Original Magnetic Resonance Research Articles

GSCFN: A graph self-construction and fusion network for semi-supervised brain tissue segmentation in MRI

In this paper, we propose a graph self-construction and fusion network (GSCFN) for semi-supervised brain tissue segmentation in Magnetic Resonance Imaging (MRI) by fusing multiple types of image features. Compared to the use of a single feature, various features bring complementary information and can contribute to a better graph representation with a great discriminative power increase. But to do so, two problems need to be solved. The first one consists in effectively inferring a graph from a Magnetic Resonance (MR) image so as to implicitly encode the segmentation information and the second in fully leveraging various features. To solve both problems, we propose an original brain MR image semi-supervised segmentation framework, called graph self-construction and fusion network. This one relies on two parts. In the first one, a graph self-construction network is utilized to obtain various graph representations of an MR image depending on different features. In the second, a multi-graph convolution network is proposed for the fusion of multiple graphs and features as well as for the classification of supervoxels which are treated as graph nodes. Experiments on the BrainWeb18 dataset and the Internet Brain Segmentation Repository 18 dataset validate the superiority of our scheme compared with approaches based on a single feature type, and some other state-of-the-art methods. The ablation experiment indicates that the proposed GSCFN can produce more accurate and reliable segmentation by seamlessly integrates multiple types of features.

MEDICAL IMAGES FUSION ALGORITHM BASED ON PROBABILISTIC GAMMA-NORMAL MODEL WITH STRUCTURE-TRANSFERRING PROPERTIES

Abstract. In medical image processing, image fusion is the process of combining complementary information from different (multimodality) images to obtain a fused image, which plays a vital role in further analysis and treatment planning. The main idea of this paper is to improve the image content by fusing computer tomography (CT) and magnetic resonance (MR) images. We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties. Firstly, we select the areas with the highest pixel intensity on original CT and MR images. In parallel with this, the structures of original images are distinguished using the probabilistic gamma-normal model. The weighted-fusion image can be obtained based on detected objects and structure. Finally, we smooth the weighted-fusion image using the structure-transferring filter and combine the smoothed image with the weighted-fusion image for obtaining the resulting image. The key point here is that we do not need to re-allocate the structure, which leads to the reduction of computation time. The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.

Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation

This paper presents a new fuzzy-based method for the segmentation of brain structures from noisy magnetic resonance (MR) images, in the presence of noise. Our algorithm is a new extension of the fuzzy C-means (FCM) algorithm. The proposed algorithm is developed by modifying the objective function in the FCM using double estimation by incorporating both the original and denoised images in place of using solely the denoised image. To the best of our knowledge, the proposed algorithm is the first extension of the FCM method that is capable of segmenting images (per pixel) based on both noisy and denoised image estimates. In this algorithm we: (a) introduce a novel formulation that assigns weights for each estimation using spatial image information and (b) apply a kernel distance metric for image segmentation. This formulation is highly applicable in segmenting images corrupted by high levels of noise. Experimental results on both simulated and original MR images are presented to demonstrate the robustness and effectiveness of our proposed algorithm in the presence of noise. These results are compared to the nonlocal fuzzy C-means method (LNLFCM), discrete cosine transform-LNLFCM (DCT-LNLFCM), kernel weighted fuzzy local information C-means (KWFLICM), and bias correction embedded fuzzy C-means with spatial constraint (BCEFCM-S) algorithm.

MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images.

