Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast.

BACKGROUND: In breast diffusion-weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts. PURPOSE: To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications. STUDY TYPE: Prospective. POPULATION: Eleven healthy volunteers (median age: 47 years old; range: 22-74 years old) and a breast phantom. FIELD STRENGTH/SEQUENCE: Single-shot echo planar DWI and three-dimensional fast gradient echo sequences at 3 T. ASSESSMENT: All DWI data were acquired with left-right, right-left, posterior-anterior, and anterior-posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE-direction preference. STATISTICAL TESTS: Analysis of variance with post-hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi-squared test were used to evaluate if each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter-reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P-value <0.05 was considered statistically significant. RESULTS: In the phantom study, median displacement was the significantly largest in posterior-anterior data. While the displacement in the anterior-posterior and left-right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior-anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936). DATA CONCLUSION: This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior-to-anterior PE direction was preferred by readers. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer.

Purpose: Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Experimental design: Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Results: Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. Conclusion: The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging.

Background: Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs). Objective: To evaluate RSIrs for automated patient-level detection of csPCa. Design setting and participants: We retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI. Intervention: We calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records. Outcome measurements and statistical analysis: We compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones. Results and limitations: AUC values for ADC, RSIrs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSIrs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p < 0.0001). RSIrs alone was comparable with PI-RADS (p = 0.8). The combination of PI-RADS and RSIrs had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSIrs alone (p < 0.05). Similar patterns were seen in the peripheral and transition zones. Conclusions: RSIrs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study. Patient summary: We evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists' interpretation. The combination of the radiologists' scores and automated score might be better than either alone.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases.

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10-3, 2.8 × 10-3, and >3.0 ×10-2 mm2/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Apparent Diffusion Coefficient Reproducibility Across 3 T Scanners in a Breast Diffusion Phantom.

BACKGROUND: To date, the accuracy and variability of diffusion-weighted MRI (DW-MRI) metrics have been reported in a limited number of scanner/protocol/coil combinations. PURPOSE: To evaluate the reproducibility of DW-MRI estimates across multiple scanners and DW-MRI protocols and to assess the effects of using an 8-channel vs. 16-channel breast coil in a breast phantom. STUDY TYPE: Prospective. PHANTOM: Breast phantom containing tubes of water and differing polyvinylpyrrolidone (PVP) concentrations with apparent diffusion coefficients (ADCs) matching breast tissue. FIELD STRENGTH/SEQUENCE: 3 T (three standard and one wide bore), three DW-MRI single-shot echo planar imaging protocols of varying acquired spatial resolution. ASSESSMENT: Accuracy of estimated ADCs was assessed using percent differences (PD) relative to nuclear magnetic resonance spectroscopy-derived reference values. Coefficients of variation (CV) were calculated to determine variation across scanners. CVs based on the median standard deviation (CVM ) were used to evaluate tube-specific dispersion using 8- or 16-channel coils at a single scanner. ADCs of PVP-containing tubes were additionally normalized by the median water tube ADC to account for temperature effects. STATISTICAL TESTS: Two-way repeated measures analysis of variance and post hoc tests were used to evaluate differences in ADC, CV, and CVM across scanners and protocols (α = 0.05). RESULTS: ADCs were within 11% (interquartile range [IQR] 7%) of reference values and significantly improved to 2% (IQR 7%) after normalization to an internal water reference. Normalization significantly reduced interscanner variability of ADC estimates from 7% to 4%. DW-MRI protocol did not affect ADC accuracy; however, the clinical and higher-resolution clinical protocols resulted in the greatest (9%) and least (6%) interscanner variability, respectively. The 8- and 16-channel receive coils yielded similar accuracy (PD: 12% vs. 16%) and precision (CVM : 2.7% vs. 2.9%). DATA CONCLUSION: Normalization by an internal reference improved interscanner ADC reproducibility. High-resolution protocols yielded comparably accurate and significantly less variable ADCs compared to a clinical standard protocol. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Diffusion Breast MRI: Current Standard and Emerging Techniques.

