Detection of Acute Brain Injury in Intensive Care Unit Patients on ECMO Support Using Ultra-Low-Field Portable MRI: A Retrospective Analysis Compared to Head CT.

Early detection of acute brain injury (ABI) is critical to intensive care unit (ICU) patient management and intervention to decrease major complications. Head CT (HCT) is the standard of care for the assessment of ABI in ICU patients; however, it has limited sensitivity compared to MRI. We retrospectively compared the ability of ultra-low-field portable MR (ULF-pMR) and head HCT, acquired within 24 h of each other, to detect ABI in ICU patients supported on extracorporeal membrane oxygenation (ECMO). A total of 17 adult patients (median age 55 years; 47% male) were included in the analysis. Of the 17 patients assessed, ABI was not observed on either ULF-pMR or HCT in eight patients (47%). ABI was observed in the remaining nine patients with a total of 10 events (8 ischemic, 2 hemorrhagic). Of the eight ischemic events, ULF-pMR observed all eight, while HCT only observed four events. Regarding hemorrhagic stroke, ULF-pMR observed only one of them, while HCT observed both. ULF-pMR outperformed HCT for the detection of ABI, especially ischemic injury, and may offer diagnostic advantages for ICU patients. The lack of sensitivity to hemorrhage may improve with modification of the imaging acquisition program.

Clinical Use of Bedside Portable Low-field Brain Magnetic Resonance Imaging in Patients on ECMO: The Results from Multicenter SAFE MRI ECMO Study.

Purpose: Early detection of acute brain injury (ABI) is critical for improving survival for patients with extracorporeal membrane oxygenation (ECMO) support. We aimed to evaluate the safety of ultra-low-field portable MRI (ULF-pMRI) and the frequency and types of ABI observed during ECMO support. Methods: We conducted a multicenter prospective observational study (NCT05469139) at two academic tertiary centers (August 2022-November 2023). Primary outcomes were safety and validation of ULF-pMRI in ECMO, defined as exam completion without adverse events (AEs); secondary outcomes were ABI frequency and type. Results: ULF-pMRI was performed in 50 patients with 34 (68%) on venoarterial (VA)-ECMO (11 central; 23 peripheral) and 16 (32%) with venovenous (VV)-ECMO (9 single lumen; 7 double lumen). All patients were imaged successfully with ULF-pMRI, demonstrating discernible intracranial pathologies with good quality. AEs occurred in 3 (6%) patients (2 minor; 1 serious) without causing significant clinical issues.ABI was observed in ULF-pMRI scans for 22 patients (44%): ischemic stroke (36%), intracranial hemorrhage (6%), and hypoxic-ischemic brain injury (4%). Of 18 patients with both ULF-pMRI and head CT (HCT) within 24 hours, ABI was observed in 9 patients with 10 events: 8 ischemic (8 observed on ULF-oMRI, 4 on HCT) and 2 hemorrhagic (1 observed on ULF-pMRI, 2 on HCT). Conclusions: ULF-pMRI was shown to be safe and valid in ECMO patients across different ECMO cannulation strategies. The incidence of ABI was high, and ULF-pMRI may more sensitive to ischemic ABI than HCT. ULF-pMRI may benefit both clinical care and future studies of ECMO-associated ABI.

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner.

