Feature-based,automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.     

Quantification of volume of tissue damage in stroke using T2- weighed MRI images

We have developed a quantitative technique for scoring of the severity of ischemic damage of the brain using quantitative data of the T2-weighted MRI images of brain in stroke. The principle of the method is the assumption that T2 signal increases proportionally to the severity of ischemic damage of cerebral tissue up to the level equal to intraventricular liquor signal in the case of postinfarction cystic degeneration. Depicting the mean T2-signal from the intraventricular liquor region as Iliq, the signal from ischemic brain area as Iinsult, and from the intact brain as Inorm, obviously, the volume quota of damaged tissue in the total volume of the stroke region is represented by the ratio (Iinsult - Inorm)/(Iliq - Inorm). The total volume of damaged tissue (VDT, cub.cm) in the stroke region is then the following sum taken over all slices i, where the stroke damage can be: VDT = sigma i d.Si.[(Iinsult - Inorm)/(Iliq - Inorm)]i, where d is the slice thickness, Si--area of the ischemic region in the slice i. The quota of damaged tissue in the physical volume of the stroke region is henceforth the following ratio: Q = [sigma i d.Si.[(Iinsult - Inorm)/(Iliq - Inorm)]i]/[sigma i d.Si]. The technique was applied in retrospective analysis of routine MRI studies in 15 patients referred because of acute ischemic stroke. The studies were performed using low-field MRI tomograph Magnetom-Open (Siemens Medical) with field strength 0.22 T. In patients studied during the first day after occurrence of ischemic insult with the minimal degree of acute neurologic deficit, who later have demonstrated clinically full recovery, the VDT was below 20 cm3, and Q was below 10%. In cases with VDT > 25 cm3 and Q > 20% the full regress was not observed in any patient. Henceforth, the quantification of cerebral damage in stroke using quantitative indices based on measurement of T2-parameters over ischemic and intact zones of the brain are of independent prognostic clinical value and improve clinical usefulness of the MRI in ischemic brain stroke.     

Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network

Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm.     

Comparison of Multiparametric MRI Scoring Systems and the Impact on Cancer Detection in Patients Undergoing MR US Fusion Guided Prostate Biopsies

Introduction

Multiple scoring systems have been proposed for prostate MRI reporting. We sought to review the clinical impact of the new Prostate Imaging Reporting and Data System v2 (PI-RADS) and compare those results to our proposed Simplified Qualitative System (SQS) score with respect to detection of prostate cancers and clinically significant prostate cancers.

Methods

All patients who underwent multiparametric prostate MRI (mpMRI) had their images interpreted using PI-RADS v1 and SQS score. PI-RADS v2 was calculated from prospectively collected data points. Patients with positive mpMRIs were then referred by their urologists for enrollment in an IRB-approved prospective phase III trial of mpMRI-Ultrasound (MR/TRUS) fusion biopsy of suspicious lesions. Standard 12-core biopsy was performed at the same setting. Clinical data were collected prospectively.

Results

1060 patients were imaged using mpMRI at our institution during the study period. 341 participants were then referred to the trial. 312 participants underwent MR/TRUS fusion biopsy of 452 lesions and were included in the analysis. 202 participants had biopsy-proven cancer (64.7%) and 206 (45.6%) lesions were positive for cancer. Distribution of cancer detected at each score produced a Gaussian distribution for SQS while PI-RADS demonstrates a negatively skewed curve with 82.1% of cases being scored as a 4 or 5. Patient-level data demonstrated AUC of 0.702 (95% CI 0.65 to 0.73) for PI-RADS and 0.762 (95% CI 0.72 to 0.81) for SQS (p< 0.0001) with respect to the detection of prostate cancer. The analysis for clinically significant prostate cancer at a per lesion level resulted in an AUC of 0.725 (95% CI 0.69 to 0.76) and 0.829 (95% CI 0.79 to 0.87) for the PI-RADS and SQS score, respectively (p< 0.0001).

