超声造影在结节性甲状腺肿背景下良恶性结节鉴别诊断中的应用价值

目的探讨超声造影在结节性甲状腺肿背景下良、恶性结节鉴别诊断中的应用价值。方法对结节性甲状腺肿疾病中的105个可疑结节均进行超声造影检查,分析其增强特点,所有病灶均经手术病理结果证实。结果恶性结节主要呈低增强（44.8%,30/67）,良性结节主要呈等增强（39.5%,15/38）,差异有统计学意义（P＜0.05）;良、恶性结节均以不均匀增强表现为主（60.5%、68.7%）,差异无统计学意义（P＞0.05）。恶性结节主要呈慢进同出（62.7%,42/67）,良性结节主要呈同进同出（50%,19/38）,差异具有统计学意义（P＜0.01）。结论超声造影对鉴别结节性甲状腺肿背景下良、恶性结节有一定的诊断价值。     

实时三维超声引导 VACORA真空微创穿刺活检系统在乳腺良性疾病微创诊疗中的应用

目的探讨三维超声引导下VACORA真空微创穿刺活检系统在乳腺良性疾病诊疗中的应用价值。方法选取术前诊断为乳腺良性肿块患者103例（223个肿块）,经二维超声引导治疗54例（二维组）,经实时三维超声引导治疗49例（三维组）,记录两组患者肿块容积、手术切除条数、手术耗时、术后血肿发生情况及血肿吸收情况。结果全部肿块均成功引导切除,两组患者手术耗时无明显差异（P＞0.05）,三维组肿块切除条数及术后血肿发生率均明显少于二维组（均P＜0.05）,而且血肿0.16～3.80ml者平均吸收时间均显著明显短于血肿4.7～19.5及25.2～32.0ml者（均P＜0.01）。两组患者术后随访1～12个月均无复发。结论超声引导VACORA真空微创穿刺活检系统适用于体积＜2.4ml乳腺良性肿块的微创切除,实时三维超声引导定位准确、安全、损伤小,是乳腺肿块微创切除的一种有效方法。     

超声引导下Mammotome乳腺微创旋切系统活检术在乳腺恶性肿瘤早期诊断中的价值

乳腺癌是女性发病率较高的恶性肿瘤之一,微钙化对乳腺癌尤其是早期乳腺癌的诊断意义重大。乳腺钼靶及高频探头超声检查为触诊阴性的乳腺病变的检出提供了重要的影像学依据,而确诊尚需要病理学的支持。微钙化是乳腺病变中常见的影像学表现之一,有报道指出50%触诊阴性的乳腺病变行钼靶或超声检查均可探及微钙化[1]。超声引导下Mammotome乳腺微创旋切系统活检术（简称Mammotome活检术）作为一种新技术被广泛应用于触诊阴性或有微钙化乳腺病灶的病理活检,由于其可在同一病灶处反复多次取材,故可以提供足够的组织样本。有研究指出,相对于传统的针吸细胞学穿刺检查,Mammotome活检术在确定微钙化病灶性质方面优势明显[2]。我院已开展此项手术并用于触诊阴性或微钙化乳腺病灶的诊治,现将结果报道如下,并对其在乳腺恶性肿瘤早期诊断中的应用价值作一探讨。     

自动乳腺全容积成像冠状面图像在乳腺肿瘤鉴别诊断中的应用价值

目的探讨自动乳．腺全容积成像技术（ABVS）冠状面图像在乳腺肿瘤鉴别诊断中的应用价值。方法回顾性分析90例患者（共92个乳腺肿块）,其ABVS冠状面表现为边缘不光整,根据冠状面图像的特征分为边缘模糊不清、毛刺、成角、分叶4种,与手术病理检查结果对比分析。结果边缘毛刺征82个,其中74个呈“火山口”征,“火山口”征对浸润性导管癌的诊断符合率达100％。其余类型乳腺恶性肿瘤ABVS冠状面表现呈多样性,有成角、分叶、模糊不清。其中11个肿块经病理检查证实为乳腺良性肿瘤。结论ABVS的冠状面“火山口”征对于乳腺恶性肿瘤有较高的敏感性,但也有部分良性肿块冠状面呈不光整边缘,需结合其他检测手段客观进行鉴别诊断。     