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging.

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm2) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C1 compartment and product of the restricted and intermediate diffusion compartments (C1 and C2). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

A flow-diffusion model of oxygen transport for quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) with single gas calibrated fMRI.

One promising approach for mapping CMRO2 is dual-calibrated functional MRI (dc-fMRI). This method exploits the Fick Principle to combine estimates of CBF from ASL, and OEF derived from BOLD-ASL measurements during arterial O2 and CO2 modulations. Multiple gas modulations are required to decouple OEF and deoxyhemoglobin-sensitive blood volume. We propose an alternative single gas calibrated fMRI framework, integrating a model of oxygen transport, that links blood volume and CBF to OEF and creates a mapping between the maximum BOLD signal, CBF and OEF (and CMRO2). Simulations demonstrated the method's viability within physiological ranges of mitochondrial oxygen pressure, PmO2, and mean capillary transit time. A dc-fMRI experiment, performed on 20 healthy subjects using O2 and CO2 challenges, was used to validate the approach. The validation conveyed expected estimates of model parameters (e.g., low PmO2), with spatially uniform OEF maps (grey matter, GM, OEF spatial standard deviation ≈ 0.13). GM OEF estimates obtained with hypercapnia calibrated fMRI correlated with dc-fMRI (r = 0.65, p = 2·10-3). For 12 subjects, OEF measured with dc-fMRI and the single gas calibration method were correlated with whole-brain OEF derived from phase measures in the superior sagittal sinus (r = 0.58, p = 0.048; r = 0.64, p = 0.025 respectively). Simplified calibrated fMRI using hypercapnia holds promise for clinical application.

MRI evaluation of cerebral metabolic rate of oxygen (CMRO2) in obstructive sleep apnea.

Patients with obstructive sleep apnea (OSA) are at elevated risk of developing systemic vascular disease and cognitive dysfunction. Here, cerebral oxygen metabolism was assessed in patients with OSA by means of a magnetic resonance-based method involving simultaneous measurements of cerebral blood flow rate and venous oxygen saturation in the superior sagittal sinus for a period of 10 minutes at an effective temporal resolution of 1.3 seconds before, during, and after repeated 24-second breath-holds mimicking spontaneous apneas, yielding, along with pulse oximetry-derived arterial saturation, whole-brain CMRO2 via Fick's Principle. Enrolled subjects were classified based on their apnea-hypopnea indices into OSA (N = 31) and non-sleep apnea reference subjects (NSA = 21), and further compared with young healthy subjects (YH, N = 10). OSA and NSA subjects were matched for age and body mass index. CMRO2 was lower in OSA than in the YH group during normal breathing (105.6 ± 14.1 versus 123.7 ± 22.8 µmol O2/min/100g, P = 0.01). Further, the fractional change in CMRO2 in response to a breath-hold challenge was larger in OSA than in the YH group (15.2 ± 9.2 versus 8.5 ± 3.4%, P = 0.04). However, there was no significant difference in CMRO2 between OSA and NSA subjects. The data suggest altered brain oxygen metabolism in OSA and possibly in NSA as well.

Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone.

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10-20). On average (± SD), participants' largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0-7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10-12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Characterization of the diffusion signal of breast tissues using multi-exponential models.

PURPOSE: Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. METHODS: The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. RESULTS: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. CONCLUSION: Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

BACKGROUND: Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity. PURPOSE: To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa. STUDY TYPE: Retrospective. SUBJECTS: Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa. FIELD STRENGTH/SEQUENCE: A 3 T, multishell diffusion-weighted and axial T2-weighted sequences. ASSESSMENT: High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated. STATISTICAL TESTS: Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05. RESULTS: RSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05). DATA CONCLUSION: RSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced-Field-of-View Echo-Planar Imaging and Enhanced Reversed Polarity Gradient Method.

