Automatic left ventricle segmentation in volumetric SPECT data set by variational level set

Introduction

Left ventricle (LV) quantification in nuclear medicine images is a challenging task for myocardial perfusion scintigraphy. A hybrid method for left ventricle myocardial border extraction in SPECT datasets was developed and tested to automate LV ventriculography.

Methods

Automatic segmentation of the LV in volumetric SPECT data was implemented using a variational level set?algorithm. The method consists of two steps: (1) initialization and (2) segmentation. Initially, we estimate the initial closed curves in SPECT images using adaptive thresholding and morphological operations. Next, we employ the initial closed curves to estimate the final contour by variational level set. The performance of the proposed approach was evaluated by comparing manually obtained boundaries with automated segmentation contours in 10 SPECT data sets obtained from adult patients. Segmented images by proposed methods were visually compared with manually outlined contours and the performance was evaluated using ROC analysis.

Results

The proposed method and a traditional level set method were compared by computing the sensitivity and specificity of ventricular outlines as well as ROC analysis. The results show that the proposed method can effectively segment LV regions with a sensitivity and specificity of 88.9 and 96.8%, respectively. Experimental results demonstrate the effectiveness and reasonable robustness of the automatic method.

Conclusion

A new variational level set technique was able to automatically trace the LV contour in cardiac SPECT data sets, based on the characteristics of the overall region of LV images. Smooth and accurate LV contours were extracted using this new method, reducing the influence of nearby interfering structures including a hypertrophied right ventricle, hepatic or intestinal activity, and pulmonary or intramammary activity.     

Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography

Rationale and objectives

Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume.

Materials and methods

The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging. This work picks the middle of mid-ventricular level image slices as the lead slice. The proposed method develops two contouring modes to sketch the myocardium contour on the lead slice. By establishing the obtained contours as the initial contours, the region-growing method is employed to identify the contour of the myocardial region for each slice. The convex-hull finding algorithm is then used to refine the extracted contour. Finally, the width properties of the myocardial region and the morphological operators are used to obtain the entire LV myocardial volume.

Results

Twenty-seven healthy patients who had no symptoms of ischemic heart disease are examined to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results using computer simulation reveal that the proposed method reliably identifies contours similar to those obtained using manual sketching.

Conclusion

The proposed method provides robust contouring for the LV myocardium on delayed-phase MDCT. The potential role of this technique may substantially reduce the time required to sketch manually a precise contour with high stability.     

Automated liver lesion detection in CT images based on multi-level geometric features

Purpose

Due to the increasing number of liver cancer cases in clinical practice, there is a significant need for efficient tools for computer-assisted liver lesion analysis. A wide range of clinical applications, such as lesion characterization, quantification and follow-up, can be facilitated by automated liver lesion detection. Liver lesions vary significantly in size, shape, density and heterogeneity, which make them difficult to detect automatically. The goal of this work was to develop a method that can detect all types of liver lesions with high sensitivity and low false positive rate within a short run time.

Methods

The proposed method identifies abnormal regions in liver CT images based on their intensity using a multi-level segmentation approach. The abnormal regions are analyzed from the inside-out using basic geometric features (such as asymmetry, compactness or volume). Using this multi-level shape characterization, the abnormal regions are classified into lesions and other region types (including vessel, liver boundary). The proposed analysis also allows defining the contour of each finding. The method was trained on a set of 55 cases involving 120 lesions and evaluated on a set of 30 images involving 59 (various types of) lesions, which were manually contoured by a physician.

Results

The proposed algorithm demonstrated a high detection rate (92 %) at a low (1.7) false positive per case (precision 51 %), when the method was started from a manually contoured liver. The same level of false positive per case (1.6) and precision (51 %) was achieved at a somewhat lower detection rate (85 %), when the volume of interest was defined by a fully automated liver segmentation.

Conclusions

The proposed method can efficiently detect liver lesions irrespective of their size, shape, density and heterogeneity within half a minute. According to the evaluation, its accuracy is competitive with the actual state-of-the-art approaches, and the contour of the detected findings is acceptable in most of the cases. Future work shall focus on more precise lesion contouring so that the proposed method can be a solid basis for fully automated liver tumour burden estimation.     

Segmentation of radiographic images under topological constraints: application to the femur

Purpose

A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning.

Methods

The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford–Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation.

Results

Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest.

Conclusions

A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.     

Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

Purpose   

Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.

Methods   

First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method’s performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself.

Results   

The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images.

Conclusion   

A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.     

