Revealing tumor microstructure with oscillating diffusion encoding MRI in pre-surgical and post-treatment glioma patients.

PURPOSE: We hypothesized that the time-dependent diffusivity at short diffusion times, as measured by oscillating gradient spin echo (OGSE) diffusion MRI, can characterize tissue microstructures in glioma patients. THEORY AND METHODS: Five adult patients with known diffuse glioma, including two pre-surgical and three with new enhancing lesions after treatment for high-grade glioma, were scanned in an ultra-high-performance gradient 3.0T MRI system. OGSE diffusion MRI at 30-100 Hz and pulsed gradient spin echo diffusion imaging (approximated as 0 Hz) were obtained. The ADC and trace-diffusion-weighted image at each acquired frequency were calculated, that is, ADC (f) and TraceDWI (f). RESULTS: In pre-surgical patients, biopsy-confirmed solid enhancing tumor in a high-grade glioblastoma showed higher ADC ( f ) ADC ( 0 Hz ) $$ \frac{\mathrm{ADC}\ (f)}{\mathrm{ADC}\ \left(0\ \mathrm{Hz}\right)} $$ and lower TraceDWI ( f ) TraceDWI ( 0 Hz ) $$ \frac{\mathrm{TraceDWI}\ (f)}{\mathrm{TraceDWI}\ \left(0\ \mathrm{Hz}\right)} $$ , compared to that at same OGSE frequency in a low-grade astrocytoma. In post-treatment patients, the enhancing lesions of two patients who were diagnosed with tumor progression contained more voxels with high ADC ( f ) ADC ( 0 Hz ) $$ \frac{\mathrm{ADC}\ (f)}{\mathrm{ADC}\ \left(0\ \mathrm{Hz}\right)} $$ and low TraceDWI ( f ) TraceDWI ( 0 Hz ) $$ \frac{\mathrm{TraceDWI}\left(\mathrm{f}\right)}{\mathrm{TraceDWI}\left(0\ \mathrm{Hz}\right)} $$ , compared to the enhancing lesions of a patient who was diagnosed with treatment effect. Non-enhancing T2 signal abnormality lesions in both the pre-surgical high-grade glioblastoma and post-treatment tumor progressions showed regions with high ADC ( f ) ADC ( 0 Hz ) $$ \frac{\mathrm{ADC}\ (f)}{\mathrm{ADC}\ \left(0\ \mathrm{Hz}\right)} $$ and low TraceDWI ( f ) TraceDWI ( 0 Hz ) $$ \frac{\mathrm{TraceDWI}\ \left(\mathrm{f}\right)}{\mathrm{TraceDWI}\ \left(0\ \mathrm{Hz}\right)} $$ , consistent with infiltrative tumor. The solid tumor of the glioblastoma, the enhancing lesions of post-treatment tumor progressions, and the suspected infiltrative tumors showed high diffusion time-dependency from 30 to 100 Hz, consistent with high intra-tumoral volume fraction (cellular density). CONCLUSION: Different characteristics of OGSE-based time-dependent diffusivity can reveal heterogenous tissue microstructures that indicate cellular density in glioma patients.

Radiotherapy at oligoprogression for metastatic castration-resistant prostate cancer patients: a multi-institutional analysis.

PURPOSE: To retrospectively estimate the impact of radiotherapy as a progression-directed therapy (PDT) in oligoprogressive metastatic castration-resistant prostate cancer (mCRPC) patients under androgen receptor-target therapy (ARTT). MATERIALS AND METHODS: mCRPC patients are treated with PDT. End-points were time to next-line systemic treatment (NEST), radiological progression-free survival (r-PFS) and overall survival (OS). Toxicity was registered according to Common Terminology Criteria for Adverse Events v4.0. Survival analysis was performed using the Kaplan-Meier method; univariate and multivariate analyses were performed. RESULTS: Fifty-seven patients were analyzed. The median follow-up after PDT was 25.2 months (interquartile, 17.1-44.5). One-year NEST-free survival, r-PFS and OS were 49.8%, 50.4% and 82.1%, respectively. At multivariate analysis, polymetastatic condition at diagnosis of metastatic hormone-sensitive prostate cancer (mHSPC) (HR 2.82, p = 0.004) and PSA doubling time at diagnosis of mCRPC (HR 2.76, p = 0.006) were associated with NEST-free survival. The same variables were associated with r-PFS (HR 2.32, p = 0.021; HR 2.24, p = 0.021). One patient developed late grade ≥ 2 toxicity. CONCLUSION: Our study shows that radiotherapy in oligoprogressive mCRPC is safe, is effective and seems to prolong the efficacy of ARTT in patients who otherwise would have gone systemic treatment switch, positively affecting disease progression. Prospective trials are needed.