Stroke is a serious manifestation of various cerebrovascular diseases and one of the most dangerous diseases in the world today. Volume quantification and location detection of chronic stroke lesions provide vital biomarkers for stroke rehabilitation. Recently, deep learning has seen a rapid growth, with a great potential in segmenting medical images. In this work, unlike most deep learning-based segmentation methods utilizing only magnetic resonance (MR) images as the input, we propose and validate a novel stroke lesion segmentation approach named multi-inputs UNet (MI-UNet) that incorporates brain parcellation information, including gray matter (GM), white matter (WM) and lateral ventricle (LV). The brain parcellation is obtained from 3D diffeomorphic registration and is concatenated with the original MR image to form two-channel inputs to the subsequent MI-UNet. Effectiveness of the proposed pipeline is validated using a dataset consisting of 229 T1-weighted MR images. Experiments are conducted via a five-fold cross-validation. The proposed MI-UNet performed significantly better than UNet in both 2D and 3D settings. Our best results obtained by 3D MI-UNet has superior segmentation performance, as measured by the Dice score, Hausdorff distance, average symmetric surface distance, as well as precision, over other state-of-the-art methods.

Comparison of Contrast-Enhanced Ultrasound Versus Magnetic Resonance Imaging in the Detection and Characterization of Uterine Leiomyomas.

Uterine fibroids are common findings in women with pelvic pain and abnormal uterine bleeding. The reference standard test in the pretreatment evaluation of fibroids is contrast-enhanced magnetic resonance (MR) imaging. This study compared the number, size, location, and enhancement of uterine fibroids identified by contrast-enhanced ultrasound (CEUS) and MR. The aim of this study was to demonstrate that CEUS performs similarly to MR and could be used as an alternative imaging modality. In this prospective observational study, 26 women underwent transabdominal CEUS and MR examinations. Blinded to the original clinical MR interpretations, 2 readers reviewed the MR and CEUS studies for each patient. The number, size, location, and enhancement of each fibroid per patient were reported by MR and CEUS. A Pearson correlation coefficient was calculated for the number of fibroids identified by each modality. In total, 126 fibroids were imaged: 115 (91.3%) were observed on both examinations; 9 (7.1%) were observed by MR only; and 2 (1.6%) were observed by CEUS only. A high correlation was found between the modalities for the number of fibroids identified per patient (r = 0.97; P < .001). There was also no significant difference between the modalities for each patient in the fibroid number, size, location, or enhancement. These findings suggest that transabdominal CEUS may represent an alternative to MR in pretreatment evaluation of uterine fibroids and could serve as a test of choice in patients with a contraindication to MR.

A novel privacy-supporting 2-class classification technique for brain MRI images

Developing automated Computer Aided Diagnosis (CAD) framework for assisting radiologists in a fast and effective classification of brain Magnetic Resonance (MR) images is of great importance, given plausible usage of Electronic Health Records (EHR) in healthcare system. This work aims at proposing two novel privacy supporting classifiers for automatic segregation of brain MR images. To ensure privacy, our article employs a spatial steganographic approach to hide patients sensitive health information in brain images itself. Proposed methods employ Discrete Wavelet Transform (DWT) for extracting relevant features from original and stego images. Subsequently, Symmetrical Uncertainty Ranking (SUR) and Probabilistic Principal Components Analysis (PPCA) are used to obtain a reduced feature vector for Support Vector Machine (SVM) and Filtered Classifier (FC) respectively. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate this work, the proposed schemes are experimented on both original and stego brain MR images and are compared against eight state-of-the-art classification techniques with respect to six standard parameters. The results reveal that the proposed techniques are robust and scalable with respect to the size of the datasets. Moreover, it is concluded that applying steganographic algorithm on brain MR images yield equally satisfactory classification rate.

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment.Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format.Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% (P < 0.001) and downgrading rate by 6.4% (P = 0.029).Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.

"Isolated" Suprascapular Neuropathy: Compression, Traction, or Inflammation?