Quantitation of the thickness of the non-enhanced myocardial rim predicts recovery of territorial myocardial function in chronic ischemic heart disease: a cardiac magnetic resonance imaging study

Objectives

We sought to determine whether the thickness of the non-contrast-enhanced myocardial rim (RIM) predicts recovery of territorial myocardial function after revascularization in chronic ischemic cardiomyopathy (ICM).

Background

Non-contrast-enhanced dysfunctional myocardium at late gadolinium-enhanced CMR depicts the presence of viable myocardium.

Methods

In 29 patients (65 ± 8 years) with ICM (EF 33 ± 10), ceCMR and cine images were acquired 5 ± 10 days before revascularization. Cine images were repeated after 6 months. Regional wall thickness, wall thickening and RIM were determined in each of 12 segments per short-axis slice (4–8/patient), which were assigned to the respective supplying coronary artery (LAD, LCX and RCA). A threshold for normal wall-thickening was derived from a control group (n = 14; 52 ± 17 years). Functional improvement at follow-up was defined as wall thickening >2 mm.

Results

Of the 1,896 analyzed segments, 655 segments showed severe dysfunction. At follow-up, 307 segments demonstrated functional improvement. The RIM differed between segments with and without improvement (6.6 ± 2.4 mm vs. 2.8 ± 2.0 mm; p < 0.0001). The area under the receiver operator characteristic (ROC) for predicting overall functional recovery was 0.91 (95%, CI 0.88–0.93, p < 0.001). A RIM of 4.0 mm predicted functional recovery after revascularization of the supplying coronary artery with a sensitivity and a specificity of 88 and 82% for the LAD, 96 and 86% for the RCA and 88 and 83% for the LCX, respectively.

Conclusion

RIM may be a useful marker for predicting territorial functional recovery after revascularization in patients with chronic ICM.     

Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study

Purpose

In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images.

Methods

Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images.

Results

We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 % with the number of false positives/case at 5.0, and it was 8 % higher than the sensitivity of independent detection systems using CT or PET images alone.

Conclusion

   Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.     

Value of black blood T2* cardiovascular magnetic resonance

Background

Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare coronary artery anomaly. This study shows the role of cardiovascular magnetic resonance (CMR) in assessing young patients following surgical repair of ALCAPA.

Methods

6 patients, aged 9-21 years, with repaired ALCAPA (2 Tackeuchi method, 4 direct re-implantation) underwent CMR because of clinical suspicion of myocardial ischemia. Imaging used short and long axis cine images (assess ventricular function), late-gadolinium enhancement (LGE) (detect segmental myocardial fibrosis), adenosine stress perfusion (detect reversible ischaemia) and 3D whole-heart imaging (visualize proximal coronary arteries).

Results

The left ventricular (LV) global systolic function was preserved in all patients (mean LV ejection fraction = 62.7% ± 4.23%). The LV volumes were within the normal ranges, (mean indexed LVEDV = 75.4 ± 3.5 ml/m², LVESV = 31.6 ± 9.4 ml/m²). In 1 patient, hypokinesia of the anterior segments was visualized. Five patients showed sub-endocardial LGE involving the basal, antero-lateral wall and the anterior papillary muscle. Three patients had areas of reversible ischemia. In these 3, 3D whole-heart MRA showed that the proximal course of the left coronary artery was occluded (confirmed with cardiac catheterisation).

Conclusions

CMR is a good, non-invasive, radiation-free investigation in the post-surgical evaluation of ALCAPA. In referred patients we show that basal, antero-lateral sub-endocardial myocardial fibrosis is a characteristic finding. Furthermore, stress adenosine CMR perfusion, can identify reversible ischemia in this group, and was indicative of left coronary artery occlusion.     

Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data

Purpose

   Over 40,000 annuloplasty rings are implanted each year in the USA to treat mitral regurgitation. However, the used measuring techniques to select a suitable annuloplasty ring are imprecise and highly depending on the expert’s experience. This can cause a re-occurrence of the mitral regurgitation or an annuloplasty ring dehiscence, and thus the necessity of a re-operation. We propose a method to create a 4D model of the mitral annulus from ultrasound data to enable precise measurement and patient-specific implant planning.

Methods

   An initial mitral annulus model is placed interactively in the 4D image data by defining commissure points and the annulus plane for one time step in diastole and systole. The model is automatically optimized using distinct image features. A shape and pose prior of the mitral annulus is used to compensate for artifacts and to enforce a plausible anatomical morphology, while a temporal alignment ensures a natural motion of the 4D model.