超声内镜对良恶性胃溃疡的鉴别诊断及术前分期中的应用价值

胃癌是消化道最常见的恶性肿瘤之一，病死率高，选择合理的治疗方案或手术方式非常关键，因此术前准确分期显得尤其重要。良、恶性胃溃疡的鉴别一直受到临床医师的高度重视，但有时内镜检查及活检很难及时给以准确判断。近年来超声内镜（endoscopic ultra原sonography，EUS）检查在消化道疾病的诊治中应用越来越广泛[1-2]。EUS检查能清晰显示消化道管壁5层结构及其周围情况，判断胃癌浸润深度及有无局部邻近脏器浸润和淋巴结转移。本研究通过对65例常规内镜检查怀疑为恶性胃溃疡的患者进行EUS检查，分析其在良、恶性胃溃疡鉴别诊断中的意义，同时对恶性胃溃疡进行TN分期，评估其在手术前的分期价值。     

胸腔镜手术在孤立性肺结节诊治中的应用

孤立性肺结节（SPN）系指肺实质内直径臆3cm的圆形或类圆形病灶，不伴有肺不张、肺门增大或胸腔积液[1]。随着胸部CT检查的广泛应用，临床上的SPN患者检出率增高，大多数术前难以明确结节性质。对于直径＜0.8cm的SPN患者可以门诊随访，定期复查胸部CT来动态观察结节大小变化[2]。恶性病例存在着肿瘤扩散、疾病进展的风险。如何准确判断肺部结节的良、恶性，尚无特定方法。CT引导下肺穿刺或者纤维支气管镜检查对诊断帮助也有限，尤其对一些肿块较小、位置较深的患者，采用手术切除进行活检往往是唯一的诊断和治疗方法。传统的开胸手术创伤大、疼痛明显、恢复慢，而现代胸腔镜肺手术技术不断成熟，其微创的优点成为临床更好的选择。近年来，我院对87例SPN患者进行胸腔镜手术，效果满意，现报道如下。     

基于动脉自旋标记技术的正常大脑后循环血流分析

目的 利用动脉自旋标记技术对正常大脑后循环进行MRI 灌注成像,测量正常大脑后循环血流量,提供正常大脑后循环供血区血流量参考值。方法 对60 例志愿者进行MRI 常规扫描和3D-ASL 灌注扫描,获得血流量图,分别测得双侧枕叶、颞叶后部、小脑半球及脑干血流量（cerebral blood flow,CBF）,结果进行统计分析。结果 测得血流量,双侧枕叶为（64.45±12.72）ml/（min·100g）,颞叶后部为（63.28±14.97）ml/（min·100g）,小脑半球为（61.75±13.54）ml/（min·100g）,脑干为（53.64±12.71）ml/（min·100g）,男性与女性的血流量未见差异;不同年龄段的血流量存在差异。结论 3D-ASL 能定量测量后循环血流量,对脑梗死的病理生理学的进一步研究有着重要价值。     

宝石能谱CT对胃癌区域淋巴结转移的诊断价值

目的 研究宝石能谱CT（gemstone spectral CT,GSCT）对胃癌区域淋巴结转移的诊断价值。方法 回顾分析52例胃镜下组织学活检证实为胃癌并行GSCT（GSCT 组,24 例）或双源CT（dual source CT,DSCT）（DSCT 组,28 例）的患者,两组除常规CT增强检查外,GSCT在动脉期应用能谱成像GSI（gemstone spectral imaging）软件,获得区域转移淋巴结与胃癌病变的能谱衰减曲线,对比分析两组CT结果与术后病理结果符合率及GSCT 对转移淋巴结评估的准确性。结果 通过计算淋巴结大小、CT 值差值及短长径比值的方法,GSCT 组评估淋巴结转移与病理符合率高于DSCT 组（P＜0.05）;胃癌及转移淋巴结能谱分析结果无统计学差异（P≥0.05）。结论 GSCT 检查胃癌区域淋巴结转移与病理结果的一致性高于DSCT;GSCT可对胃癌术前转移淋巴结进行详细判断,并对术前评估提供了理论依据和技术支持,具有重要的临床应用价值。     