BACKGROUND: Diffusion-weighted (DW) echo-planar imaging (EPI) is prone to geometric distortions due to B0 inhomogeneities. Both prospective and retrospective approaches have been developed to decrease and correct such distortions. PURPOSE: The purpose of this work was to evaluate the performance of reduced-field-of-view (FOV) acquisition and retrospective distortion correction methods in decreasing distortion artifacts for breast imaging. Coverage of the axilla in reduced-FOV DW magnetic resonance imaging (MRI) and residual distortion were also assessed. STUDY TYPE: Retrospective. POPULATION/PHANTOM: Breast phantom and 169 women (52.4 ± 13.4 years old) undergoing clinical breast MRI. FIELD STRENGTH/SEQUENCE: A 3.0 T/ full- and reduced-FOV DW gradient-echo EPI sequence. ASSESSMENT: Performance of reversed polarity gradient (RPG) and FSL topup in correcting breast full- and reduced-FOV EPI data was evaluated using the mutual information (MI) metric between EPI and anatomical images. Two independent breast radiologists determined if coverage on both EPI data sets was adequate to evaluate axillary nodes and identified residual nipple distortion artifacts. STATISTICAL TESTS: Two-way repeated-measures analyses of variance and post hoc tests were used to identify differences between EPI modality and distortion correction method. Generalized linear mixed effects models were used to evaluate differences in axillary coverage and residual nipple distortion. RESULTS: In a breast phantom, residual distortions were 0.16 ± 0.07 cm and 0.22 ± 0.13 cm in reduced- and full-FOV EPI with both methods, respectively. In patients, MI significantly increased after distortion correction of full-FOV (11 ± 5% and 18 ± 9%, RPG and topup) and reduced-FOV (8 ± 4% both) EPI data. Axillary nodes were observed in 99% and 69% of the cases in full- and reduced-FOV EPI images. Residual distortion was observed in 93% and 0% of the cases in full- and reduced-FOV images. DATA CONCLUSION: Minimal distortion was achieved with RPG applied to reduced-FOV EPI data. RPG improved distortions for full-FOV images but with more modest improvements and limited correction near the nipple. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model.

PURPOSE: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. EXPERIMENTAL DESIGN: Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC. RESULTS: Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991). CONCLUSIONS: The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

BACKGROUND: Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. PURPOSE: To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. STUDY TYPE: Retrospective. SUBJECTS: Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. FIELD STRENGTH/SEQUENCE: 3T multishell diffusion-weighted sequence. ASSESSMENT: Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. STATISTICAL TESTS: Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. RESULTS: The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. DATA CONCLUSION: The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Noncontrast MRI with advanced diffusion weighted imaging for breast cancer detection in a lactating woman.

Magnetic resonance imaging (MRI) is used for preoperative evaluation, high-risk screening, and other select indications for breast cancer. However, the interpretation of breast MR images in pregnant and lactating women is complicated by physiologic changes of the breast that may result in marked background enhancement. Breast MRI with contrast administration is contraindicated in pregnancy. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted (DW)-MRI method that theoretically reflects signal from cells with high nuclear-to-cytoplasm ratio without gadolinium-based contrast. This report describes a case in which RSI notably increased tumor conspicuity in a lactating woman, compared to contrast-enhanced (CE)-MRI and conventional DW-MRI.

Multiparametric MRI characterization of level dependent differences in lumbar muscle size, quality, and microstructure.