Several hypotheses have been proposed for the pathophysiology of suprascapular nerve (SSN) palsy, including compression, traction, and nerve inflammation. To provide insight into the pathophysiology of isolated nontraumatic SSN palsy by performing critical reinterpretations of electrodiagnostic (EDX) studies and magnetic resonance (MR) images of patients with such diagnosis. We retrospectively reviewed all patients referred to our institution for the past 20 yr with a diagnosis of nontraumatic isolated suprascapular neuropathy who had an upper extremity EDX study and a shoulder or brachial plexus MR scan. Patient charts were reviewed to analyze their initial clinical examination, and their original EDX study and MR images were reinterpreted by an experienced neurologist and a musculoskeletal radiologist, respectively, both blinded from the authors' hypothesis and from each other's findings. Fifty-nine patients were included. Fifty of them (85%) presented with at least 1 finding that was inconsistent with an isolated SSN palsy. Forty patients (68%) had signs on physical examination beyond the SSN distribution. Thirty-one patients (53%) had abnormalities on their EDX studies not related to the SSN. Twenty-two patients (37%) had denervation atrophy in other muscles than the spinati, or neural hyperintensity in other nerves than the SSN on their MR scans, without any evidence of SSN extrinsic compression. The great majority of patients with presumed isolated SSN palsy had clinical, electrophysiological, and/or imaging evidence of a more diffuse pattern of neuromuscular involvement. These data strongly support an inflammatory pathophysiology in many cases of "isolated" SSN palsy.

Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut.

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in medical images is an import task for clinical applications, including radiotherapy. However, NPC features large variations in lesion size and shape, as well as inhomogeneous intensities within the tumor and similar intensity to that of nearby tissues, making its segmentation a challenging task. The present study proposes a novel automated NPC segmentation method in magnetic resonance (MR) images by combining a deep convolutional neural network (CNN) model and a 3-dimensional (3D) graph cut-based method in a two-stage manner. First, a multi-view deep CNN-based segmentation method is performed. A voxel-wise initial segmentation is generated by integrating the inferential classification information of three trained single-view CNNs. Instead of directly using the CNN classification results to achieve a final segmentation, the proposed method uses a 3D graph cut-based method to refine the initial segmentation. Specifically, the probability response map obtained using the multi-view CNN method is utilized to calculate the region cost, which represents the likelihood of a voxel being assigned to the tumor or non-tumor. Structure information in 3D from the original MR images is used to calculate the boundary cost, which measures the difference between the two voxels in the 3D neighborhood. The proposed method was evaluated on T1-weighted images from 30 NPC patients using the leave-one-out method. The experimental results demonstrated that the proposed method is effective and accurate for NPC segmentation.

Image Registration for Microwave Tomography of the Breast Using Priors From Nonsimultaneous Previous Magnetic Resonance Images.

Microwave imaging is a low-cost imaging method that has shown promise for breast imaging and, in particular, neoadjuvant chemotherapy monitoring. The early studies of microwave imaging in the therapy monitoring setting are encouraging. For the neoadjuvant therapy application, it would be desirable to achieve the most accurate possible characterization of the tissue properties. One method to achieve increased resolution and specificity in microwave imaging reconstruction is the use of a soft prior regularization. The objective of this study is to develop a method to use magnetic resonance (MR) images, taken in a different imaging configuration, as this soft prior. To enable the use of the MR images as a soft prior, it is necessary to register the MR images to the microwave imaging space. Registration fiducials were placed around the breast that are visible in both the MRI and with an optical scanner integrated into the microwave system. Utilizing these common registration locations, numerical algorithms have been developed to warp the original breast MR images into a geometry closely resembling that in which the breast is pendant in the microwave system.

A Novel Public MR Image Dataset of Multiple Sclerosis Patients With Lesion Segmentations Based on Multi-rater Consensus

