Matthew D. Blackledge

Whole-Body Diffusion-Weighted MRI: Tips, Tricks, and Pitfalls

OBJECTIVE We examine the clinical impetus for whole-body diffusion-weighted MRI and discuss how to implement the technique with clinical MRI systems. We include practical tips and tricks to optimize image quality and reduce artifacts. The interpretative pitfalls are enumerated, and potential challenges are highlighted. CONCLUSION Whole-body diffusion-weighted MRI can be used for tumor staging and assessment of treatment response. Meticulous technique and knowledge of potential interpretive pitfalls will help to avoid mistakes and establish this modality in radiologic practice.

Computed Diffusion-weighted MR Imaging May Improve Tumor Detection

PURPOSE To describe computed diffusion weighted (DW) magnetic resonance (MR) imaging as a method for obtaining high-b-value images from DW MR imaging performed at lower b values and to investigate the feasibility of the technique to improve lesion detection in oncologic cases. MATERIALS AND METHODS The study was approved by the institutional and research committee, and written informed consent was obtained from all patients. DW MR imaging was performed on a CuSO(4) phantom at 1.5 T with a range of b values and compared with computed DW MR imaging images synthesized from lower b values (0 and 600 sec/mm(2)). The signal-to-noise ratio (SNR) was compared, and agreement between the SNR of computed DW MR imaging and theoretical estimation assessed. Computed DW MR imaging was evaluated in 10 oncologic patients who underwent whole-body DW MR imaging with b values of 0 and 900 sec/mm(2). Computed DW MR images at computed b values of 1500 and 2000 sec/mm(2) were generated. The image quality and background suppression of acquired and computed images were rated by a radiologist using a four-point scale. The diagnostic performance for malignant lesion detection using these images was evaluated and compared by using the McNemar Test. RESULTS The SNR of computed DW MR imaging of the phantom conformed closely to theoretical predictions. Computed DW MR imaging resulted in a higher SNR compared with acquired DW MR imaging, especially at b values greater than 840 sec/mm(2). In patients, images with a computed b value of 2000 sec/mm(2) produced good image quality and high background suppression (mean scores of 2.8 and 4.0, respectively). Evaluation of images with a computed b value of 2000 sec/mm(2) resulted in higher overall diagnostic sensitivity (96.0%) and specificity (96.6%) compared with images with an acquired b value of 900 sec/mm(2) (sensitivity, 89.4%; specificity, 87.5%; P < .01). CONCLUSION Computed DW MR imaging in the body allows higher-b-value images to be obtained with a good SNR. Clinical computed DW MR imaging is feasible and may improve disease detection. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101919/-/DC1.

Assessment of Treatment Response by Total Tumor Volume and Global Apparent Diffusion Coefficient Using Diffusion-Weighted MRI in Patients with Metastatic Bone Disease: A Feasibility Study

We describe our semi-automatic segmentation of whole-body diffusion-weighted MRI (WBDWI) using a Markov random field (MRF) model to derive tumor total diffusion volume (tDV) and associated global apparent diffusion coefficient (gADC); and demonstrate the feasibility of using these indices for assessing tumor burden and response to treatment in patients with bone metastases. WBDWI was performed on eleven patients diagnosed with bone metastases from breast and prostate cancers before and after anti-cancer therapies. Semi-automatic segmentation incorporating a MRF model was performed in all patients below the C4 vertebra by an experienced radiologist with over eight years of clinical experience in body DWI. Changes in tDV and gADC distributions were compared with overall response determined by all imaging, tumor markers and clinical findings at serial follow up. The segmentation technique was possible in all patients although erroneous volumes of interest were generated in one patient because of poor fat suppression in the pelvis, requiring manual correction. Responding patients showed a larger increase in gADC (median change = +0.18, range = −0.07 to +0.78×10−3 mm2/s) after treatment compared to non-responding patients (median change = −0.02, range = −0.10 to +0.05×10−3 mm2/s, p = 0.05, Mann-Whitney test), whereas non-responding patients showed a significantly larger increase in tDV (median change = +26%, range = +3 to +284%) compared to responding patients (median change = −50%, range = −85 to +27%, p = 0.02, Mann-Whitney test). Semi-automatic segmentation of WBDWI is feasible for metastatic bone disease in this pilot cohort of 11 patients, and could be used to quantify tumor total diffusion volume and median global ADC for assessing response to treatment.