Denoising and Multiple Tissue Compartment Visualization of Multi-b-Valued Breast Diffusion MRI.

BACKGROUND: Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times. PURPOSE: To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions. STUDY TYPE: Prospective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b). SUBJECTS: One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b). FIELD STRENGTH/SEQUENCE: 3T multi-shell acquisition and 1.5T multi-b acquisition. ASSESSMENT: The "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity. STATISTICAL TESTS: Parametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue. RESULTS: In the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps. DATA CONCLUSION: Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY STAGE: 1.

Post-mastectomy immediate breast reconstruction and adjuvant radiotherapy: long term results of a mono institutional experience.

INTRODUCTION: The aim of this paper is to investigate the outcome of patients treated with mastectomy, immediate breast reconstruction (IBR) and post-mastectomy radiotherapy (PMRT) and the risk of late complications. MATERIAL AND METHOD: All patients had post-mastectomy, immediate reconstructive surgical procedure by using autologous abdominal implant; tissue expander (TE)/permanent prosthesis (PP); or even combined procedures. Adjuvant external beam radiotherapy treatment (EBRT) was delivered to the reconstructed chest wall and supraclavicular nodes, for a total dose of 50 Gy in 25 fractions. The Kaplan-Meyer analysis evaluates patients' rate of late side effects, Overall Survival (OS), Progression Free survival (PFS), Local-regional free survival (LRFS) and Metastasis Free Survival (MFS). The univariate analysis investigates the correlation between late toxicity and related factors. RESULTS: Between November 2003 and October 2016, 91 breast cancer patients were treated with IBR and PMRT. Twenty-three (25.3%) patients experimented late toxicity. Overall, 16 (17.6%) patients experienced late complications which required a surgical approach. The 1- 2- 5- years late toxicity rates were 96.6%, 87.1% and 77.9%, respectively. The type of reconstruction was not statistically related with late toxicity rate (P = 0.35). The median follow-up period was 59 months (range 6-142 months). Median OS was not reached, the 1- 2- 5-years OS rates were 100%, 95.4% and 81% respectively. CONCLUSION: This study underlines that the type of reconstruction does not influence late toxicity rate. Moreover, IBR followed by adjuvant radiotherapy, has showed acceptable late toxicity profile and no influence on OS.

Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.

LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Inoperable early-stage primary and early recurrent non-small cell lung cancer: outcomes of a mono-institutional experience using a moderate hypofractionated schedule.

BACKGROUND: Patients with medically inoperable early-stage non-small cell lung cancer (NSCLC) may beneficiate of a hypofractionated radiation therapy in order to intensificate the treatment and to reduce the number of hospital access. METHODS: From 2007 to 2015, 27 patients with early-stage primary or limited loco-regional recurrent (T2a > 4 cm, T2b N0 or T1-2 N1M0) NSCLC were treated. All patients were medically inoperable or refused surgery and were treated with 60 Gy in 20 fractions, 5 times per week. Thirteen (48.1%) presented limited recurrence after surgery and 14 (51.9%) primary disease. RESULTS: Median follow-up was 34 months. Twelve patients achieved a CR (44.4%) and 8 a PR (29.6%) with a tumour response rate of 74%. Median overall survival (OS) and 2-year OS were 34 months and 63.0%, respectively. Median and 2-year loco-regional progression-free survival (LR-PFS) were 31 months and 51.4%, respectively. Survival outcomes were statistically favourable in patients with partial or complete response with respect to patients with stable or progressive disease, whereas stage (N0 vs N1) and primary or relapse/recurrent disease not. No cases of acute toxicity > grade 2 were observed. Seven patients (25.9%) presented grade 2 late toxicities. CONCLUSION: Sixty Gy in 20 fractions is well tolerated and achieves good clinical outcomes in early primary or recurrent NSCLC patients. A greater number of patients and a longer follow-up are necessary to confirm the results obtained with our treatment.

Locally advanced inoperable primary or recurrent non-small cell lung cancer treated with 4-week hypofractionated radiation therapy (3 Gy/fraction).