Quantified volume and count of white-matter lesions based on magnetic resonance (MR) images are important biomarkers in several neurodegenerative diseases. For a routine extraction of these biomarkers an accurate and reliable automated lesion segmentation is required. To objectively and reliably determine a standard automated method, however, creation of standard validation datasets is of extremely high importance. Ideally, these datasets should be publicly available in conjunction with standardized evaluation methodology to enable objective validation of novel and existing methods. For validation purposes, we present a novel MR dataset of 30 multiple sclerosis patients and a novel protocol for creating reference white-matter lesion segmentations based on multi-rater consensus. On these datasets three expert raters individually segmented white-matter lesions, using in-house developed semi-automated lesion contouring tools. Later, the raters revised the segmentations in several joint sessions to reach a consensus on segmentation of lesions. To evaluate the variability, and as quality assurance, the protocol was executed twice on the same MR images, with a six months break. The obtained intra-consensus variability was substantially lower compared to the intra- and inter-rater variabilities, showing improved reliability of lesion segmentation by the proposed protocol. Hence, the obtained reference segmentations may represent a more precise target to evaluate, compare against and also train, the automatic segmentations. To encourage further use and research we will publicly disseminate on our website http://lit.fe.uni-lj.si/tools the tools used to create lesion segmentations, the original and preprocessed MR image datasets and the consensus lesion segmentations.

Brain MR image segmentation using NAMS in pseudo-color

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm

Over the last few decades, segmentation applied to numerous applications using medical images have rapidly been increased, especially for the big data of magnetic resonance (MR) images. Brain tumor segmentation on MR images is a challenging task in clinical analysis for surgical and treatment planning. Numerous brain tumor segmentation algorithms have been proposed. However, they have still faced the problems of over and under segmentation according to characteristics of ambiguous tumor boundaries. Improving segmentation method is still a challenging research. This paper presents a framework of two paradigms to improve the brain tumor segmentation; image transformation and segmentation algorithm. To cope with ambiguous tumor boundaries, the proposed novel gray-level co-occurrence matrix based cellular automata (GLCM-CA) is presented. GLCM-CA aims to transform an original MR image to the target featured image. It enhances features of the tumor similar to the background areas prior to segmentation. For segmentation, the efficient Tumor-Cut algorithm is improved. Tumor-Cut is an efficient algorithm in tumor segmentation, but faces the problem of robustness in seed growing leading to under segmentation. To cope with this problem, the novel patch weighted distance is proposed in the proposed Improved Tumor-Cut (ITC). ITC significantly enhances the robustness of seed growing. For performance evaluation, BraTS2013 benchmark dataset is empirically experimented throughout in comparison with the state-of-the-art methods using dice quantitative evaluation metrics. Experiments are carried out on 55 real MR images consisting of training and testing datasets. In this regard, the proposed method based on GLCM-CA feature space and ITC provides the outstanding result superior to the state-of-the-art compared methods.

Compressively sampled magnetic resonance image reconstruction using separable surrogate functional method

ABSTRACTAccording to the theory of compressed sensing, magnetic resonance (MR) images can be well reconstructed from randomly sub‐Nyquist sampling of the k‐space data using a nonlinear recovery technique if certain conditions are satisfied. The sparse coefficients of the partial Fourier data can be estimated by minimizing the reconstruction cost function. Separable surrogate functional (SSF) method is one of the effective numerical techniques for minimizing mixed convex optimization problems. This article presents a novel recovery technique for compressively sampled MR images that uses SSF method subject to the data consistency constraints. The experimental results show that the proposed recovery algorithm outperforms the projection onto convex sets and low resolution‐based reconstruction techniques in terms of artifact power, improved signal‐to‐noise ratio, and correlation for the same number of iterations. The results are validated using the phantom and original human head MR images taken from the magnetic resonance imaging scanner at St. Mary's Hospital, London. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part A 43A: 157–165, 2015.

Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data

The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of SUV(max): -3.0% ± 3.9% for MR; -6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Our MR-based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images.