Results

   Ground truth data were created for 4D images of 42 patients with varying image quality. A parameter and shape prior training was performed on a third of the ground truth data, while the rest was used to validate the method. The average error of the resulting mitral annulus models was computed as 2.25 ( \(\pm 0.38\) ) mm. The average expert standard deviation was determined as 1.86 ( \(\pm 0.32\) ) mm.

Conclusion

   The proposed method enables the 4D modeling of mitral annuli based on ultrasound data in less than 2 min. The resulting models are comparable to manually delineated models and can be used for measurements of annular geometries and patient-specific annuloplasty treatment planning.     

Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

Purpose

   An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility.

Methods

   A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes’ bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images.

Results

   The total time to initialize and simulate an MRgLITT procedure using the GUI was \(<\) 5 min. Each independent simulation took \(<\) 30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour \((57\,^{\circ }\hbox {C})\) was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm \((\sigma \,=\,0.4\,\hbox {mm})\) , whereas the mean Dice similarity coefficient was 0.93 \((\sigma =0.026)\) .

Conclusions

   We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.     

An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

Purpose

Accurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.

Methods

Firstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.

Results

We obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).

Conclusion

We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.

     

Needle deflection estimation: prostate brachytherapy phantom experiments

Purpose

The performance of a fusion-based needle deflection estimation method was experimentally evaluated using prostate brachytherapy phantoms. The accuracy of the needle deflection estimation was determined. The robustness of the approach with variations in needle insertion speed and soft tissue biomechanical properties was investigated.

Methods

A needle deflection estimation method was developed to determine the amount of needle bending during insertion into deformable tissue by combining a kinematic deflection model with measurements taken from two electromagnetic trackers placed at the tip and the base of the needle. Experimental verification of this method for use in prostate brachytherapy needle insertion procedures was performed. A total of 21 beveled tip, 18 ga, 200 mm needles were manually inserted at various speeds through a template and toward different targets distributed within 3 soft tissue mimicking polyvinyl chloride prostate phantoms of varying stiffness. The tracked positions of both the needle tip and base were recorded, and Kalman filters were applied to fuse the sensory information. The estimation results were validated using ground truth obtained from fluoroscopy images.

Results

The manual insertion speed ranged from 8 to 34 mm/s, needle deflection ranged from 5 to 8 mm at an insertion depth of 76 mm, and the elastic modulus of the soft tissue ranged from 50 to 150 kPa. The accuracy and robustness of the estimation method were verified within these ranges. When compared to purely model-based estimation, we observed a reduction in needle tip position estimation error by \(52\pm 17\)  % (mean  \(\pm \)  SD) and the cumulative deflection error by \(57\pm 19\)  %.

Conclusions

Fusion of electromagnetic sensors demonstrated significant improvement in estimating needle deflection compared to model-based methods. The method has potential clinical applicability in the guidance of needle placement medical interventions, particularly prostate brachytherapy.     

Contour propagation using non-uniform cubic B-splines for lung tumor delineation in 4D-CT

Purpose

Accurate target delineation is a critical step in radiotherapy. In this study, a robust contour propagation method is proposed to help physicians delineate lung tumors in four-dimensional computer tomography (4D-CT) images efficiently and accurately.

Methods

The proposed method starts with manually delineated contours on the reference phase. Each contour is fitted by a non-uniform cubic B-spline curve, and its deformation on the target phase is achieved by moving its control vertexes such that the intensity similarity between the two contours is maximized. Since contour is usually the boundary of lesion or tissue which may deform quite differently from the tissues outside the boundary, the proposed method treats each contour as a deformable entity, a non-uniform cubic B-spline curve, and focuses on the registration of contour entity instead of the entire image to avoid the deformation of contour to be smoothed by its surrounding tissues, meanwhile to greatly reduce the time consumption while keeping the accuracy of the contour propagation. Eighteen 4D-CT cases with 444 gross tumor volume (GTV) contours manually delineated slice by slice on the maximal inhale and exhale phases are used to verify the proposed method.

Results

The Jaccard similarity coefficient (JSC) between the propagated GTV and the manually delineated GTV is 0.885 ± 0.026, and the Hausdorff distance (HD) is \(2.93\,\pm \,0.93\) mm. In addition, the time for propagating GTV to all the phases is 3.67 ± 3.41 minutes. The results are better than fast adaptive stochastic gradient descent (FASGD) B-spline method, 3D+t B-spline method and diffeomorphic Demons method.

Conclusions

The proposed method is useful to help physicians delineate target volumes efficiently and accurately.

     

Extracellular volume fraction mapping in the myocardium, part 2: initial clinical experience

Background

The purpose of this study was to quantify myocardial strain on the subendocardial and epicardial layers of the left ventricle (LV) using tagged cardiovascular magnetic resonance (CMR) and to investigate the transmural degree of contractile impairment in the chronic ischemic myocardium.