Conclusions

mpMRI is a useful tool in the workup of patients at risk for prostate cancer, and serves as a platform to guide further evaluation with MR/TRUS fusion biopsy. SQS score provided a more normal distribution of scores and yielded a higher AUC than PI-RADS v2. However until our findings are validated, we recommend reporting of detailed sequence-specific findings. This will allow for prospectively collected data to be utilized in determining the impact of ongoing changes to these scoring systems as our understanding of mpMRI interpretation evolves.     

利用T4噬菌体展示猪瘟病毒E2抗原

利用重组PCR技术将猪瘟病毒 (CSFV )E2蛋白主要抗原编码区基因 (mE2 )与T4噬菌体SOC基因融合 ,构建了大小为 643bp的SOC/mE2融合基因 ,再将其插入携带T4溶菌酶基因 (e)和(denV)基因的T4重组载体 (PRH) ,构建了重组载体pRsmE2。通过重组载体与缺失突变型T4发生同源重组 ,可将SOC/mE2融合基因整合入T4的基因组中 ,并成功地将大小约 2 1 5aaSOC/mE2融合蛋白展示于T4噬菌体衣壳表面。经Westernblot、胶体金免疫电镜等免疫学检测证实 ,展示于T4表面的mE2融合蛋白具有CSFV免疫学活性。     

T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

BackgroundThe objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images.Materials and methodsA total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256.ResultsThe relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size.ConclusionsInput resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations.     

Detection of Intratumoral Susceptibility Signals Using T2*-Weighted Gradient Echo MRI in Patients with Clear Cell Renal Cell Carcinoma

Objective

To retrospectively evaluate whether T2*-weighted imaging can be used to grade clear cell renal cell carcinomas (ccRCC) based on intratumoral susceptibility signals (ISSs).

Materials and Methods

MR imaging from 37 patients with pathologically-proven ccRCCs was evaluated. ISSs on T2*WI were classified as linear or conglomerated linear structures (type I) and dot-like or patchy foci (type II). Two radiologists assessed the likelihood of the presence of ISS, dominant structure of ISS and ratio of ISS area to tumor area. Results were analyzed by nonparametric Mann-Whitney test.

Results

ISSs were seen in all patients except for four patients with low-grade ccRCCs and two patients with high-grade ccRCCs. There was no significant difference of the likelihood of the presence of ISS between low- and high-grade ccRCCs. More type I ISSs and less type II ISSs were predictive of low-grade tumors, whereas more conspicuity type II ISSs correlated with higher occurrence of high-grade tumors (P<0.05). The ratio of ISS area to tumor area was also significantly higher for the high-grade group (1.27±0.79) than that for the low-grade group (0.81±0.40) (P<0.05).

Conclusion

ISSs on T2*-weighted gradient-echo MR images can help grade ccRCCs before operations.     

Detection of the T cell activation state using nonlinear optical microscopy

The ability to monitor the activation state of T‐cells during immunotherapy is of great importance. Although specific activation markers do exist, their abundance and complicated regulation cannot definitely define the activation state of the cells. Previous studies have shown that Third Harmonic Generation (THG) imaging could distinguish between activated versus resting microglia and healthy versus cancerous cells, mainly based on their lipid‐body profiles. In the present study, mitogen or antigen‐stimulated T‐cells were subjected to THG imaging microscopy. Qualitative and quantitative analysis showed statistically significant increase of THG mean area and intensity in activated versus resting T‐cells. The connection of THG imaging to chemical information was achieved using Raman spectroscopy, which showed significant differences between the activation processes and controls, correlating of THG signal area with cholesterol and lipid compounds, but not with triglycerides. The obtained results suggested a potential employment of nonlinear microscopy in evaluating of T‐cell activation, which is expected to be largely appreciated in the clinical practice.      

Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation

Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.     