BACKGROUND: The prognosis of locally advanced non-small cell lung cancer (NSCLC) treated with conventional radiotherapy remains poor. Hypofractionation reduces overall treatment time increasing biological effect in patients not suitable for concurrent chemo-radiotherapy. METHOD: From January 2009 to October 2016, 76 inoperable locally advanced primary or recurrent NSCLC patients were treated with 60 Gy in 20 fractions of 3 Gy/each for 4 weeks as exclusive or post-chemotherapy treatment. Fifty-eight patients (76.3%) had stage III and 18 (23.7%) stage IV (≤ 2 metastases) disease: 63 primary (82.9%) and 13 recurrent (17.1%). RESULTS: Median and 2-year overall survival were 17 months and 38.9%, respectively. Median and 2-year loco-regional progression free survival were 27 months and 55.3%, respectively. Univariate and multivariate analyses demonstrated that patients with complete response presented better outcomes, whereas no statistically relevant difference was evidenced in terms of previous chemotherapy, recurrent vs primary disease, volume and stage. Thirty patients (39.5%) presented acute esophagitis (1-grade 3) and 19 (25.0%) acute pneumonitis (2-grade 3). Six patients (7.9%) developed grade 2-3 late pneumonitis and 3 patients (3.9%) grade 1 late esophagitis. CONCLUSION: In patients not suitable of concurrent radio-chemotherapy, exclusive or sequential hypofractionated schedule using 60 Gy in 20 fractions was well tolerated and presented promising results. Complete local response was a predictor of better outcomes, and any efforts will be made to perform prospective clinical trials to further evaluate hypofractionated regimens with increased lesional BED.

Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution.

PURPOSE: Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation. METHODS: A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods. RESULTS: We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient. CONCLUSION: The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes.

PURPOSE: Diffusional kurtosis imaging (DKI) is an approach to characterizing the non-Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal-to-noise ratio of diffusion-weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision and bias using weighted linear least squares fitting of different acquisition schemes including several multishell schemes, a diffusion spectrum imaging (DSI) scheme, as well as a compressed sensing reconstruction of undersampled DSI scheme. METHODS: Monte Carlo simulations were performed to study the three-dimensional distribution of the apparent kurtosis coefficient (AKC). Experimental data were acquired from one healthy volunteer with multiple repetitions, using the same acquisition schemes as for the simulations. RESULTS: The angular distribution of the bias and precision were very inhomogeneous. While axial kurtosis was significantly overestimated, radial kurtosis was underestimated. The precision of radial kurtosis was up to 10-fold lower than axial kurtosis. CONCLUSION: The noise bias behavior of DKI is highly complex and can cause overestimation as well as underestimation of the AKC even within one voxel. The acquisition scheme with three shells, suggested by Poot et al, provided overall the best performance. Magn Reson Med 76:1684-1696, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Multi-directional anisotropy from diffusion orientation distribution functions.

PURPOSE: To evaluate a model-independent, multi-directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single-direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi-directional diffusivity. MATERIALS AND METHODS: An expression for MDA was defined from the orientation distribution function (ODF) and its analytical relation to FA was derived. Simulations of single and crossed double-fibers were performed using a compressed-sensing-accelerated diffusion-spectrum-imaging (CS-DSI) scheme. In vivo brain imaging using CS-DSI was performed on eight healthy subjects. MDA was compared with FA and with another ODF-based metric known as generalized FA (GFA). RESULTS: In simulated single-direction fibers, MDA was shown to be equivalent to FA (from FA = 0.2 to 0.8). In crossed fibers, MDA provided superior differentiation of the underlying anisotropy as compared to FA and GFA. In vivo analysis shows that the MDA was superior to both FA (P = 0.015) and GFA (P = 0.021) in terms of its relative accuracy in crossed fiber regions. CONCLUSION: MDA provides a potentially superior measure of fiber anisotropy relative to conventional FA or GFA, and may be used to improve the assessment of disease in regions with multi-directional brain fibers.

Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial.

