Abbie M. Wood

Kinetic Curves of Malignant Lesions Are Not Consistent Across MRI Systems: Need for Improved Standardization of Breast Dynamic Contrast-Enhanced MRI Acquisition

OBJECTIVE The purpose of this study was to compare MRI kinetic curve data acquired with three systems in the evaluation of malignant lesions of the breast. MATERIALS AND METHODS The cases of 601 patients with 682 breast lesions (185 benign, 497 malignant) were selected for review. The malignant lesions were classified as ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and other. The dynamic MRI protocol consisted of one unenhanced and three to seven contrast-enhanced images acquired with one of three imaging protocols and systems. An experienced radiologist analyzed the shapes of the kinetic curves according to the BI-RADS lexicon. Several quantitative kinetic parameters were calculated, and the kinetic parameters of malignant lesions were compared across the three systems. RESULTS Imaging protocol and system 1 were used to image 304 malignant lesions (185 IDC, 62 DCIS); imaging protocol and system 2, 107 lesions (72 IDC, 21 DCIS); and imaging protocol and system 3, 86 lesions (64 IDC, 17 DCIS). Compared with those visualized with imaging protocols and systems 1 and 2, IDC lesions visualized with imaging protocol and system 3 had significantly less initial enhancement, longer time to peak enhancement, and a slower washout rate (p < 0.004). Only 47% of IDC lesions imaged with imaging protocol and system 3 exhibited washout type curves, compared with 75% and 74% of those imaged with imaging protocols and systems 2 and 1, respectively. The diagnostic accuracy of kinetic analysis was lowest for imaging protocol and system 3, but the difference was not statistically significant. CONCLUSION The kinetic curve data on malignant lesions acquired with one system showed significantly lower initial contrast uptake and a different curve shape in comparison with data acquired with the other two systems. Differences in k-space sampling, T1 weighting, and magnetization transfer effects may be explanations for the difference.

Sensitivity to tumor microvasculature without contrast agents in high spectral and spatial resolution MR images

Contrast‐enhanced (CE)‐MRI is sensitive to cancers but can produce adverse reactions and suffers from insufficient specificity and morphological detail. This research investigated whether high spectral and spatial resolution (HiSS) MRI detects tumor vasculature without contrast agents, based on the sensitivity of the water resonance line shape to tumor blood vessels. HiSS data from AT6.1 tumors inoculated in the hind legs of rats (N = 8) were collected pre‐ and post–blood pool contrast agent (iron‐oxide particles) injection. The waterline in small voxels was significantly more asymmetric at the tumor rim compared to the tumor center and normal muscle (P < 0.003). Composite images were synthesized, with the intensity in each voxel determined by the Fourier component (FC) of the water resonance having the greatest relative image contrast at that position. We tested whether regions with high contrast in FC images (FCIs) contain vasculature by comparing FCIs with CE‐MRI as the “gold standard” of vascular density. The FCIs had 75% ± 13% sensitivity, 74% ± 10% specificity, and 91% ± 4% positive predictive value (PPV) for vasculature detection at the tumor rim. These results suggest that tumor microvasculature can be detected using HiSS imaging without the use of contrast agents. Magn Reson Med 61:291–298, 2009.

Non-contrast Enhanced MRI for Evaluation of Breast Lesions: Comparison of Non-contrast Enhanced High Spectral and Spatial Resolution (HiSS) Images Versus Contrast Enhanced Fat-suppressed Images

RATIONALE AND OBJECTIVES The aims of this study were to evaluate high spectral and spatial resolution (HiSS) magnetic resonance imaging (MRI) for the diagnosis of breast cancer without the injection of contrast media by comparing the performance of precontrast HiSS images to that of conventional contrast-enhanced, fat-suppressed, T1-weighted images on the basis of image quality and in the task of classifying benign and malignant breast lesions. MATERIALS AND METHODS Ten benign and 44 malignant lesions were imaged at 1.5 T with HiSS (precontrast administration) and conventional fat-suppressed imaging (3-10 minutes after contrast administration). This set of 108 images, after randomization, was evaluated by three experienced radiologists blinded to the imaging technique. Breast Imaging Reporting and Data System morphologic criteria (lesion shape, lesion margin, and internal signal intensity pattern) and final assessment were used to measure reader performance. Image quality was evaluated on the basis of boundary delineation and quality of fat suppression. An overall probability of malignancy was assigned to each lesion for HiSS and conventional images separately. RESULTS On boundary delineation and quality of fat suppression, precontrast HiSS scored similarly to conventional postcontrast MRI. On benign versus malignant lesion separation, there was no statistically significant difference in receiver-operating characteristic performance between HiSS and conventional MRI, and HiSS met a reasonable noninferiority condition. CONCLUSIONS Precontrast HiSS imaging is a promising approach for showing lesion morphology without blooming and other artifacts caused by contrast agents. HiSS images could be used to guide subsequent dynamic contrast-enhanced MRI scans to maximize spatial and temporal resolution in suspicious regions. HiSS MRI without contrast agent injection may be particularly important for patients at risk for contrast-induced nephrogenic systemic fibrosis or allergic reactions.

Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions

To compare the performance of computer‐aided diagnosis (CADx) analysis of precontrast high spectral and spatial resolution (HiSS) MRI to that of clinical dynamic contrast‐enhanced MRI (DCE‐MRI) in the diagnostic classification of breast lesions.

How one institution overcame the challenges to start an MRI-based brachytherapy program for cervical cancer

Purpose Adaptive magnetic resonance imaging (MRI)-based brachytherapy results in improved local control and decreased high-grade toxicities compared to historical controls. Incorporating MRI into the workflow of a department can be a major challenge when initiating an MRI-based brachytherapy program. This project aims to describe the goals, challenges, and solutions when initiating an MRI-based cervical cancer brachytherapy program at our institution. Material and methods We describe the 6-month multi-disciplinary planning phase to initiate an MRI-based brachytherapy program. We describe the specific challenges that were encountered prior to treating our first patient. Results We describe the solutions that were realized and executed to solve the challenges that we faced to establish our MRI-based brachytherapy program. We emphasize detailed coordination of care, planning, and communication to make the workflow feasible. We detail the imaging and radiation physics solutions to safely deliver MRI-based brachytherapy. The focus of these efforts is always on the delivery of optimal, state of the art patient care and treatment delivery within the context of our available institutional resources. Conclusions Previous publications have supported a transition to MRI-based brachytherapy, and this can be safely and efficiently accomplished as described in this manuscript.

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

Inhomogeneously broadened, non‐Lorentzian water resonances have been observed in small image voxels of breast tissue. The non‐Lorentzian components of the water resonance are probably produced by bulk magnetic susceptibility shifts caused by dense, deoxygenated tumor blood vessels (the ‘blood oxygenation level‐dependent’ effect), but can also be produced by other characteristics of local anatomy and physiology, including calcifications and interfaces between different types of tissue. Here, we tested the hypothesis that the detection of non‐Lorentzian components of the water resonance with high spectral and spatial resolution (HiSS) MRI allows the classification of breast lesions without the need to inject contrast agent. Eighteen malignant lesions and nine benign lesions were imaged with HiSS MRI at 1.5 T. A new algorithm was developed to detect non‐Lorentzian (or off‐peak) components of the water resonance. After a Lorentzian fit had been subtracted from the data, the largest peak in the residual spectrum in each voxel was identified as the major off‐peak component of the water resonance. The difference in frequency between these off‐peak components and the main water peaks, and their amplitudes, were measured in malignant lesions, benign lesions and breast fibroglandular tissue. Off‐peak component frequencies were significantly different between malignant and benign lesions (p < 0.001). Receiver operating characteristic (ROC) analysis was used to assess the diagnostic performance of HiSS off‐peak component analysis compared with dynamic contrast‐enhanced (DCE) MRI parameters. The areas under the ROC curves for the ‘DCE rapid uptake fraction’, ‘DCE washout fraction’, ‘off‐peak component amplitude’ and ‘off‐peak component frequency’ were 0.75, 0.83, 0.50 and 0.86, respectively. These results suggest that water resonance lineshape analysis performs well in the classification of breast lesions without contrast injection and could improve the diagnostic accuracy of clinical breast MR examinations. In addition, this approach may provide an alternative to DCE MRI in women who are at risk for adverse reactions to contrast media. Copyright

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.

TU‐A‐301‐09: Moving towards Quantitative Breast MRI: Dynamic Contrast Media Concentration Images

Purpose: To develop methods for the acquisition of dynamic contrast media concentration images during routine clinical DCE‐MRI. Methods: We designed calibration phantoms consisting of color‐coded tubes filled with gadodiamide solutions (0.0–0.5mM Omniscan), which were placed into a 16‐channel bilateral breast coil. Three patients (ages a. 55, b. 50, and c. 41) were scanned at 1.5T (a, b) and 3T (c) with IRB approval. We acquired one variable flip angle gradient echo series, and a T1‐weighted dynamic series (3D turbo field echo) before and after a gadodiamide injection (0.1 mmol/kg). Under the present experimental conditions 1/T1 is approximately proportional to signal intensity. Allowing us to convert signal intensity to concentration of contrast media, by determining the factor of proportionality from the known T1 values in the phantom. This relation is corrected using the phantom‐to‐tissue proton density ratio to make it applicable to breast tissue. Concentration images for the different time points in the series were produced, using the signal from the standard dynamic series. Results: After conversion from signal intensity to concentration, peak contrast media concentration in the parenchyma was measured in the range of 0.26–0.32mM. For patient ‘c’ a mucinous cancer was present in the left breast and had a peak concentration of 0.59mM. The phantom‐to‐tissue apparent proton density ratios were in the range of 3.75– 3.90, and 2.25 for the cancer. Conclusions: The results of this pilot study demonstrate the possibility of performing quantitative measurements on standard clinical data. The pulse sequence used for the present study is not easy to model accurately; the ‘spoiled gradient echo’ model is not appropriate. However, this approach demonstrates that subtraction images can be converted into quantitative concentration images, even if a good mathematical model is not available. The concentration images could facilitate inter‐institutional comparisons, as they allow for the standardization of DCE‐MRI across different scanners.