Assessing the accuracy and precision of musculoskeletal motion tracking using cine-PC MRI on a 3.0T platform

The rising cost of musculoskeletal pathology, disease, and injury creates a pressing need for accurate and reliable methods to quantify 3D musculoskeletal motion, fostering a renewed interest in this area over the past few years. To date, cine-phase contrast (PC) MRI remains the only technique capable of non-invasively tracking in vivo 3D musculoskeletal motion during volitional activity, but current scan times are long on the 1.5T MR platform (～2.5 min or 75 movement cycles). With the clinical availability of higher field strength magnets (3.0T) that have increased signal-to-noise ratios, it is likely that scan times can be reduced while improving accuracy. Therefore, the purpose of this study is to validate cine-PC MRI on a 3.0T platform, in terms of accuracy, precision, and subject-repeatability, and to determine if scan time could be minimized. On the 3.0T platform it is possible to limit scan time to 2 min, with sub-millimeter accuracy (<0.33 mm/0.97°), excellent technique precision (<0.18°), and strong subject-repeatability (<0.73 mm/1.10°). This represents reduction in imaging time by 25% (42 s), a 50% improvement in accuracy, and a 72% improvement in technique precision over the original 1.5T platform. Scan time can be reduced to 1 min (30 movement cycles), but the improvements in accuracy are not as large.     

Feasibility of using ultra-high field (7 T) MRI for clinical surgical targeting

The advantages of ultra-high magnetic field (7 Tesla) MRI for basic science research and neuroscience applications have proven invaluable. Structural and functional MR images of the human brain acquired at 7 T exhibit rich information content with potential utility for clinical applications. However, (1) substantial increases in susceptibility artifacts, and (2) geometrical distortions at 7 T would be detrimental for stereotactic surgeries such as deep brain stimulation (DBS), which typically use 1.5 T images for surgical planning. Here, we explore whether these issues can be addressed, making feasible the use of 7 T MRI to guide surgical planning. Twelve patients with Parkinson's disease, candidates for DBS, were scanned on a standard clinical 1.5 T MRI and a 7 T MRI scanner. Qualitative and quantitative assessments of global and regional distortion were evaluated based on anatomical landmarks and transformation matrix values. Our analyses show that distances between identical landmarks on 1.5 T vs. 7 T, in the mid-brain region, were less than one voxel, indicating a successful co-registration between the 1.5 T and 7 T images under these specific imaging parameter sets. On regional analysis, the central part of the brain showed minimal distortion, while inferior and frontal areas exhibited larger distortion due to proximity to air-filled cavities. We conclude that 7 T MR images of the central brain regions have comparable distortions to that observed on a 1.5 T MRI, and that clinical applications targeting structures such as the STN, are feasible with information-rich 7 T imaging.     

Repeat Prostate Biopsy Strategies after Initial Negative Biopsy: Meta-Regression Comparing Cancer Detection of Transperineal,Transrectal Saturation and MRI Guided Biopsy

Introduction

There is no consensus on how to investigate men with negative transrectal ultrasound guided prostate biopsy (TRUS-B) but ongoing suspicion of cancer. Three strategies used are transperineal (TP-B), transrectal saturation (TS-B) and MRI-guided biopsy (MRI-B). We compared cancer yields of these strategies.

Methods

Papers were identified by search of Pubmed, Embase and Ovid Medline. Included studies investigated biopsy diagnostic yield in men with at least one negative TRUS-B and ongoing suspicion of prostate cancer. Data including age, PSA, number of previous biopsy episodes, number of cores at re-biopsy, cancer yield, and Gleason score of detected cancers were extracted. Meta-regression analyses were used to analyse the data.

Results

Forty-six studies were included; 12 of TS-B, 14 of TP-B, and 20 of MRI-B, representing 4,657 patients. Mean patient age, PSA and number of previous biopsy episodes were similar between the strategies. The mean number of biopsy cores obtained by TP-B and TS-B were greater than MRI-B. Cancer detection rates were 30·0%, 36·8%, and 37·6% for TS-B, TP-B, and MRI-B respectively. Meta-regression analysis showed that MRI-B had significantly higher cancer detection than TS-B. There were no significant differences however between MRI-B and TP-B, or TP-B and TS-B. In a sensitivity analysis incorporating number of previous biopsy episodes (36 studies) the difference between MRI-B and TP-B was not maintained resulting in no significant difference in cancer detection between the groups. There were no significant differences in median Gleason scores detected comparing the three strategies.

Conclusions

In the re-biopsy setting, it is unclear which strategy offers the highest cancer detection rate. MRI-B may potentially detect more prostate cancers than other modalities and can achieve this with fewer biopsy cores. However, well–designed prospective studies with standardised outcome measures are needed to accurately compare modalities and define an optimum re-biopsy approach.     