PURPOSE: To evaluate a gradient nonlinearity correction (GNC) program for quantitative apparent diffusion coefficient (ADC) measurements on phantom and human subject diffusion-weighted (DW) magnetic resonance imaging (MRI) scans in a multicenter breast cancer treatment response study MATERIALS AND METHODS: A GNC program using fifth-order spherical harmonics for gradient modeling was applied retrospectively to qualification phantom and human subject scans. Ice-water phantoms of known diffusion coefficient were scanned at five different study centers with different scanners and receiver coils. Human in vivo data consisted of baseline and early-treatment exams on 54 patients from four sites. ADC maps were generated with and without GNC. Regions of interest were defined to quantify absolute errors and changes with GNC over breast imaging positions. RESULTS: Phantom ADC errors varied with region of interest (ROI) position and scanner configuration; the mean error by configuration ranged from 1.4% to 19.9%. GNC significantly reduced the overall mean error for all sites from 9.9% to 0.6% (P = 0.016). Spatial dependence of GNC was highest in the right-left (RL) and anterior-posterior (AP) directions. Human subject mean tumor ADC was reduced 0.2 to 12% by GNC at different sites. By regression, every 1-cm change in tumor ROI position between baseline and follow-up visits resulted in an estimated change of 2.4% in the ADC early-treatment response measurement. CONCLUSION: GNC is effective for removing large, system-dependent errors in quantitative breast DWI. GNC may be important in ensuring reproducibility in multicenter studies and in reducing errors in longitudinal treatment response measures arising from spatial variations in tumor position between visits.

Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.

OBJECTIVE: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit. METHODS AND PROCEDURES: We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization. RESULTS: The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively. CONCLUSION: The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement-A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.

Vertebral and Femoral Bone Mineral Density (BMD) Assessment with Dual-Energy CT versus DXA Scan in Postmenopausal Females.

This study aimed to demonstrate the potential role of dual-energy CT in assessing bone mineral density (BMD) using hydroxyapatite-fat material pairing in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using a HAP-fat material pair. Statistical analysis was performed using the Bland-Altman method to assess the agreement between DXA and DECT HAP-fat measurements. Mean BMD, vertebral, and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP-fat values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) was found for measurements at the femoral neck. In conclusion, we demonstrate the enhanced importance of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Improved correction for gradient nonlinearity effects in diffusion-weighted imaging.

PURPOSE: To provide an improved correction for gradient nonlinearity (GN) effects in diffusion-weighted imaging (DWI). These effects produce spatially varying apparent diffusion coefficient (ADC), a result that will be significant in large field-of-view imaging, and may be confounded by distortion and concomitant fields related to the DWI acquisition. MATERIALS AND METHODS: The effect of more accurate gradient field maps on GN correction (GNC) of ADC was evaluated. A simulation compared GN effects in commonly imaged anatomy. A temperature-controlled phantom was imaged at positions 0 cm and 11 cm from isocenter and in two whole-body MRI systems at 1.5T with different patient bore diameters (55 cm and 60 cm). Varying correction methods were applied to determine the errors from spatial variance and interscanner reproducibility. RESULTS: As compared to conventional fifth-order spherical harmonics, a seventh-order GNC improved ADC accuracy by 1%. The combination of GNC with a dual-spin-echo pulse sequence and a retrospective concomitant field correction reduced ADC error due to spatial variance from 9.5% to 1.8% (55 cm bore) and from 4.2% to 1.8% (60 cm bore). The error in ADC attributed to interscanner reproducibility was reduced from 5.8% to 0.15% (at isocenter) and from 10% to 0.63% (11 cm from isocenter). CONCLUSION: GNC in DWI improved spatial accuracy and interscanner reproducibility of ADC.

Modified cine inversion recovery pulse sequence for the quantification of myocardial T1 and gadolinium partition coefficient.

PURPOSE: To optimize and validate a modified cine inversion recovery sequence (MCine-IR) for myocardial T1 quantification and gadolinium partition coefficient (λ(Gd)) estimation at 1.5 T. MATERIALS AND METHODS: The original version of the cine inversion recovery sequence was modified to allow fully transverse magnetization recovery between two successive inversion pulses. Sixty heart phases were acquired from a number of heart cycles determined on a patient heart rate basis. Phantom studies were carried out to find the optimal effective TR for myocardial and blood pool T1 quantifications in pre- and postcontrast studies. Four patients with myocardial infarct (MI) and 22 dilated cardiomyopathy (DCM) were investigated, as well as 11 healthy subjects used as controls. RESULTS: Effective TR was identified to be 5000 msec and 2000 msec, respectively, for pre- and postcontrast studies. A longer precontrast (948 ± 102 msec) and shorter postcontrast (348 ± 27 msec) T1 in ischemic patients relative to DCM (815 ± 98 msec, P = 0.03 and 409 ± 42 msec, P = 0.001) were noted in delayed enhancement (DE) areas. In MI patients λ(Gd) resulted higher than in DCM in DE areas (609 ± 167 vs. 422 ± 52, P = 0.01) but lower in segments not exhibiting DE (355 ± 100 vs. 398 ± 54, P = 0.02). CONCLUSION: It was feasible to measure T1 and λ(Gd) with MCine-IR and the results were in good agreement with the literature.

