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.

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.

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.

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.

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.

Optimization of 7-T chemical exchange saturation transfer parameters for validation of glycosaminoglycan and amide proton transfer of fibroglandular breast tissue.

PURPOSE: To (a) implement simulation-optimized chemical exchange saturation transfer (CEST) measurements sensitive to amide proton transfer (APT) and glycosaminoglycan (GAG) hydroxyl proton transfer effects in the human breast at 7 T and (b) determine the reliability of these techniques for evaluation of fibroglandular tissue in the healthy breast as a benchmark for future studies of pathologic findings. MATERIALS AND METHODS: All human studies were institutional review board approved, were HIPAA compliant, and included informed consent. The CEST parameters of saturation duration (25 msec) and amplitude (1 µT) were chosen on the basis of simulation-driven optimization for APT contrast enhancement with the CEST effect quantified by using residuals of a Lorentzian fit. Optimized parameters were implemented at 7 T in 10 healthy women in two separate examinations to evaluate the reliability of CEST magnetic resonance (MR) imaging measurements in the breast. CEST z-spectra were acquired over saturation offset frequencies ranging between ±40 ppm by using a quadrature unilateral breast coil. The imaging-repeat imaging reliability was assessed in terms of the intraclass correlation coefficient, which indicates the ratio of between-subject variation to total variation. RESULTS: Simulations were performed of the Bloch equations with chemical exchange-guided selection of optimal values for pulse duration and amplitude, 25 msec and 1 µT, respectively. Reliability was evaluated by using intraclass correlation coefficients (95% confidence intervals), with acceptable results: 0.963 (95% confidence interval: 0.852, 0.991) and 0.903 (95% confidence interval: 0.609, 0.976) for APT and GAG, respectively. CONCLUSION: Simulations were used to derive optimal CEST preparation parameters to elicit maximal CEST contrast enhancement in healthy fibroglandular breast tissue due to APT at 7 T. By using these parameters, reproducible values were obtained for both the amide and hydroxyl protons from CEST MR imaging at 7 T and are feasible in the human breast.

DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings.

PURPOSE: The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response. METHODS: Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. RESULTS: Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. CONCLUSION: In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.

Sequence design and evaluation of the reproducibility of water-selective diffusion-weighted imaging of the breast at 3 T.

Diffusion measurements derived from breast MRI can be adversely affected by unwanted signals from abundant fatty tissues if they are not suppressed adequately. To minimize this undesired contribution, we designed and optimized a water-selective diffusion-weighted imaging (DWI) sequence, which relies on spectrally selective excitation on the water resonance, obviating the need for fat suppression. As this method is more complex than standard DWI methods, we also report a test-retest study to evaluate its reproducibility. In this study, a spectrally selective Gaussian pulse on water resonance was combined with a pair of slice-selective adiabatic refocusing pulses for water-only DWI. Field map-based shimming and manual determination of the center frequency were used for water selection. The selectivity of the excitation pulse was optimized by a spectrally selective spectroscopy sequence based on the same principles. A test-retest study of 10 volunteers in two separate visits was used to evaluate its reproducibility. Our results from all subjects showed high-quality diffusion-weighted images of the breast without fat contamination. Mean apparent diffusion coefficients for b = 0, 600 s/mm(2) and b = 50, 600 s/mm(2) all showed good reproducibility, as 95% confidence intervals of the apparent diffusion coefficients were 4 × 10(-5) mm(2) /s and 5 × 10(-5) mm(2) /s and repeatability values were 1.09 × 10(-4) and 1.31 × 10(-4) , respectively. In conclusion, water-selective DWI is a feasible alternative to standard methods of DWI based on fat suppression. The added complexity of the method does not compromise the reproducibility of diffusion measurements in the breast.

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.

Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response.

Chemical exchange saturation transfer imaging can generate contrast that is sensitive to amide protons associated with proteins and peptides (termed amide proton transfer, APT). In breast cancer, APT contrast may report on underlying changes in microstructural tissue composition. However, to date, there have been no developments or applications of APT chemical exchange saturation transfer to breast cancer. As a result, the aims of this study were to (i) experimentally explore optimal scan parameters for breast chemical exchange saturation transfer near the amide resonance at 3 T, (ii) establish the reliability of APT imaging of healthy fibroglandular tissue, and (iii) demonstrate preliminary results on APT changes in locally advanced breast cancer observed during the course of neoadjuvant chemotherapy. Chemical exchange saturation transfer measurements were experimentally optimized on cross-linked bovine serum albumin phantoms, and the reliability of APT imaging was assessed in 10 women with no history of breast disease. The mean difference between test-retest APT values was not significantly different from zero, and the individual difference values were not dependent on the average APT value. The 95% confidence interval limits were ±0.70% (α = 0.05), and the repeatability was 1.91. APT measurements were also performed in three women before and after one cycle of chemotherapy. Following therapy, APT increased in the one patient with progressive disease and decreased in the two patients with a partial or complete response. Together, these results suggest that APT imaging may report on treatment response in these patients.