前列腺癌MRI灌注指标与ADC值的相关性研究

目的:探讨前列腺癌磁共振灌注加权成像和弥散加权成像参数的相关性,从影像学角度上间接反映前列腺癌微循环灌注水平与癌组织增殖的内在关系.方法:对前列腺癌病例49例,均使用GE Echo-speed1.5T超导成像仪行PWI和DWI.在工作站应用functool软件.获得PWI信号-时间曲线(signal intensity-time curve,SI-TC)图和各灌注参数相对值,并计算ADC值.结果:前列腺癌、良性前列腺增生、正常前列腺外周组织的相对负增强积分(rNEI)依次降低且具有统计学差异(P<0.05)前列腺癌的rNEI与Gleason分级、TNM、PSA分期均呈正相关关系(P<0.05)前列腺癌、良性前列腺增生和正常前列腺组织的ADC值依次升高(P<0.05).前列腺癌的rNEI与ADC呈负相关关系(P<0.05).结论:联合应用PWI和DWI可以从影像学的角度上间接提示血流供应与前列腺癌组织增殖的关系.     

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.     

An Ribonuclease T2 Family Protein Modulates Acinetobacter baumannii Abiotic Surface Colonization

Acinetobacter baumannii is an emerging bacterial pathogen of considerable medical concern. The organism''s transmission and ability to cause disease has been associated with its propensity to colonize and form biofilms on abiotic surfaces in health care settings. To better understand the genetic determinants that affect biomaterial attachment, we performed a transposon mutagenesis analysis of abiotic surface-colonization using A. baumannii strain 98-37-09. Disruption of an RNase T2 family gene was found to limit the organism''s ability to colonize polystyrene, polypropylene, glass, and stainless steel surfaces. DNA microarray analyses revealed that in comparison to wild type and complemented cells, the RNase T2 family mutant exhibited reduced expression of 29 genes, 15 of which are predicted to be associated with bacterial attachment and surface-associated motility. Motility assays confirmed that RNase T2 mutant displays a severe motility defect. Taken together, our results indicate that the RNase T2 family protein identified in this study is a positive regulator of A. baumannii''s ability to colonize inanimate surfaces and motility. Moreover, the enzyme may be an effective target for the intervention of biomaterial colonization, and consequently limit the organism''s transmission within the hospital setting.     

A prospective comparative dosimetric study between diffusion weighted MRI (DWI) & T2-weighted MRI (T2W) for target delineation and planning in cervical cancer brachytherapy

AimTo evaluate the difference between GTVBT (Gross Tumor Volume at Brachytherapy) and HR CTV (High Risk Clinical Tumor Volume) delineated with DWI and T2W MRI. To evaluate doses to organs at risk and targets from plans generated using T2W and DWI.BackgroundFunctional imaging with DWI can improve cervical tumor distinction as it is more sensitive than T2W MRI even in detecting parametrial invasion. This study does a dosimetric comparison between a T2W and DWI based plan.MethodsFifty carcinoma cervix patients were subjected to MRI based brachytherapy. T2W and a diffusion weighted sequence were acquired. Target delineation and brachytherapy planning was done on both T2W and DWI. Standard DVH parameters were recorded and the treatment was given using the plan generated from T2W images.ResultsGTVBT and HRCTV contours on DWI were different when compared with T2W. Mean GTVBT volume on T2W and DWI was 5.25 and 5.23, respectively (p value 0.8). Mean HRCTV on T2W and DWI was 28.3 and 27 cc, respectively (p value 0.003). Planning on the above volumes resulted in a superior coverage in terms of HRCTV D90 and D100 for DWI based plan, HRCTV D90 — 735.1 and 741 cGy for T2W and DWI, respectively (p value 0.006), HRCTV D100 — 441.05 and 444.5 for T2W and DWI plans, respectively (p value = 0.006). Doses to the OAR were not significantly increased.ConclusionGEC ESTRO based contouring guidelines cover all the functionally abnormal areas on DWI. DWI should only be used as a supplement to T2W for contouring target volumes.