Methods

3T tagged CMR was performed at rest in 12 patients with severe coronary artery disease who had been scheduled for coronary artery bypass grafting. Circumferential strain (C-strain) at end-systole on subendocardial and epicardial layers was measured using the short-axis tagged images of the LV and available software (Intag; Osirix). The myocardial segment was divided into stenotic and non-stenotic segments by invasive coronary angiography, and ischemic and non-ischemic segments by stress myocardial perfusion scintigraphy. The difference in C-strain between the two groups was analyzed using the Mann-Whitney U-test. The diagnostic capability of C-strain was analyzed using receiver operating characteristics analysis.

Results

The absolute subendocardial C-strain was significantly lower for stenotic (-7.5 ± 12.6%) than non-stenotic segment (-18.8 ± 10.2%, p < 0.0001). There was no difference in epicardial C-strain between the two groups. Use of cutoff thresholds for subendocardial C-strain differentiated stenotic segments from non-stenotic segments with a sensitivity of 77%, a specificity of 70%, and areas under the curve (AUC) of 0.76. The absolute subendocardial C-strain was significantly lower for ischemic (-6.7 ± 13.1%) than non-ischemic segments (-21.6 ± 7.0%, p < 0.0001). The absolute epicardial C-strain was also significantly lower for ischemic (-5.1 ± 7.8%) than non-ischemic segments (-9.6 ± 9.1%, p < 0.05). Use of cutoff thresholds for subendocardial C-strain differentiated ischemic segments from non-ischemic segments with sensitivities of 86%, specificities of 84%, and AUC of 0.86.

Conclusions

Analysis of tagged CMR can non-invasively demonstrate predominant impairment of subendocardial strain in the chronic ischemic myocardium at rest.     

Computer-aided focal liver lesion detection

Purpose

Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.

Method

   A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components. It subsequently uses a discriminative approach to suppress false positives with the advantage of tumoroid, a novel measurement combining three shape features spherical symmetry, compactness and size.

Results

   This method was tested on 71 abdominal CT datasets (5,854 slices from 61 patients, with 261 FLLs covering six pathological types) and evaluated using the free-response receiver operating characteristic (FROC) curves. Overall, it achieved a true positive rate of 90 % with one false positive per liver. It degenerated gently with the decrease in lesion sizes to 30 ml. It achieved a true-positive rate of 36 % when tested on the lesions less than 4 ml. The average computing time of the lesion detection is 4 min and 28 s per CT volume on a PC with 2.67 GHz CPU and 4.0 GB RAM.

Conclusions

   The proposed method is comparable to the radiologists’ visual investigation in terms of efficiency. The tool has great potential to reduce radiologists’ burden in going through thousands of images routinely.     

Simultaneous extraction of carotid artery intima-media interfaces in ultrasound images: assessment of wall thickness temporal variation during the cardiac cycle

Objectives

   The aim of this work is to present and evaluate a novel segmentation method for localizing the contours of the intima-media complex in the carotid artery wall through longitudinal ultrasound B-mode imaging. The method is used to investigate the association between atherosclerosis risk factors and the cyclic variation of the intima-media thickness during the heart beat.

Methods

   The framework introduced is based on two main features. The first is a simultaneous extraction of both the lumen-intima and the media-adventitia interfaces, using the combination of an original shape-adapted filter bank and a specific dynamic programming scheme. The second is an innovative spatial transformation that eases the extraction of skewed and curved contours, and exploits the result from the previous image as a priori information, when processing the current image. The intima-media thickness is automatically derived from the estimated contours for each time step during the cardiac cycle. Our method was evaluated in vivo on 57 healthy volunteers and 25 patients at high cardiovascular risk. Reference contours were generated for each subject by averaging the tracings performed by three experienced observers.

Results

   Segmentation errors were \(29 \pm 27\,\upmu \hbox {m}\) for the lumen-intima interface, \(42 \pm 38\,\upmu \hbox {m}\) for the media-adventitia interface, and \(22 \pm 16\,\upmu \hbox {m}\) for the intima-media thickness. This uncertainty was similar to inter- and intra-observer variability. Furthermore, the amplitude of the temporal variation in thickness of the intima-media layers during the cardiac cycle was significantly higher in at-risk patients compared to healthy volunteers \((79 \pm 36\) vs. \(64 \pm 26\,\upmu \hbox {m},\, p=0.032)\) .

Conclusion

   The method proposed may provide a relevant diagnostic aid for atherosclerosis screening in clinical studies.     