Diffusion-weighted Imaging as a Treatment Response Biomarker for Evaluating Bone Metastases in Prostate Cancer: A Pilot Study

Purpose To determine the usefulness of whole-body diffusion-weighted imaging (DWI) to assess the response of bone metastases to treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). Materials and Methods A phase II prospective clinical trial of the poly-(adenosine diphosphate-ribose) polymerase inhibitor olaparib in mCRPC included a prospective magnetic resonance (MR) imaging substudy; the study was approved by the institutional research board, and written informed consent was obtained. Whole-body DWI was performed at baseline and after 12 weeks of olaparib administration by using 1.5-T MR imaging. Areas of abnormal signal intensity on DWI images in keeping with bone metastases were delineated to derive total diffusion volume (tDV); five target lesions were also evaluated. Associations of changes in volume of bone metastases and median apparent diffusion coefficient (ADC) with response to treatment were assessed by using the Mann-Whitney test and logistic regression; correlation with prostate-specific antigen level and circulating tumor cell count were assessed by using Spearman correlation (r). Results Twenty-one patients were included. All six responders to olaparib showed a decrease in tDV, while no decrease was observed in all nonresponders; this difference between responders and nonresponders was significant (P = .001). Increases in median ADC were associated with increased odds of response (odds ratio, 1.08; 95% confidence interval [CI]: 1.00, 1.15; P = .04). A positive association was detected between changes in tDV and best percentage change in prostate-specific antigen level and circulating tumor cell count (r = 0.63 [95% CI: 0.27, 0.83] and r = 0.77 [95% CI: 0.51, 0.90], respectively). When assessing five target lesions, decreases in volume were associated with response (odds ratio for volume increase, 0.89; 95% CI: 0.80, 0.99; P = .037). Conclusion This pilot study showed that decreases in volume and increases in median ADC of bone metastases assessed with whole-body DWI can potentially be used as indicators of response to olaparib in mCRPC. Online supplemental material is available for this article.

Volume of Bone Metastasis Assessed with Whole-Body Diffusion-weighted Imaging Is Associated with Overall Survival in Metastatic Castration-resistant Prostate Cancer

Purpose To determine the correlation between the volume of bone metastasis as assessed with diffusion-weighted (DW) imaging and established prognostic factors in metastatic castration-resistant prostate cancer (mCRPC) and the association with overall survival (OS). Materials and Methods This retrospective study was approved by the institutional review board; informed consent was obtained from all patients. The authors analyzed whole-body DW images obtained between June 2010 and February 2013 in 53 patients with mCRPC at the time of starting a new line of anticancer therapy. Bone metastases were identified and delineated on whole-body DW images in 43 eligible patients. Total tumor diffusion volume (tDV) was correlated with the bone scan index (BSI) and other prognostic factors by using the Pearson correlation coefficient (r). Survival analysis was performed with Kaplan-Meier analysis and Cox regression. Results The median tDV was 503.1 mL (range, 5.6-2242 mL), and the median OS was 12.9 months (95% confidence interval [CI]: 8.7, 16.1 months). There was a significant correlation between tDV and established prognostic factors, including hemoglobin level (r = -0.521, P < .001), prostate-specific antigen level (r = 0.556, P < .001), lactate dehydrogenase level (r = 0.534, P < .001), alkaline phosphatase level (r = 0.572, P < .001), circulating tumor cell count (r = 0.613, P = .004), and BSI (r = 0.565, P = .001). A higher tDV also showed a significant association with poorer OS (hazard ratio, 1.74; 95% CI: 1.02, 2.96; P = .035). Conclusion Metastatic bone disease from mCRPC can be evaluated and quantified with whole-body DW imaging. Whole-body DW imaging-generated tDV showed correlation with established prognostic biomarkers and is associated with OS in mCRPC. (©) RSNA, 2016 Online supplemental material is available for this article.

Inter- and Intra-Observer Repeatability of Quantitative Whole-Body, Diffusion-Weighted Imaging (WBDWI) in Metastatic Bone Disease

Quantitative whole-body diffusion-weighted MRI (WB-DWI) is now possible using semi-automatic segmentation techniques. The method enables whole-body estimates of global Apparent Diffusion Coefficient (gADC) and total Diffusion Volume (tDV), both of which have demonstrated considerable utility for assessing treatment response in patients with bone metastases from primary prostate and breast cancers. Here we investigate the agreement (inter-observer repeatability) between two radiologists in their definition of Volumes Of Interest (VOIs) and subsequent assessment of tDV and gADC on an exploratory patient cohort of nine. Furthermore, each radiologist was asked to repeat his or her measurements on the same patient data sets one month later to identify the intra-observer repeatability of the technique. Using a Markov Chain Monte Carlo (MCMC) estimation method provided full posterior probabilities of repeatability measures along with maximum a-posteriori values and 95% confidence intervals. Our estimates of the inter-observer Intraclass Correlation Coefficient (ICCinter) for log-tDV and median gADC were 1.00 (0.97–1.00) and 0.99 (0.89–0.99) respectively, indicating excellent observer agreement for these metrics. Mean gADC values were found to have ICCinter = 0.97 (0.81–0.99) indicating a slight sensitivity to outliers in the derived distributions of gADC. Of the higher order gADC statistics, skewness was demonstrated to have good inter-user agreement with ICCinter = 0.99 (0.86–1.00), whereas gADC variance and kurtosis performed relatively poorly: 0.89 (0.39–0.97) and 0.96 (0.69–0.99) respectively. Estimates of intra-observer repeatability (ICCintra) demonstrated similar results: 0.99 (0.95–1.00) for log-tDV, 0.98 (0.89–0.99) and 0.97 (0.83–0.99) for median and mean gADC respectively, 0.64 (0.25–0.88) for gADC variance, 0.85 (0.57–0.95) for gADC skewness and 0.85 (0.57–0.95) for gADC kurtosis. Further investigation of two anomalous patient cases revealed that a very small proportion of voxels with outlying gADC values lead to instability in higher order gADC statistics. We therefore conclude that estimates of median/mean gADC and tumour volume demonstrate excellent inter- and intra-observer repeatability whilst higher order statistics of gADC should be used with caution when ascribing significance to clinical changes.

Body Diffusion-weighted MR Imaging in Oncology: Imaging at 3 T

Advances in hardware and software enable high-quality body diffusion-weighted images to be acquired for oncologic assessment. 3.0 T affords improved signal/noise for higher spatial resolution and smaller field-of-view diffusion-weighted imaging (DWI). DWI at 3.0 T can be applied as at 1.5 T to improve tumor detection, disease characterization, and the assessment of treatment response. DWI at 3.0 T can be acquired on a hybrid PET-MR imaging system, to allow functional MR information to be combined with molecular imaging.

Rapid development of image analysis research tools

We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for all lesions, in agreement with a reduction in concurrent measures of serum prostate-specific antigen (PSA).

Evaluating the Diagnostic Sensitivity of Computed Diffusion-Weighted MR Imaging in the Detection of Breast Cancer

To evaluate the diagnostic sensitivity of computed diffusion‐weighted (DW)‐MR imaging for the detection of breast cancer.

Response evaluation in mesothelioma: Beyond RECIST.

Malignant pleural mesothelioma (MPM) typically demonstrates a non-spherical growth pattern, so it is often difficult to accurately categorize change in tumour burden using size-based tumour response criteria (e.g., WHO (World Health Organisation), RECIST (Response Evaluation Criteria in Solid Tumours) and modified RECIST). Functional imaging techniques are applied to derive quantitative measurements of tumours, which reflect particular aspects of the tumour pathophysiology. By quantifying how these measurements change with treatment, it is possible to observe treatment effects. In this review, we survey the existing roles of CT and MRI for the management of MPM, including the currently applied size measurement criteria for the assessment of treatment response. New functional imaging techniques, such as positron emission tomography (PET), diffusion-weighted MRI (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) that may potentially improve the assessment of treatment response will be highlighted and discussed.