血清CEA、CA19-9、CA72-4联合检测对胃癌诊断的价值

目的 探讨肿瘤标志物联合检测在胃癌诊断中的应用价值。方法 采用电化学发光分析法测定经病理检查证实的127 例各期胃癌患者和105 例胃良性病变及98 例健康体检者的血清CEA、CA19-9、CA72-4 水平并进行统计和分析,计算单项检测与联合检测诊断胃癌的灵敏度及特异度并作比较。结果 胃癌患者的CEA、CA19-9、CA72-4水平均明显高于胃良性病变患者及正常对照者, 差异均有统计学意义（均P＜0.05）;胃良性病变患者与正常对照者比较,差异无统计学意义（P＞0.05）。3 项指标联合检测诊断胃癌的灵敏度达87.4%,明显高于其他任一单项检测的灵敏度,差异有统计学意义（均P＜0.05）;3 项联合检测的特异度为52.7%,虽明显低于其他任一单项检测的特异度,但差异无统计学意义（均P＞0.05）。胃癌术后复发者CEA、CA19-9、CA72-4 的水平明显高于未复发者,差异有统计学意义（均P＜0.01）。结论 血清多项肿瘤标记物联合检测有助于提高胃癌诊断的阳性率。     

双源CT4D-CTA联合灌注成像在急性脑缺血性疾病中的应用价值

目的：探讨双源CT 4维血管造影（4D- CTA）及灌注成像（CTPI）在急性脑缺血性疾病中的应用价值。方法对30例临床拟诊为急性脑缺血性疾病的患者于发病2-24h内行头颅CT平扫和全脑CTPI检查,获得脑血流量、脑血容量、平均通过时间、达峰时间等参数图,同时获得4D- CTA图像;于发病的1-3d后行MRI检查,分析CT平扫、CTPI、4D- CTA、MRI表现。结果 CT平扫发现12例有16个缺血病灶,CTPI发现26例32个缺血病灶,MRI发现25例28个缺血病灶,4D- CTA显示有20例责任血管有不同程度的狭窄或闭塞。结论双源CT 4D- CTA联合灌注成像能为急性脑缺血患者提供全面、详细的影像学信息,对急性缺血性脑梗死的早期诊断和治疗有重要价值。     

Asymptotic Formulas for Thermography Based Recovery of Anomalies

We start from a realistic half space model for thermal imaging, which we then use to develop a mathematical asymptotic analysis well suited for the design of reconstruction algorithms. We seek to reconstruct thermal anomalies only through their rough features. With this way our proposed algorithms are stable against measurement noise and geometry perturbations. Based on rigorous asymptotic estimates, we first obtain an approximation for the temperature profile which we then use to design nonit-erative detection algorithms. We show on numerical simulations evidence that they are accurate and robust. Moreover, we provide a mathematical model for ultrasonic temperature imaging, which is an important technique in cancerous tissue ablation therapy.AMS subject classifications: 35R20, 35B30     

Imaging of multiple linear cracks using impedance data

This paper develops a fast, simple algorithm for locating one or more perfectly insulating, pair-wise disjoint, linear cracks in a homogeneous two-dimensional electrical conductor, using flux-potential boundary measurements. We also explore the issue of what types boundary inputs yield the most stable images.     

SEPARATION OF SCALES IN ELASTICITY IMAGING： A NUMERICAL STUDY

In magnetic resonance elastography, one seeks to reconstruct the shear modulus from measurements of the displacement field in the whole body. In this paper, we present an optimization approach which solves the problem by simply minimizing a discrepancy functional. In order to recover a complex anomaly in a homogenous medium, we first observe that the information contained in the wavefield should be decomposed into two parts, a ＂near-field＂ part in the region around the anomaly and a ＂far-field＂ part in the region away from the anomaly. As will be justified both theoretically and numerically, separating these scales provides a local and precise reconstruction.     

Multilevel Functional Principal Component Analysis for High-Dimensional Data

We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high-dimensional vectors observed at multiple visits. The proposed inferential methods do not require loading the entire dataset at once in the computer memory and instead use only sequential access to data. This allows deployment of our methodology on low-resource computers where computations can be done in minutes on extremely large datasets. Our methods are motivated by and applied to a study where hundreds of subjects were scanned using Magnetic Resonance Imaging (MRI) at two visits roughly five years apart. The original data possess over ten billion measurements. The approach can be applied to any type of study where data can be unfolded into a long vector including densely observed functions and images. Supplemental materials are provided with source code for simulations, some technical details and proofs, and additional imaging results of the brain study.     