Accelerated MR imaging using compressive sensing with no free parameters.

We describe and evaluate a robust method for compressive sensing MRI reconstruction using an iterative soft thresholding framework that is data-driven, so that no tuning of free parameters is required. The approach described here combines a Nesterov type optimal gradient scheme for iterative update along with standard wavelet-based adaptive denoising methods, resulting in a leaner implementation compared with the nonlinear conjugate gradient method. Tests with T2 weighted brain data and vascular 3D phase contrast data show that the image quality of reconstructions is comparable with those from an empirically tuned nonlinear conjugate gradient approach. Statistical analysis of image quality scores for multiple datasets indicates that the iterative soft thresholding approach as presented here may improve the robustness of the reconstruction and the image quality, when compared with nonlinear conjugate gradient that requires manual tuning for each dataset. A data-driven approach as illustrated in this article should improve future clinical applicability of compressive sensing image reconstruction.

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method.

Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial feasibility study, we collected ultrasound color Doppler images of femoral arteries in an animal model of vascular injury (N = 11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.

Long-Term Stability of Gradient Characteristics Warrants Model-Based Correction of Diffusion Weighting Bias.

The study aims to test the long-term stability of gradient characteristics for model-based correction of diffusion weighting (DW) bias in an apparent diffusion coefficient (ADC) for multisite imaging trials. Single spin echo (SSE) DWI of a long-tube ice-water phantom was acquired quarterly on six MR scanners over two years for individual diffusion gradient channels, along with B0 mapping, as a function of right-left (RL) and superior-inferior (SI) offsets from the isocenter. Additional double spin-echo (DSE) DWI was performed on two systems. The offset dependences of derived ADC were fit to 4th-order polynomials. Chronic shim gradients were measured from spatial derivatives of B0 maps along the tube direction. Gradient nonlinearity (GNL) was modeled using vendor-provided gradient field descriptions. Deviations were quantified by root-mean-square differences (RMSD), normalized to reference ice-water ADC, between the model and reference (RMSDREF), measurement and model (RMSDEXP), and temporal measurement variations (RMSDTMP). Average RMSDREF was 4.9 ± 3.2 (%RL) and -14.8 ± 3.8 (%SI), and threefold larger than RMSDEXP. RMSDTMP was close to measurement errors (~3%). GNL-induced bias across gradient systems varied up to 20%, while deviation from the model accounted at most for 6.5%, and temporal variation for less than 3% of ADC reproducibility error. Higher SSE RMSDEXP = 7.5-11% was reduced to 2.5-4.8% by DSE, consistent with the eddy current origin. Measured chronic shim gradients below 0.1 mT/m had a minor contribution to ADC bias. The demonstrated long-term stability of spatial ADC profiles and consistency with system GNL models justifies retrospective and prospective DW bias correction based on system gradient design models. Residual errors due to eddy currents and shim gradients should be corrected independent of GNL.

Accelerated diffusion spectrum imaging in the human brain using compressed sensing.

We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4.

Localization of Arterial Bleeds Using Pulse Wave Reflections.

INTRODUCTION: Localization of internal arterial bleeds is necessary for treatment in the battlefield. In this article, we describe a novel approach that utilizes pulse wave reflections generated by a bleed to locate it. MATERIALS AND METHODS: To demonstrate our approach, velocity and diameter waveforms in the presence of bleeds were simulated using the 1D wave propagation equations in a straight-vessel model of the human thoracic aorta. The simulated waveforms were then decomposed into forward and backward components using wave intensity analysis. Reflections arising from the bleed were identified from the decomposed waveforms. RESULTS: Reflection generated by the bleed introduced a new feature in the backward component, compared to the normal, no-bleed condition. The bleed location could be determined from the time delay between this reflection feature and the forward wave creating it, and the pulse wave velocity in the vessel. CONCLUSIONS: The findings of this study could be utilized by ultrasound for hemorrhage localization.