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

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

BackgroundMulticompartmental 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.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study typeRetrospective.SubjectsForty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field strength/sequence3T multishell diffusion-weighted sequence.AssessmentMulticompartmental 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 testsAnalysis 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.ResultsThe 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 conclusionThe 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

Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer.

BACKGROUND:High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored. PURPOSE:To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions. MATERIAL AND METHODS:This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived ( SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials ( KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal ( KCE). RESULTS:Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 - 0.87), 0.69 (95% CI = 0.68-0.70), and 0.86 (95% CI = 0.86-0.87), respectively. CONCLUSION:In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision . KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP

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

Voxel Level Radiologic-Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer.

PurposeRestriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions.Experimental designTen cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity.ResultsRSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003).ConclusionsBuilding on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668-74. ©2016 AACR

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

Patient-level detection of grade group ≥2 prostate cancer using quantitative diffusion MRI

AbstractPurposeMultiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa.Materials and MethodsRetrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone.Results151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p<0.0001) were superior to ADC for patient-level detection of csPCa. The performance of RSIrs was comparable to that of PI-RADS (p=0.6). AUC for PI-RADS+RSIrs was 0.84 [0.77, 0.90], superior to PI-RADS or RSIrs, alone (p=0.008, p=0.009).ConclusionsRSIrs was superior to conventional ADC and comparable to (routine, clinical) PI-RADS for patient-level detection of csPCa. The combination of PI-RADS and RSIrs was superior to either alone. RSIrs is a promising quantitative marker worthy of prospective study in the setting of csPCa detection.DisclosuresMEH reports honoraria from Multimodal Imaging Services Corporation and research funding from General Electric Healthcare. AMD is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. and receives funding through research agreements with General Electric Healthcare. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. TMS reports honoraria from Multimodal Imaging Services Corporation, Varian Medical Systems, and WebMD; he has an equity interest in CorTechs Labs, Inc. and also serves on its Scientific Advisory Board. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies

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

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

BackgroundMultiparametric 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).ObjectiveTo evaluate RSIrs for automated patient-level detection of csPCa.Design setting and participantsWe 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.InterventionWe calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.Outcome measurements and statistical analysisWe 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 limitationsAUC 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.ConclusionsRSIrs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study.Patient summaryWe 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