Markov chain Monte Carlo Using an Approximation

This article presents a method for generating samples from an unnormalized posterior distribution f(·) using Markov chain Monte Carlo (MCMC) in which the evaluation of f(·) is very difficult or computationally demanding. Commonly, a less computationally demanding, perhaps local, approximation to f(·) is available, say f^**_x(·). An algorithm is proposed to generate an MCMC that uses such an approximation to calculate acceptance probabilities at each step of a modified Metropolis–Hastings algorithm. Once a proposal is accepted using the approximation, f(·) is calculated with full precision ensuring convergence to the desired distribution. We give sufficient conditions for the algorithm to converge to f(·) and give both theoretical and practical justifications for its usage. Typical applications are in inverse problems using physical data models where computing time is dominated by complex model simulation. We outline Bayesian inference and computing for inverse problems. A stylized example is given of recovering resistor values in a network from electrical measurements made at the boundary. Although this inverse problem has appeared in studies of underground reservoirs, it has primarily been chosen for pedagogical value because model simulation has precisely the same computational structure as a finite element method solution of the complete electrode model used in conductivity imaging, or “electrical impedance tomography.” This example shows a dramatic decrease in CPU time, compared to a standard Metropolis–Hastings algorithm.     

Detecting and imaging dielectric objects from real data: A shape-based approach

In this paper we investigate the performance of a shape-reconstruction technique as tested on the ‘Marseille data’. This approach, which is based on a level set technique, offers several advantages compared to other approaches, as for example well-defined boundaries and the incorporation of an intrinsic regularization in the form of a priori assumptions regarding the general structures in the medium. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions during this reconstruction process. Our algorithm is aiming at, not only detecting the objects, but simultaneously determining their approximate locations, sizes and dielectric properties. The numerical experiments show the utility of this method.     

On a bilinear optimization problem in parallel magnetic resonance imaging

This work is concerned with the structure of bilinear minimization problems arising in recovering sub-sampled and modulated images in parallel magnetic resonance imaging. By considering a physically reasonable simplified model exhibiting the same fundamental mathematical difficulties, it is shown that such problems suffer from poor gradient scaling and non-convexity, which causes standard optimization methods to perform inadequately. A globalized quasi-Newton method is proposed which is able to reconstruct both image and the unknown modulations without additional a priori information. Thus the present paper serves as a first contribution toward understanding and solving such bilinear optimization problems.     

Generalization of Strang's Preconditioner with Applications to Toeplitz Least Squares Problems

In this paper, we propose a method to generalize Strang's circulant preconditioner for arbitrary n-by-n matrices A_n. The th column of our circulant preconditioner Sn is equal to the th column of the given matrix A_n. Thus if A_n is a square Toeplitz matrix, then Sn is just the Strang circulant preconditioner. When Sn is not Hermitian, our circulant preconditioner can be defined as . This construction is similar to the forward-backward projection method used in constructing preconditioners for tomographic inversion problems in medical imaging. We show that if the matrix A_n has decaying coefficients away from the main diagonal, then is a good preconditioner for An. Comparisons of our preconditioner with other circulant-based preconditioners are carried out for some 1-D Toeplitz least squares problems: min ∥ b - Ax∥₂. Preliminary numerical results show that our preconditioner performs quite well, in comparison to other circulant preconditioners. Promising test results are also reported for a 2-D deconvolution problem arising in ground-based atmospheric imaging.     

Imaging through flesh tissue using fs electronic holographic gating method

The experimental results of imaging through flesh tissue using fs electronic holographic gating method is reported. In the experiment, Ti: sapphire mode-locked laser is used as light source, of which the repetition rate is 100 MHz, central wavelength 800 nm, duration of pulse 20 fs, output power 80 mW. Tissue is a 7 mm thick chicken slice, and the imaged object is a metal wire with diameter of 0.5 mm. A general CCD is used to record holograms and a clear image of metal wire is obtained. Several relevant problems are discussed.