Purpose: Portable magnetic resonance imaging (pMRI) has potential to rapidly acquire images at the patients' bedside to improve access in locations lacking MRI devices. The scanner under consideration has a magnetic field strength of 0.064 T, thus image-processing algorithms to improve image quality are required. Our study evaluated pMRI images produced using a deep learning (DL)-based advanced reconstruction scheme to improve image quality by reducing image blurring and noise to determine if diagnostic performance was similar to images acquired at 1.5 T. Approach: Six radiologists viewed 90 brain MRI cases (30 acute ischemic stroke (AIS), 30 hemorrhage, 30 no lesion) with T1, T2, and fluid attenuated inversion recovery sequences, once using standard of care (SOC) images (1.5 T) and once using pMRI DL-based advanced reconstruction images. Observers provided a diagnosis and decision confidence. Time to review each image was recorded. Results: Receiver operating characteristic area under the curve revealed overall no significant difference (p=0.0636) between pMRI and SOC images. Examining each abnormality, for acute ischemic stroke, there was a significant difference (p=0.0042) with SOC better than pMRI; but for hemorrhage, there was no significant difference (p=0.1950). There was no significant difference in viewing time for pMRI versus SOC (p=0.0766) or abnormality (p=0.3601). Conclusions: The deep learning (DL)-based reconstruction scheme to improve pMRI was successful for hemorrhage, but for acute ischemic stroke the scheme could still be improved. For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, although radiologists should be aware of limitations of low-field MRI devices in overall quality and take that into account when diagnosing. As an initial triage to aid in the decision of whether to transport or keep patients on site, pMRI images likely provide enough information.

MRI-cytometry: Mapping nonparametric cell size distributions using diffusion MRI.

PURPOSE: This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. METHODS: MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. RESULTS: Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. CONCLUSION: The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.

Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer.

Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t0 ; n = 46), after cycle 1 (t1 ; n = 1) or 2 (t2 ; n = 10), or after all neoadjuvant therapy (t3 ; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUVmax , SUVpeak , SULmax , and SULpeak . PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.

Magnetic resonance imaging of mean cell size in human breast tumors.

PURPOSE: Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell-cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell-size imaging for clinical applications. METHODS: We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion-weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients. RESULTS: With fast acquisitions (~7 minutes), IMPULSED can provide high-resolution (1.3 mm in-plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size. CONCLUSION: The proposed IMPULSED method can assess cell-size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.

Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery.

Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity. Therefore, the ability to determine patient-specific in vivo breast tissue properties would be advantageous for these procedural applications. While some in vivo elastography methods are not quantitative and others do not measure material properties under deformation conditions that are appropriate to the application of concern, in this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. More specifically, reconstruction of mechanical properties appropriate for the standard-of-care supine lumpectomy was performed by iteratively fitting two anatomical images before and after deformations taking place in the supine breast configuration. The method proposed is workflow-friendly, quantitative, and uses a noncontact, gravity-induced deformation source.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results.

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network.

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

Dual Src and EGFR inhibition in combination with gemcitabine in advanced pancreatic cancer: phase I results : A phase I clinical trial.

Pancreatic adenocarcinoma remains a major therapeutic challenge, as the poor (<8%) 5-year survival rate has not improved over the last three decades. Our previous preclinical data showed cooperative attenuation of pancreatic tumor growth when dasatinib (Src inhibitor) was added to erlotinib (EGFR inhibitor) and gemcitabine. Thus, this study was designed to determine the maximum-tolerated dose of the triplet combination. Standard 3 + 3 dose escalation was used, starting with daily oral doses of 70 mg dasatinib and 100 mg erlotinib with gemcitabine on days 1, 8, and 15 (800 mg/m2) of a 28-day cycle (L0). Nineteen patients were enrolled, yet 18 evaluable for dose-limiting toxicities (DLTs). One DLT observed at L0, however dasatinib was reduced to 50 mg (L-1) given side effects observed in the first two patients. At L-1, a DLT occurred in 1/6 patients and dose was re-escalated to L0, where zero DLTs reported in next four patients. Dasatinib was escalated to 100 mg (L1) where 1/6 patients experienced a DLT. Although L1 was tolerable, dose escalation was stopped as investigators felt L1 was within the optimal therapeutic window. Most frequent toxicities were anemia (89%), elevated aspartate aminotransferase (79%), fatigue (79%), nausea (79%), elevated alanine aminotransferase (74%), lymphopenia (74%), leukopenia (74%), neutropenia (63%), and thrombocytopenia (63%), most Grade 1/2. Stable disease as best response was observed in 69% (9/13). Median progression-free and overall survival was 3.6 and 8 months, respectively. Dasatinib, erlotinib, and gemcitabine was safe with manageable side effects, and with encouraging preliminary clinical activity in advanced pancreatic cancer.

