A framework of whole heart extracellular volume fraction estimation for low-dose cardiac CT images

Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.

An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images and low contrast in computed tomography (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.

A framework to measure myocardial extracellular volume fraction using dual-phase low dose CT images

PURPOSE

Myocardial extracellular volume fraction (ECVF) is a surrogate imaging biomarker of diffuse myocardial fibrosis, a hallmark of pathologic ventricular remodeling. Low dose cardiac CT is emerging as a promising modality to detect diffuse interstitial myocardial fibrosis due to its fast acquisition and low radiation; however, the insufficient contrast in the low dose CT images poses great challenge to measure ECVF from the image.

METHODS

To deal with this difficulty, the authors present a complete ECVF measurement framework including a point-guided myocardial modeling, a deformable model-based myocardium segmentation, nonrigid registration of pre- and post-CT, and ECVF calculation.

RESULTS

The proposed method was evaluated on 20 patients by two observers. Compared to the manually delineated reference segmentations, the accuracy of our segmentation in terms of true positive volume fraction (TPVF), false positive volume fraction (FPVF), and average surface distance (ASD), were 92.18% ± 3.52%, 0.31% ± 0.10%, 0.69 ± 0.14 mm, respectively. The interobserver variability measured by concordance correlation coefficient regarding TPVF, FPVF, and ASD were 0.95, 0.90, 0.94, respectively, demonstrating excellent agreement. Bland-Altman method showed 95% limits of agreement between ECVF at CT and ECVF at MR.

CONCLUSIONS

The proposed framework demonstrates its efficiency, accuracy, and noninvasiveness in ECVF measurement and dramatically advances the ECVF at cardiac CT toward its clinical use.

Myocardial perfusion imaging with cardiac computed tomography: state of the art

Cardiac computed tomography (CCT) has become an important tool for the anatomic assessment of patients with suspected coronary disease. Its diagnostic accuracy for detecting the presence of underlying coronary artery disease and ability to risk stratify patients are well documented. However, the role of CCT for the physiologic assessment of myocardial perfusion during resting and stress conditions is only now emerging. With the addition of myocardial perfusion imaging to coronary imaging, CCT has the potential to assess both coronary anatomy and its functional significance with a single non-invasive test. In this review, we discuss the current state of CCT myocardial perfusion imaging for the detection of myocardial ischemia and myocardial infarction and examine its complementary role to CCT coronary imaging.

Point-guided modeling and segmentation of myocardium for low dose cardiac CT images

Cardiac CT is emerging as a preferable modality to detect myocardial stress/rest perfusion; however the insufficient contrast of myocardium on CT image makes its segmentation difficult. In this paper, we present a point-guided modeling and deformable model-based segmentation method. This method first builds a triangular surface model of myocardium through Bézier contour fitting based on a few points selected by clinicians. Then, a deformable model-based segmentation method is developed to refine the segmentation result. The experiments on 8 cases show the accuracy of the segmentation in terms of true positive volume fraction, false positive volume fractions, and average surface distance can reach 91.0%, 0.3%, and 0.6mm, respectively. The comparison between the proposed method and a graph cut-based method is performed. The results demonstrate that this method is effective in improving the accuracy further.

Measurement of myocardial extracellular volume fraction by using equilibrium contrast-enhanced CT: validation against histologic findings

PURPOSE

To develop and validate equilibrium contrast material-enhanced computed tomography (CT) to measure myocardial extracellular volume (ECV) fraction by using a histologic reference standard and to compare equilibrium CT with equilibrium contrast-enhanced magnetic resonance (MR) imaging.

MATERIALS AND METHODS

A local ethics committee approved the study, and all subjects gave fully informed written consent. An equilibrium CT protocol was developed using iohexol at 300 mg of iodine per milliliter (bolus of 1 mg per kilogram of body weight administered at a rate of 3 mL/sec, followed immediately by an infusion of 1.88 mL/kg per hour with CT imaging before and at 25 minutes after injection of bolus of contrast agent) and ECV within the myocardial septum measured using both equilibrium CT and equilibrium MR imaging in patients with severe aortic stenosis. Biopsy samples of the myocardial septum collected during valve replacement surgery were used for histologic quantification of extracellular fibrosis with picrosirius red staining. Equilibrium CT- and equilibrium MR imaging-derived ECV measurements were compared with histologically quantified fibrosis by using Pearson correlation. Agreement between equilibrium CT and equilibrium MR imaging was assessed by using Bland-Altman comparison.

RESULTS

Twenty-three patients (16 male, seven female; mean age, 70.8 years; standard deviation, 8.3) were recruited. The mean percentage of histologic fibrosis was 18% (intersubject range, 5%-40%). There was a significant correlation between both equilibrium CT- and equilibrium MR imaging-derived ECV and percentage of histologic fibrosis (r = 0.71 [P < .001] and r = 0.84 [P < .0001], respectively). Equilibrium CT-derived ECV was significantly correlated to equilibrium MR imaging-derived ECV (r = 0.73).

CONCLUSION

ECV measured by using equilibrium CT in patients with aortic stenosis correlates with histologic quantification of myocardial fibrosis and with ECV derived by using equilibrium MR imaging.

Cardiovascular health informatics: risk screening and intervention

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.

3D left ventricular extracellular volume fraction by low-radiation dose cardiac CT: assessment of interstitial myocardial fibrosis

BACKGROUND

Myocardial fibrosis leads to impaired cardiac function and events. Extracellular volume fraction (ECV) assessed with an iodinated contrast agent and measured by cardiac CT may be a useful noninvasive marker of fibrosis.

OBJECTIVE

The purpose of this study was to develop and evaluate a 3-dimensional (3D) ECV calculation toolkit (ECVTK) for ECV determination by cardiac CT.

METHODS

Twenty-four subjects (10 systolic heart failure, age, 60 ± 17 years; 5 diastolic failure, age 56 ± 20 years; 9 matched healthy subjects, age 59 ± 7 years) were evaluated. Cardiac CT examinations were done on a 320-multidetector CT scanner before and after 130 mL of iopamidol (Isovue-370; Bracco Diagnostics, Plainsboro, NJ, USA) was administered. A calcium score type sequence was performed before and 7 minutes after contrast with single gantry rotation during 1 breath hold and single cardiac phase acquisition. ECV was calculated as (ΔHUmyocardium/ΔHUblood) × (1 - Hct) where Hct is the hematocrit, and ΔHU is the change in Hounsfield unit attenuation = HUafter iodine - HUbefore iodine. Cardiac magnetic resonance imaging was performed to assess myocardial structure and function.

RESULTS

Mean 3D ECV values were significantly higher in the subjects with systolic heart failure than in healthy subjects and subjects with diastolic heart failure (mean, 41% ± 6%, 33% ± 2%, and 35% ± 5%, respectively; P = 0.02). Interobserver and intraobserver agreements were excellent for myocardial, blood pool, and ECV (intraclass correlation coefficient, >0.90 for all). Higher 3D ECV by cardiac CT was associated with reduced systolic circumferential strain, greater end-diastolic and -systolic volumes, and lower ejection fraction (r = 0.70, r = 0.60, r = 0.73, and r = -0.68, respectively; all P < 0.001).

CONCLUSION

3D ECV by cardiac CT can be performed with ECVTK. We demonstrated increased ECV in subjects with systolic heart failure compared with healthy subjects. Cardiac CT results also showed good correlation with important functional heart biomarkers, suggesting the potential for myocardial tissue characterization with the use of 3D ECV by cardiac CT. This trial is registered at www.ClinicalTrials.gov as NCT01160471.