Magnetic resonance imaging (MRI) is a diagnostic tool that can be used to noninvasively assess lumbar muscle size and fatty infiltration, important biomarkers of muscle health. Diffusion tensor imaging (DTI) is an MRI technique that is sensitive to muscle microstructural features such as fiber size (an important biomarker of muscle health), which is typically only assessed using invasive biopsy techniques. The goal of this study was to establish normative values of level-dependent lumbar muscle size, fat signal fraction, and restricted diffusion assessed by MRI in a highly active population. Forty-two active-duty Marines were imaged using a (a) high-resolution anatomical, (b) fat-water separation, and (c) DT-MRI scan. The multifidus and erector spinae muscles were compared at each level using two-way repeated measures ANOVA. Secondary analysis included Three dimensional (3D) reconstructions to qualitatively assess lumbar muscle size, fatty infiltration, and fiber orientation via tractography. The erector spinae was found to be larger than the multifidus above L5, with lower fat signal fraction above L3, and a less restricted diffusion profile than the multifidus above L4, with this pattern reversed in the lower lumbar spine. 3D reconstructions demonstrated accumulations of epimuscular fat in the anterior and posterior regions of the lumbar musculature, with minimal intramuscular fatty infiltration. Tractography images demonstrated different orientations of adjacent lumbar musculature, which cannot be visualized with standard MRI pulse sequences. The level dependent differences found in this study provide a normative baseline, for which to better understand whole muscle and microstructural changes associated with aging, low back pain, and pathology.

MRI evaluation of cerebrovascular reactivity in obstructive sleep apnea.

Obstructive sleep apnea (OSA) is characterized by intermittent obstruction of the airways during sleep. Cerebrovascular reactivity (CVR) is an index of cerebral vessels' ability to respond to a vasoactive stimulus, such as increased CO2. We hypothesized that OSA alters CVR, expressed as a breath-hold index (BHI) defined as the rate of change in CBF or BOLD signal during a controlled breath-hold stimulus mimicking spontaneous apneas by being both hypercapnic and hypoxic. In 37 OSA and 23 matched non sleep apnea (NSA) subjects, we obtained high temporal resolution CBF and BOLD MRI data before, during, and between five consecutive BH stimuli of 24 s, each averaged to yield a single BHI value. Greater BHI was observed in OSA relative to NSA as derived from whole-brain CBF (78.6 ± 29.6 vs. 60.0 ± 20.0 mL/min2/100 g, P = 0.010) as well as from flow velocity in the superior sagittal sinus (0.48 ± 0.18 vs. 0.36 ± 0.10 cm/s2, P = 0.014). Similarly, BOLD-based BHI was greater in OSA in whole brain (0.19 ± 0.08 vs. 0.15 ± 0.03%/s, P = 0.009), gray matter (0.22 ± 0.09 vs. 0.17 ± 0.03%/s, P = 0.011), and white matter (0.14 ± 0.06 vs. 0.10 ± 0.02%/s, P = 0.010). The greater CVR is not currently understood but may represent a compensatory mechanism of the brain to maintain oxygen supply during intermittent apneas.

Calibrated fMRI for dynamic mapping of CMRO2 responses using MR-based measurements of whole-brain venous oxygen saturation.

Functional MRI (fMRI) can identify active foci in response to stimuli through BOLD signal fluctuations, which represent a complex interplay between blood flow and cerebral metabolic rate of oxygen (CMRO2) changes. Calibrated fMRI can disentangle the underlying contributions, allowing quantification of the CMRO2 response. Here, whole-brain venous oxygen saturation (Yv) was computed alongside ASL-measured CBF and BOLD-weighted data to derive the calibration constant, M, using the proposed Yv-based calibration. Data were collected from 10 subjects at 3T with a three-part interleaved sequence comprising background-suppressed 3D-pCASL, 2D BOLD-weighted, and single-slice dual-echo GRE (to measure Yv via susceptometry-based oximetry) acquisitions while subjects breathed normocapnic/normoxic, hyperoxic, and hypercapnic gases, and during a motor task. M was computed via Yv-based calibration from both hypercapnia and hyperoxia stimulus data, and results were compared to conventional hypercapnia or hyperoxia calibration methods. Mean M in gray matter did not significantly differ between calibration methods, ranging from 8.5 ± 2.8% (conventional hyperoxia calibration) to 11.7 ± 4.5% (Yv-based calibration in response to hyperoxia), with hypercapnia-based M values between (p = 0.56). Relative CMRO2 changes from finger tapping were computed from each M map. CMRO2 increased by ∼20% in the motor cortex, and good agreement was observed between the conventional and proposed calibration methods.