy, yielding a train of 127 phase-coherent gradient echo (GRE) images. In the temporal domain, changes in voxel intensity were analyzed and linear (R) and quadratic (R1, R2) quantifiers of signal logarithm decay were calculated. In the spectral domain, three signal scaling-independent parameters were calculated: water resonance peak width (PW), relative peak asymmetry (PRA), and relative peak distortion from ideal Lorentzian shape (PRD). Seven cancer and five normal tissue regions of interest were identified in correlation with pathology and compared.

RESULTS

HiSS-derived quantifiers, except R2, showed high reproducibility (coefficients of variation, 5%-14%). Spectral domain quantifiers performed better than temporal domain quantifiers, with receiver operator characteristic areas under the curve ranging from of 0.83 to 0.91. For temporal domain parameters, the range was 0.74 to 0.91. Low absolute values of the coefficients of correlation between monoexponential decay markers (R, PW) and resonance shape markers (PRA, PRD) were observed (range, 0.23-0.38).

CONCLUSION

The feasibility and potential diagnostic utility of HiSS MRI in the prostate at 3 T without an endorectal coil was confirmed. Weak correlation between well-performing markers indicates that complementary information could be leveraged to further improve diagnostic accuracy.

Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T

PURPOSE

To develop and assess a full-coverage, sensitivity encoding (SENSE)-accelerated breast high spatial and spectral resolution (HiSS) magnetic resonance imaging (MRI) within clinically reasonable times as a potential nonenhanced MRI protocol for breast density measurement or breast cancer screening.

MATERIALS AND METHODS

Sixteen women with biopsy-proven cancer or suspicious lesions, and 13 women who were healthy volunteers or were screened for breast cancer, received 3T breast MRI exams, including SENSE-accelerated HiSS MRI, which was implemented as a submillimeter spatial resolution echo-planar spectroscopic imaging (EPSI) sequence. In postprocessing, fat and water resonance peak height and integral images were generated from EPSI data. The postprocessing software was custom-designed, and new algorithms were developed to enable processing of whole-coverage axial HiSS datasets. Water peak height HiSS images were compared to pre- and postcontrast T₁ -weighted images. Fat suppression was quantified as parenchymal-to-suppressed-fat signal ratio in HiSS water peak height and nonenhanced T₁ -weighted images, and artifact levels were scored.

RESULTS

Approximately a 4-fold decrease in acquisition speed, with a concurrent 2.5-fold decrease in voxel size, was achieved, with low artifact levels, and with spectral signal-to-noise ratio (SNR) of 45:1. Fat suppression was 1.9 times more effective (P < 0.001) in HiSS images than in T₁ -weighted images (SPAIR), and HiSS images showed higher SNR in the axilla. HiSS MRI visualized 10 of 13 malignant lesions identified on dynamic contrast-enhanced (DCE)-MRI, and did not require skin removal in postprocessing to generate maximum intensity projection images.

CONCLUSION

We demonstrate full-coverage, SENSE-accelerated breast HiSS MRI within clinically reasonable times, as a potential protocol for breast density measurement or breast cancer screening.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1341-1348.

Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm

PURPOSE

To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm.

METHODS

A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported.

RESULTS

In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air.

CONCLUSION

These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.

Breast density estimation from high spectral and spatial resolution MRI

A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists' breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 ([Formula: see text]) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 ([Formula: see text]) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 ([Formula: see text]) was observed between HiSS-based breast density estimations and radiologists' BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy.

Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer

Water resonance lineshapes observed in breast lesions imaged with high spectral and spatial resolution (HiSS) magnetic resonance imaging have been shown to contain diagnostically useful non-Lorentzian components. The purpose of this work is to update a previous method of breast lesion diagnosis by including phase-corrected absorption and dispersion spectra. This update includes information about the shape of the complex water resonance, which could improve the performance of a computer-aided diagnosis breast lesion classification scheme. The non-Lorentzian characteristics observed in complex breast lesion water resonance spectra are characterized by comparing a plot of the real versus imaginary components of the spectrum to that of a perfect complex Lorentzian spectrum, a "dispersion versus absorption" (DISPA) analysis technique. Distortion in the shape of the observed spectra indicates underlying physiologic changes, which have been shown to be correlated with malignancy. These spectral shape distortions in each lesion voxel are quantified by summing the deviations in DISPA radius from an ideal complex Lorentzian spectrum over all Fourier components, yielding a "total radial difference" (TRD). We limited our analysis to those voxels in each lesion with the largest TRD. The number of voxels considered was dependent on the lesion size. The TRD was used to classify voxels from 15 malignant and 8 benign lesions ([Formula: see text] voxels after voxel elimination). Lesion discrimination performance was evaluated for both the average and variance of the TRD within each lesion. Area under the receiver operating characteristic curve (ROC AUC) was used to assess both the voxel- and lesion-based discrimination methods in the task of distinguishing between malignant and benign. In the task of distinguishing voxels from malignant and benign lesions, TRD yielded an AUC of 0.89 (95% confidence interval [0.84, 0.91]). In the task of distinguishing malignant from benign lesions, the average radial difference yielded an AUC of 0.90 (95% confidence interval [0.71, 1.00]) and the variance in the radial difference yielded an AUC of 0.84 (95% confidence interval [0.61, 0.99]). We have applied the DISPA spectroscopic analysis method to HiSS data in order to identify and quantify voxels in breast lesions displaying non-Lorentzian characteristics. We have shown that a breast lesion classification scheme based on the absorption and dispersion spectral data obtained from HiSS acquisitions may outperform a similar classifier based on single off-peak component analysis, as it uses shape details of the entire spectrum instead of the magnitude at a single spectral location.

Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HiSS) MRI: a pilot study

PURPOSE

High spectral and spatial resolution magnetic resonance imaging (HiSS MRI) yields information on the local environment of suspicious lesions. Previous work has demonstrated the advantages of HiSS (complete fat-suppression, improved image contrast, no required contrast agent, etc.), leading to initial investigations of water resonance lineshape for the purpose of breast lesion classification. The purpose of this study is to investigate a quantitative imaging biomarker, which characterizes non-Lorentzian components of the water resonance in HiSS MRI datasets, for computer-aided diagnosis (CADx).

METHODS

The inhomogeneous broadening and non-Lorentzian or "off-peak" components seen in the water resonance of proton spectra of breast HiSS images are analyzed by subtracting a Lorentzian fit from the water peak spectra and evaluating the difference spectrum or "residual." The maxima of these residuals (referred to hereafter as "off-peak components") tend to be larger in magnitude in malignant lesions, indicating increased broadening in malignant lesions. The authors considered only those voxels with the highest magnitude off-peak components in each lesion, with the number of selected voxels dependent on lesion size. Our voxel-based method compared the magnitudes and frequencies of off-peak components of all voxels from all lesions in a database that included 15 malignant and 8 benign lesions (yielding ≈ 3900 voxels) based on the lesions' biopsy-confirmed diagnosis. Lesion classification was accomplished by comparing the average off-peak component magnitudes and frequencies in malignant and benign lesions. The area under the ROC curve (AUC) was used as a figure of merit for both the voxel-based and lesion-based methods.

RESULTS

In the voxel-based task of distinguishing voxels from malignant and benign lesions, off-peak magnitude yielded an AUC of 0.88 (95% confidence interval [0.84, 0.91]). In the lesion-based task of distinguishing malignant and benign lesions, average off-peak magnitude yielded an AUC 0.83 (95% confidence interval [0.61, 0.98]).

CONCLUSIONS

These promising AUC values suggest that analysis of the water-resonance in each HiSS image voxel using "residual analysis" could have high diagnostic utility and could be used to enhance current CADx methods and allow detection of breast cancer without the need to inject contrast agents.