Amide proton transfer imaging of the human breast at 7T: development and reproducibility.

Chemical exchange saturation transfer (CEST) can offer information about protons associated with mobile proteins through the amide proton transfer (APT) effect, which has been shown to discriminate tumor from healthy tissue and, more recently, has been suggested as a prognosticator of response to therapy. Despite this promise, APT effects are small (only a few percent of the total signal), and APT imaging is often prone to artifacts resulting from system instability. Here we present a procedure that enables the detection of APT effects in the human breast at 7T while mitigating these issues. Adequate signal-to-noise ratio (SNR) was achieved via an optimized quadrature RF breast coil and 3D acquisitions. To reduce the influence of fat, effective fat suppression schemes were developed that did not degrade SNR. To reduce the levels of ghosting artifacts, dummy scans have been integrated into the scanning protocol. Compared with results obtained at 3T, the standard deviation of the measured APT effect was reduced by a factor of four at 7T, allowing for the detection of APT effects with a standard deviation of 1% in the human breast at 7T. Together, these results demonstrate that the APT effect can be reliably detected in the healthy human breast with a high level of precision at 7T.

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.

Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer.

By fitting dynamic contrast-enhanced MRI data to an appropriate pharmacokinetic model, quantitative physiological parameters can be estimated. In this study, we compare four different models by applying four statistical measures to assess their ability to describe dynamic contrast-enhanced MRI data obtained in 28 human breast cancer patient sets: the chi-square test (χ(2)), Durbin-Watson statistic, Akaike information criterion, and Bayesian information criterion. The pharmacokinetic models include the fast exchange limit model with (FXL_v(p)) and without (FXL) a plasma component, and the fast and slow exchange regime models (FXR and SXR, respectively). The results show that the FXL_v(p) and FXR models yielded the smallest χ(2) in 45.64 and 47.53% of the voxels, respectively; they also had the smallest number of voxels showing serial correlation with 0.71 and 2.33%, respectively. The Akaike information criterion indicated that the FXL_v(p) and FXR models were preferred in 42.84 and 46.59% of the voxels, respectively. The Bayesian information criterion also indicated the FXL_v(p) and FXR models were preferred in 39.39 and 45.25% of the voxels, respectively. Thus, these four metrics indicate that the FXL_v(p) and the FXR models provide the most complete statistical description of dynamic contrast-enhanced MRI time courses for the patients selected in this study.

Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.

Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 (P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 (P < 0.05).

Motion correction in diffusion-weighted MRI of the breast at 3T.

PURPOSE: To provide a quantitative assessment of motion and distortion correction of diffusion-weighted images (DWIs) of the breast and to evaluate the effects of registration on the mean apparent diffusion coefficient (mADC). MATERIALS AND METHODS: Eight datasets from four patients with breast cancer and eight datasets from six healthy controls were acquired on a 3T scanner. A 3D affine registration was used to align each set of images and principal component analysis was used to assess the results. Variance in tumor ADC measurements, tumor mADC values, and voxel-wise tumor mADC values were compared before and after registration for each patient. RESULTS: Image registration significantly (P = 0.008) improved image alignment for both groups and significantly (P < 0.001) reduced the variance across individual tumor ADC measurements. While misalignment led to potential under- and overestimation of mADC values for individual voxels, average tumor mADC values did not significantly change (P > 0.09) after registration. CONCLUSION: 3D affine registration improved the alignment of DWIs of the breast and reduced the variance between ADC measurements. Although the reduced variance did not significantly change tumor region-of-interest measures of mADC, it may have a significant impact on voxel-based analyses.

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.

Spatial normalization of the fiber orientation distribution based on high angular resolution diffusion imaging data.

Comparison of high angular resolution diffusion imaging (HARDI) measurements between subjects or between timepoints for the same subject are facilitated by spatial normalization. In this work an algorithm was developed to transform the fiber orientation distribution (FOD) function, based on HARDI data, taking into account not only translation, but also rotation, scaling, and shearing effects of the spatial transformation. The algorithm was tested using simulated data and intrasubject and intersubject normalization of in vivo human data. All cases demonstrated reliable transformation of the FOD. This technique makes it possible to compare the intravoxel fiber distribution between subjects, between groups, or between timepoints for a single subject, which will be helpful in HARDI studies of white matter disease.

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.