Unenhanced CT for the detection of renal cell carcinoma: effect of tumor size and contour type

Purpose

To evaluate effect of tumor size and contour type for the detection of renal cell carcinoma (RCC) on unenhanced CT.

Methods

This retrospective institutional review board approved study that includes 111 patients with RCC and 100 patients without RCC who underwent unenhanced CT. Two readers performed a blinded and independent review of the presence of RCC on unenhanced CT. The area under the receiver operating characteristic curves (AUC) was compared by tumor size (<3 cm: small, or ≥3 cm: large) and contour type (endophytic, mesophytic, or exophytic).

Results

For tumor size, the AUC for small RCC (0.70 and 0.78, for reader 1 and reader 2) was significantly lower than that for large RCC (0.97 and 0.99, for reader 1 and reader 2) (p < 0.001). As for contour type of tumor, the AUC for endophytic RCC (0.60 and 0.71, for reader 1 and reader 2) was significantly lower than that for mesophytic RCC (0.95 and 0.98, for reader 1 and reader 2) and exophytic RCC (0.98 and 0.99, reader 1 and reader 2) (p < 0.001).

Conclusion

On unenhanced CT, tumor size and contour type can affect the detection of RCC. While most large or exophytic RCC can be easily detected, the detection of small and endophytic RCC is highly limited.     

Inadequate RAAS suppression is associated with excessive left ventricular mass and systo-diastolic dysfunction

Background

Inadequate suppression of renin–angiotensin–aldosterone system (RAAS) following postural maneuvers may have detrimental effects on cardiac structure and function. In this study, we aimed to appraise the clinical significance of this phenomenon by assessing its relation with inappropriate ventricular mass (ILVM), an adverse phenotype of LV remodeling and dysfunction.

Methods

Both supine and upright plasma renin activity (PRA) and aldosterone concentrations (PAC) were measured in 115 young newly diagnosed hypertensive subjects. 24-h ambulatory blood pressure monitoring and echocardiographic evaluation including tissue Doppler imaging (TDI) were also performed. Patients were divided as follows: (1) normal PRA and PAC (N) (n = 63); (2) suppressible RAAS (SR) in supine position (n = 27); (3) not suppressible RAAS (NSR) (n = 25). ILVM was expressed as the observed/predicted LV mass ratio ×100 (%PLVM), while LV dysfunction (LVD) was identified by TDI-derived myocardial performance index (MPI).

Results

NSR showed a higher prevalence of ILVM than SR and N. As compared with N and SR, NSR patients had reduced indices of systolic and diastolic function. MPI of the LV as well as prevalence of LVD was also significantly higher in the NSR group. Regression models showed that lack of RAAS suppression was independently associated with ILVM and LVD.

Conclusions

Prevalence of ILVM and LVD is higher in patients without clinostatic RAAS suppression. Our findings encourage the assessment of RAAS deregulation to better estimate individual cardiovascular risk in patients with arterial hypertension.     

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs

Purpose

   Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs.

Methods

   Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence.

Results

   A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was $0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$ , requiring $<\!5$  s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference.

Conclusions

   A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.     

Comparison of 4D-MicroSPECT and MicroCT for Murine Cardiac Function

Purpose

The objective of this study was to compare a new generation of four-dimensional micro-single photon emission computed tomography (microSPECT) with microCT for the quantitative in vivo assessment of murine cardiac function.

Procedures

Four-dimensional isotropic cardiac images were acquired from anesthetized normal C57BL/6 mice with either microSPECT (n?=?6) or microCT (n?=?6). One additional mouse with myocardial infarction (MI) was scanned with both modalities. Prior to imaging, mice were injected with either technetium tetrofosmin for microSPECT or a liposomal blood pool contrast agent for microCT. Segmentation of the left ventricle (LV) was performed using Vitrea (Vital Images) software, to derive global and regional function.

Results

Measures of global LV function between microSPECT and microCT groups were comparable (e.g., ejection fraction?=?71?±?6 % microSPECT and 68?±?4 % microCT). Regional functional indices (wall motion, wall thickening, regional ejection fraction) were also similar for the two modalities. In the mouse with MI, microSPECT identified a large perfusion defect that was not evident with microCT.

Conclusions

Despite lower spatial resolution, microSPECT was comparable to microCT in the quantitative evaluation of cardiac function. MicroSPECT offers an advantage over microCT in the ability to evaluate simultaneously myocardial radiotracer distribution and function, simultaneously. MicroSPECT should be considered as an alternative to microCT and magnetic resonance for preclinical cardiac imaging in the mouse.