Tumor detection by using Zernike moments on segmented magnetic resonance brain images

In this study, a novel method is proposed for the detection of tumor in magnetic resonance (MR) brain images. The performance of the novel method is investigated on one phantom and 20 original MR brain images with tumor and 50 normal (healthy) MR brain images. Before the segmentation process, 2D continuous wavelet transform (CWT) is applied to reveal the characteristics of tissues in MR head images. Then, each MR image is segmented into seven classes (six head tissues and the background) by using the incremental supervised neural network (ISNN) and the wavelet-bands. After the segmentation process, the head is extracted from the background by simply discarding the background pixels. Symmetry axis of the head in the MR image is determined by using moment properties. Asymmetry is analyzed by using the Zernike moments of each of six tissues segmented in the head: two vectors are individually formed for the left and right hand sides of the symmetry axis on the sagittal plane by using the Zernike moments of the segmented tissues in the head. Presence of asymmetry and the tumors are inquired by considering the distance between these two vectors. The performance of the proposed method is further investigated by moving the location of the tumor and by modifying its size in the phantom image. It is observed that tumor detection is successfully realized for the tumorous 20 MR brain images.

Band Expansion-Based Over-Complete Independent Component Analysis for Multispectral Processing of Magnetic Resonance Images

Independent component analysis (ICA) has found great promise in magnetic resonance (MR) image analysis. Unfortunately, two key issues have been overlooked and not investigated. One is the lack of MR images to be used to unmix signal sources of interest. Another is the use of random initial projection vectors by ICA, which causes inconsistent results. In order to address the first issue, this paper introduces a band-expansion process (BEP) to generate an additional new set of images from the original MR images via nonlinear functions. These newly generated images are then combined with the original MR images to provide sufficient MR images for ICA analysis. In order to resolve the second issue, a prioritized ICA (PICA) is designed to rank the ICA-generated independent components (ICs) so that MR brain tissue substances can be unmixed and separated by different ICs in a prioritized order. Finally, BEP and PICA are combined to further develop a new ICA-based approach, referred to as PICA-BEP to perform MR image analysis.

Classification of neurovascular compression in typical hemifacial spasm: three-dimensional visualization of the facial and the vestibulocochlear nerves

In this paper, the authors introduce a method of noninvasive anatomical analysis of the facial nerve-vestibulocochlear nerve complex and the depiction of the variable vascular relationships by using 3D volume visualization. With this technique, a detailed spatial representation of the facial and vestibulocochlear nerves was obtained. Patients with hemifacial spasm (HFS) resulting from neurovascular compression (NVC) were examined. A total of 25 patients (13 males and 12 females) with HFS underwent 3D visualization using magnetic resonance (MR) imaging with 3D constructive interference in a steady state (CISS). Each data set was segmented and visualized with respect to the individual neurovascular relationships by direct volume rendering. Segmentation and visualization of the facial and vestibulocochlear nerves were performed with reference to their root exit zone (REZ), as well as proximal and distal segments including corresponding blood vessels. The 3D visualizations were interactively compared with the intraoperative situation during microvascular decompression (MVD) to verify the results with the observed microneurosurgical anatomy. Of the 25 patients, 20 underwent MVD (80%). Microvascular details were recorded on the affected and unaffected sides. On the affected sides, the anterior inferior cerebellar artery (AICA) was the most common causative vessel. The posterior inferior cerebellar artery, vertebral artery, internal auditory artery, and veins at the REZ of the facial nerve (the seventh cranial nerve) were also found to cause vascular contacts to the REZ of the facial nerve. In addition to this, the authors identified three distinct types of NVC within the REZ of the facial nerve at the affected sides. The authors analyzed the varying courses of the vessels on the unaffected sides. There were no bilateral clinical symptoms of HFS and no bilateral vascular compression of the REZ of the facial nerve. The authors discovered that the AICA is the most common vessel that interferes with the proximal and distal portions of the facial nerve without any contact between vessels and the REZ of the facial nerve on the unaffected sides. Three-dimensional visualization by direct volume rendering of 3D CISS MR imaging data offers the opportunity of noninvasive exploration and anatomical categorization of the facial nerve-vestibulocochlear nerve complex. Furthermore, it proves to be advantageous in establishing the diagnosis and guiding neurosurgical procedures by representing original MR imaging patient data in a 3D fashion. This modality provides an excellent overview of the entire neurovascular relationship of the cerebellopontine angle in each case.

