Stereotactic radiosurgery (SRS) is a common treatment used in patients with brain metastases and is associated with high rates of local control, however, at the risk of radiation necrosis. It is difficult to differentiate radiation necrosis from tumor progression using conventional MRI, making it a major diagnostic dilemma for practitioners. This prospective study investigated whether chemical exchange saturation transfer (CEST) was able to differentiate these two conditions. Sixteen patients with brain metastases who had been previously treated with SRS were included. Average time between SRS and evaluation was 12.6 months. Lesion type was determined by pathology in 9 patients and the other 7 were clinically followed. CEST imaging was performed on a 3T Philips scanner and the following CEST metrics were measured: amide proton transfer (APT), magnetization transfer (MT), magnetization transfer ratio (MTR), and area under the curve for CEST peaks corresponding to amide and nuclear Overhauser effect (NOE). Five lesions were classified as progressing tumor and 11 were classified as radiation necrosis (using histopathologic confirmation and radiographic follow-up). The best separation was obtained by NOE (NOE = 8.9 ± 0.9%, NOE = 12.6 ± 1.6%, < 0.0001) and Amide (Amide = 8.2 ± 1.0%, Amide = 12.0 ± 1.9%, < 0.0001). MT (MT = 4.7 ± 1.0%, MT = 6.7 ± 1.7%, = 0.009) and NOE (NOE = 4.3 ± 2.0% Hz, NOE = 7.2 ± 1.9% Hz, = 0.019) provided statistically significant separation but with higher values. CEST was capable of differentiating radiation necrosis from tumor progression in brain metastases. Both NOE and Amide provided statistically significant separation of the two cohorts. However, APT was unable to differentiate the two groups. .

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CEST metrics, in particular, the NOE peak amplitude, can predict volume changes 1 month post-SRS. Magn Reson Med 78:1110-1120, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Brain metastases are the most common intracranial tumors and occur in 20–40% of all cancer patients. Lung cancer, breast cancer, and melanoma are the most frequent primary cancers to develop brain metastases. Treatment options include surgical resection, whole brain radiotherapy, stereotactic radiosurgery, and systemic treatment such as targeted or immune therapy. Anatomical magnetic resonance imaging (MRI) of the tumor (in particular post-Gadolinium T 1 -weighted and T 2 -weighted FLAIR) provide information about lesion morphology and structure, and are routinely used in clinical practice for both detection and treatment response evaluation for brain metastases. Advanced MRI biomarkers that characterize the cellular, biophysical, micro-structural and metabolic features of tumors have the potential to improve the management of brain metastases from early detection and diagnosis, to evaluating treatment response. Magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), quantitative magnetization transfer (qMT), diffusion-based tissue microstructure imaging, trans-membrane water exchange mapping, and magnetic susceptibility weighted imaging (SWI) are advanced MRI techniques that will be reviewed in this article as they pertain to brain metastases.

We hypothesized that sex hormones may affect motility disorders because these diseases occur more often in women than in men, and symptoms often occur or worsen after ovulation. Luteinizing hormone (LH) is predominantly secreted by the anterior pituitary midway through the menstrual cycle; it results in the development of the corpus luteum. LH levels also increase after bilateral gonadectomy. LH and human chorionic gonadotropin (hCG) bind to the same receptor, but rats lack hCG. To assess how LH and hCG influence myoelectric activity of the small intestine and to test the specificity of the LH receptor, we implanted electrodes on the jejunum of female rats. LH (0.1 or 0.5 NIH units) was administered intraperitoneally to intact and gonadectomized rats and 0.5 NIH units to rats that had been both hypophysectomized and gonadectomized; intact animals were treated with 100 units USP of hCG. Recordings were made with the rats in fasted and in fed states, and their intestinal motility was analysed. The most striking effects of LH, hypophysectomy, and hCG were the same: phase III of the migrating myoelectric complex was markedly fragmented and its duration lengthened (P < 0.0001). Gonadectomy alone and gonadectomy with hypophysectomy also increased fragmentation and phase III duration (P < 0.01 or better). LH receptors respond similarly to LH and hCG, and both hormones alter myoelectric activity of the rat small intestine in comparable ways.

Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day0), two weeks (Day14), and four weeks (Day28) into the treatment, and one month after the end of the treatment (Day70). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3–8 months following the end of chemo-radiation. The amount of magnetization transfer () was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in at Day0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day14 and Day0 were able to differentiate the two cohorts with providing the best separation (relative  = 1.34 ± 0.21, relative  = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.

In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue.

In breast elastography, breast tissue usually undergoes large compression resulting in significant geometric and structural changes. This implies that breast elastography is associated with tissue nonlinear behavior. In this study, an elastography technique is presented and an inverse problem formulation is proposed to reconstruct parameters characterizing tissue hyperelasticity. Such parameters can potentially be used for tumor classification. This technique can also have other important clinical applications such as measuring normal tissue hyperelastic parameters in vivo. Such parameters are essential in planning and conducting computer-aided interventional procedures. The proposed parameter reconstruction technique uses a constrained iterative inversion; it can be viewed as an inverse problem. To solve this problem, we used a nonlinear finite element model corresponding to its forward problem. In this research, we applied Veronda-Westmann, Yeoh and polynomial models to model tissue hyperelasticity. To validate the proposed technique, we conducted studies involving numerical and tissue-mimicking phantoms. The numerical phantom consisted of a hemisphere connected to a cylinder, while we constructed the tissue-mimicking phantom from polyvinyl alcohol with freeze-thaw cycles that exhibits nonlinear mechanical behavior. Both phantoms consisted of three types of soft tissues which mimic adipose, fibroglandular tissue and a tumor. The results of the simulations and experiments show feasibility of accurate reconstruction of tumor tissue hyperelastic parameters using the proposed method. In the numerical phantom, all hyperelastic parameters corresponding to the three models were reconstructed with less than 2% error. With the tissue-mimicking phantom, we were able to reconstruct the ratio of the hyperelastic parameters reasonably accurately. Compared to the uniaxial test results, the average error of the ratios of the parameters reconstructed for inclusion to the middle and external layers were 13% and 9.6%, respectively. Given that the parameter ratios of the abnormal tissues to the normal ones range from three times to more than ten times, this accuracy is sufficient for tumor classification.