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.

PURPOSE: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. METHODS: We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. RESULTS: NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). CONCLUSION: TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation.

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging.

PURPOSE: To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue. METHODS: Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET. RESULTS: In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUVpeak and SUVmax measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 min of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92. CONCLUSIONS: Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations.

Phase I trial of vorinostat added to chemoradiation with capecitabine in pancreatic cancer.

BACKGROUND AND PURPOSE: This single institution phase I trial determined the maximum tolerated dose (MTD) of concurrent vorinostat and capecitabine with radiation in non-metastatic pancreatic cancer. MATERIAL AND METHODS: Twenty-one patients received escalating doses of vorinostat (100-400mg daily) during radiation. Capecitabine was given 1000mg q12 on the days of radiation. Radiation consisted of 30Gy in 10 fractions. Vorinostat dose escalation followed the standard 3+3 design. No dose escalation beyond 400mg vorinostat was planned. Diffusion-weighted (DW)-MRI pre- and post-treatment was used to evaluate in vivo tumor cellularity. RESULTS: The MTD of vorinostat was 400mg. Dose limiting toxicities occurred in one patient each at dose levels 100mg, 300mg, and 400mg: 2 gastrointestinal toxicities and one thrombocytopenia. The most common adverse events were lymphopenia (76%) and nausea (14%). The apparent diffusion coefficient (ADC) increased in most tumors. Nineteen (90%) patients had stable disease, and two (10%) had progressive disease at time of surgery. Eleven patients underwent surgical exploration with four R0 resections and one R1 resection. Median overall survival was 1.1years (95% confidence interval 0.78-1.35). CONCLUSIONS: The combination of vorinostat 400mg daily M-F and capecitabine 1000mg q12 M-F with radiation (30Gy in 10 fractions) was well tolerated with encouraging median overall survival.

Quantitative Magnetization Transfer Imaging of the Breast at 3.0 T: Reproducibility in Healthy Volunteers.

Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue. Thirteen women with no history of breast disease were scanned twice within a single scanning session, with repositioning between scans. Eleven women had appreciable FG tissue for test-retest measurements. Mean PSR values for the FG tissue ranged from 9.5% to 16.7%. The absolute value of the difference between 2 mean PSR measurements for each volunteer ranged from 0.1% to 2.1%. The 95% confidence interval for the mean difference was ±0.75%, and the repeatability value was 2.39%. These results indicate that the expected measurement variability would be ±0.75% for a cohort of a similar size and would be ±2.39% for an individual, suggesting that future studies of change in PSR in patients with breast cancer are feasible.

Bloch-Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T.

Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch-Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast. Test-retest MRI sessions were performed on 16 women with no history of breast disease. T1 was calculated using the VFA sequence, and B1 field variations were measured using the Bloch-Siegert methodology. As a gold standard, inversion recovery (IR) measurements of T1 were performed. Fibroglandular tissue and adipose tissue from each breast were segmented using the IR images, and the mean T1 was calculated for each tissue. Accuracy was evaluated by percent error (%err). Reproducibility was assessed via the 95% confidence interval (CI) of the mean difference and repeatability coefficient (r). After B1 correction, %err significantly (P < .001) decreased from 17% to 8.6%, and the 95% CI and r decreased from ±94 to ±38 milliseconds and from 276 to 111 milliseconds, respectively. Similar accuracy and reproducibility results were observed in the adipose tissue of the right breast and in both tissues of the left breast. Our data show that Bloch-Siegert B1 mapping improves accuracy and precision of VFA-derived T1 measurements in the breast.

MR Imaging Biomarkers in Oncology Clinical Trials.

The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.

PURPOSE: Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. METHODS: Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ± 150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients, and eddy currents were assessed independently. The observed bias errors were compared with numerical models. RESULTS: The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between -55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (± 5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image coregistration of individual gradient directions. CONCLUSION: The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies.

QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials.

Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.