Cystic Changes at Supraspinatus and Infraspinatus Tendon Insertion Sites: Association with Age and Rotator Cuff Disorders in 238 Patients

To retrospectively determine the frequency of posterior and anterior cystic abnormalities at rotator cuff insertion site on the greater tuberosity and to determine their relationship to patient age and rotator cuff disorders. Institutional review board approval was given; informed consent was waived. The study was HIPAA compliant. In 238 patients with rotator cuff diagnoses at surgery, preoperative magnetic resonance (MR) imaging studies were reviewed to localize osseous cystic changes as anterior (supraspinatus insertion site) or posterior (infraspinatus insertion site) on the greater tuberosity. If rotator cuff tear was present, tendon retraction and location of partial tear (articular or bursal surface) were recorded. Two radiologists reached conclusions by consensus. Locations of cysts were correlated to surgical cuff diagnoses: no tear, tendinopathy, partial-thickness tear, and complete tear. Prospective interpretations from original MR reports were compared with surgical results. Statistical analyses included one-way analysis of variance, chi(2), Fisher exact, and Student t tests, as well as logistic regression and receiver operating characteristic curve comparison. There were 238 consecutive patients (150 men, 88 women; mean age, 43 years). Cysts were located at or near footprint of cuff tendon and demonstrated fluid or soft-tissue signal intensities. Posterior cysts occurred in 56.7% of shoulders and showed no statistical correlation to age or cuff diagnosis. Anterior cysts occurred in 22.7% of shoulders and were strongly associated with cuff disorders (P<.001). Controlling for cuff disorders, there was no relationship between anterior cysts and age (P>.50). Anterior cysts were more common in partial-thickness articular (48%) than in bursal (13%) tears (P<.001). Posterior cysts were more common than anterior cysts and showed nearly random distribution among patients, regardless of age and cuff diagnosis. Anterior cysts were closely associated with cuff disorders.

Clinical impact of magnetic resonance imaging on Gamma Knife surgery for brain metastases

Stereotactic radiosurgery is beneficial for patients with a limited number of small brain metastases. Increased numbers of brain metastases, not infrequently at unreachable locations, are identified using thin-section magnetic resonance (MR) imaging on the day of Gamma Knife surgery (GKS). To improve patient selection and design better treatment strategies, a retrospective study was conducted to determine factors that may contribute to detecting additional brain metastases on the day of GKS. A total of 100 patients with brain metastases who underwent GKS between October 2003 and May 2006 at the University of California Davis Medical Center were included in the present study. Patients were categorized by age, sex, Karnofsky Performance Scale score, status of systemic disease, histological characteristics of the primary tumor, and whether they received previous whole-brain radiotherapy (WBRT). The number of lesions identified by diagnostic MR imaging at referral, by thin-section double-contrast MR imaging on the day of GKS, and the actual lesions treated by GKS were recorded. The diagnostic MR images were categorized in terms of section thickness and time interval before GKS. The characteristics of this patient population match well with the general GKS practice. Fifty-six had been treated with WBRT. On average, patients presented with 2.2 +/- 1.7 lesions, a number based on their original diagnostic MR imaging, had 3.6 +/- 3.4 lesions identified on the thin-section treatment MR imaging (p < 0.05), and underwent treatment of 3.1 +/- 2.6 lesions on the day of GKS. Significantly, treatment was compromised in 21 patients, in whom not all additional lesions could be treated with the initial headframe placement. Analysis shows that a significantly greater number of lesions were detected using thin-section MR imaging on the day of GKS in patients who had undergone thick-section diagnostic MR imaging, did not receive WBRT, and had progressive systemic disease. To optimize treatment planning and minimize additional treatment, the number of metastases needs to be determined accurately before frame placement, ideally by performing thin-section MR imaging, as used on the day of GKS.