Fast and contrast-enhanced phase-sensitive magnetic resonance imaging

Phase-sensitive magnetic resonance (MR) imaging has a number of important clinical applications, such as phase-sensitive inversion recovery (PSIR) and Dixon water/fat imaging. PSIR and Dixon techniques are widely used in neurological and body imaging to improve tissue-contrast, the former by extending the dynamic range of image intensity and the later by suppressing unnecessary fat signals. Several important limitations, however, occur in these techniques: (1) Dixon techniques cannot decompose two signals if the resonance frequencies are close. For example, in MR mammography, it is difficult to separate silicone breast implants signals (4.0 ppm) from fat signals (3.5 ppm); (2) the signal dynamic range of images acquired using Dixon techniques is limited by the equilibrium magnetization; and (3) long image acquisition time. These limitations have hindered the applications of phase-sensitive Dixon imaging techniques on breast implant imaging or as a screening tool where fast acquisition is required. In this work, novel phase-sensitive MRI techniques were developed to enhance the capability, image-contrast, and scan-efficiency of Dixon imaging techniques. Specifically, we developed (1) a generalized chemical-shift imaging technique to separate spectrally overlapped signals both T1-contrast and chemical-shift; (2) a contrast-enhanced Dixon technique to extend the signal dynamic range of Dixon images; and (3) a single-echo acquisition (SEA) imaging technique integrated with phase-sensitive MR imaging to provide ultra-fast image acquisitions. Phantom studies, performed on 1.5 T and 4.7 T MR scanners, demonstrated the developed generalized chemical-shift imaging technique could clearly separate breast silicone implant signals (4.0 ppm) from fat (3.5 ppm). The contrast-enhanced Dixon technique, by extending the dynamic range of signal intensity from positive levels to positive/negative levels, could improve image-contrast by 1.6 times, compared with a conventional single-point Dixon technique. Phantom studies, using a 64-channel SEA imaging system, showed the integrated Dixon technique with SEA could acquire decomposed 2-D water-only and fat-only images with ultra-fast frame-rates up to 1/TR, while providing improved image-contrast (by 2.4 times in this experiment) compared with a conventional SEA imaging technique

Deep Learning for Automatic Detection and Segmentation from CT Angiography of Deep Inferior Epigastric Vascular Structures for Preoperative Planning of TRAM Flap Surgeries

https://openworks.mdanderson.org/sumexp23/1131/thumbnail.jp

Oscillatory spin-orbit torque switching induced by field-like torques

Deterministic magnetization switching using spin-orbit torque (SOT) has recently emerged as an efficient means to electrically control the magnetic state of ultrathin magnets. The SOT switching still lacks in oscillatory switching characteristics over time, therefore, it is limited to bipolar operation where a change in polarity of the applied current or field is required for bistable switching. The coherent rotation based oscillatory switching schemes cannot be applied to SOT because the SOT switching occurs through expansion of magnetic domains. Here, we experimentally achieve oscillatory switching in incoherent SOT process by controlling domain wall dynamics. We find that a large field-like component can dynamically influence the domain wall chirality which determines the direction of SOT switching. Consequently, under nanosecond current pulses, the magnetization switches alternatively between the two stable states. By utilizing this oscillatory switching behavior we demonstrate a unipolar deterministic SOT switching scheme by controlling the current pulse duration

Spontaneous Lead Breakage in Implanted Spinal Cord Stimulation Systems

Spinal cord stimulation (SCS) has become an established clinical option for treatment of refractory chronic pain. Current hardware and implantation techniques for SCS are already highly developed and continuously improving; however, equipment failures over the course of long-term treatment are still encountered in a relatively high proportion of the cases treated with it. Percutaneous SCS leads seem to be particularly prone to dislocation and insulation failures. We describe our experience of lead breakage in the inserted spinal cord stimulator to a complex regional pain syndrome patient who obtained satisfactory pain relief after the revision of SCS

Renal outcomes of laparoscopic versus open surgery in patients with rectal cancer: a propensity score analysis

Background A laparoscopic approach is widely used in abdominal surgery. Although several studies have compared surgical and oncological outcomes between laparoscopic surgery (LS) and open surgery (OS) in rectal cancer patients, there have been few studies on postoperative renal outcomes. Methods We conducted a retrospective cohort study involving 1,633 patients who underwent rectal cancer surgery between 2003 and 2017. Postoperative acute kidney injury (AKI) was diagnosed according to the serum creatinine criteria of the Kidney Disease: Improving Global Outcomes classification. Results Among the 1,633 patients, 1,072 (65.6%) underwent LS. After matching propensity scores, 395 patients were included in each group. The incidence of postoperative AKI in the LS group was significantly lower than in the OS group (9.9% vs. 15.9%; p = 0.01). Operation time, estimated blood loss, and incidence of transfusion in the LS group were significantly lower than those in the OS group. Cox proportional hazard models revealed that LS was associated with decreased risk of postoperative AKI (hazard ratio [HR], 0.599; 95% confidence interval [CI], 0.402–0.893; p = 0.01) and postoperative transfusion was associated with increased risk of AKI (HR, 2.495; 95% CI, 1.529–4.072; p < 0.001). In the subgroup analysis, the incidence of postoperative AKI in patients with middle or high rectal cancer who underwent LS was much lower than in those who underwent OS (HR, 0.373; 95% CI, 0.197–0.705; p = 0.002). Conclusion This study showed that LS may have a favorable effect on the development of postoperative AKI in patients with rectal cancer

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I–III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26–77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23–74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC

Longitudinal Dynamic Contrast-Enhanced MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST

Multiparametric MRI-Based Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) \u3e 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients\u27 treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p \u3c 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p \u3c 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients