Safety limitations of MR-HIFU treatment near interfaces : a phantom validation

Peer reviewe

Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI

Purpose: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. Methods: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. Results: Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p Conclusion: High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.</p

Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images.

BACKGROUND: There is an increasing interest in non-contrast-enhanced magnetic resonance imaging (MRI) for detecting and evaluating breast lesions. We present a methodology utilizing lesion core and periphery region of interest (ROI) features derived from directional diffusion-weighted imaging (DWI) data to evaluate performance in discriminating benign from malignant lesions in dense breasts. METHODS: We accrued 55 dense-breast cases with 69 lesions (31 benign; 38 cancer) at a single institution in a prospective study; cases with ROIs exceeding 7.50 cm RESULTS: The region-growing algorithm for 3D lesion model generation improved inter-observer variability over hand drawn ROIs (DSC: 0.66 vs 0.56 (p \u3c 0.001) with substantial agreement (DSC \u3e 0.8) in 46% vs 13% of cases, respectively (p \u3c 0.001)). The overall classifier improved discrimination over mean ADC, (ROC- area under the curve (AUC): 0.85 vs 0.75 and 0.83 vs 0.74 respectively for the two readers). CONCLUSIONS: A classifier generated from directional DWI information using lesion core and lesion periphery information separately can improve lesion discrimination in dense breasts over mean ADC and should be considered for inclusion in computer-aided diagnosis algorithms. Our model-based ROIs could facilitate standardization of breast MRI computer-aided diagnostics (CADx)

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

PurposeHigh 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).MethodsThe 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.ResultsIn 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]).ConclusionsThese 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

Benign conditions that mimic prostate carcinoma : MR imaging features with histopathologic correlation

Multiparametric magnetic resonance (MR) imaging combines anatomic and functional imaging techniques for evaluating the prostate and is increasingly being used in diagnosis and management of prostate cancer. A wide spectrum of anatomic and pathologic processes in the prostate may masquerade as prostate cancer, complicating the imaging interpretation. The histopathologic and imaging findings of these potential mimics are reviewed. These entities include the anterior fibromuscular stroma, surgical capsule, central zone, periprostatic vein, periprostatic lymph nodes, benign prostatic hyperplasia (BPH), atrophy, necrosis, calcification, hemorrhage, and prostatitis. An understanding of the prostate zonal anatomy is helpful in distinguishing the anatomic entities from prostate cancer. The anterior fibromuscular stroma, surgical capsule, and central zone are characteristic anatomic features of the prostate with associated low T2 signal intensity due to dense fibromuscular tissue or complex crowded glandular tissue. BPH, atrophy, necrosis, calcification, and hemorrhage all have characteristic features with one or more individual multiparametric MR imaging modalities. Prostatitis constitutes a heterogeneous group of infective and inflammatory conditions including acute and chronic bacterial prostatitis, infective and noninfective granulomatous prostatitis, and malacoplakia. These entities are associated with variable clinical manifestations and are characterized by the histologic hallmark of marked inflammatory cellular infiltration. In some cases, these entities are indistinguishable from prostate cancer at multiparametric MR imaging and may even exhibit extraprostatic extension and lymphadenopathy, mimicking locally advanced prostate cancer. It is important for the radiologists interpreting prostate MR images to be aware of these pitfalls for accurate interpretation.14 page(s

X-Ray Fluorescence Microscopy Demonstrates Preferential Accumulation of a Vanadium-Based Magnetic Resonance Imaging Contrast Agent in Murine Colonic Tumors

Contrast agents that specifically enhance cancers on magnetic resonance imaging (MRI) will allow earlier detection. Vanadium-based chelates (VCs) selectively enhance rodent cancers on MRI, suggesting selective uptake of VCs by cancers. Here we report x-ray fluorescence microscopy (XFM) of VC uptake by murine colon cancer. Colonic tumors in mice treated with azoxymethane/dextran sulfate sodium were identified by MRI. Then a gadolinium-based contrast agent and a VC were injected intravenously; mice were sacrificed and colons sectioned. VC distribution was sampled at 120 minutes after injection to evaluate the long-term accumulation. Gadolinium distribution was sampled at 10 minutes after injection due to its rapid washout. XFM was performed on 72 regions of normal and cancerous colon from five normal mice and four cancer-bearing mice. XFM showed that all gadolinium was extracellular, with similar concentrations in colon cancers and normal colon. In contrast, the average VC concentration was twofold higher in cancers versus normal tissue ( p < .002). Cancers also contained numerous “hot spots” with intracellular VC concentrations sixfold higher than the concentration in normal colon ( p < .0001). No hot spots were detected in normal colon. This is the first direct demonstration that VCs selectively accumulate in cancer cells and thus may improve cancer detection

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.</p