Application of texture analysis to ventilation SPECT/CT data

An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV₁) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT

Objective

Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes.

Methods

A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images.

Results

The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004).

Conclusion

Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.

Second-order Texture Measurements of (3)He Ventilation MRI: Proof-of-concept Evaluation of Asthma Bronchodilator Response

RATIONALE AND OBJECTIVES

(3)He magnetic resonance imaging (MRI) can be used to quantify functional responses to asthma therapy and provocation. Ventilation imaging offers quantitative information beyond ventilation defects that have not yet been exploited. Therefore, our objective was to evaluate hyperpolarized (3)He MRI ventilation defect percent (VDP) and compare this and pulmonary function measurements to ventilation image texture features and their changes post-bronchodilator administration in patients with asthma.

MATERIALS AND METHODS

Volunteers with a diagnosis of asthma provided written informed consent to an ethics board-approved protocol and underwent pulmonary function tests and MRI before and after salbutamol inhalation. MR images were analyzed using VDP, and their texture was evaluated via gray-level run-length matrices. These texture classifiers were compared to VDP in responders to bronchodilation based on VDP (VDP responders) and forced expiratory volume in 1 s (FEV1) (FEV1 responders).

RESULTS

In total, 47 patients with asthma (18 males 39 ± 13 years, FEV1 = 79 ± 21%) reported significantly improved FEV1, FEV1/forced vital capacity (FVC), residual volume (RV)/total lung capacity (TLC) (all P = .0001) and VDP (P = .01) post-salbutamol. Post-salbutamol, VDP responders and nonresponders to salbutamol were significantly different for coarse-texture features including long-run emphasis (LRE) and long-run, low gray-level emphasis (LRLGE, both P < .05) and for FEV1 responders to salbutamol, there was significantly different long-run, high gray-level emphasis (LRHGE, P = .04). There were significant relationships for VDP with LRE (R = .50, P = .0003), LRLGE (R = .34, P = .02), and LRHGE (R = .56, P = .0001). Receiver operating characteristic curves showed VDP with the strongest performance (AUC = .92), followed by coarse-texture classifier LRHGE (AUC = .83), FEV1 (AUC = .80), LRE (AUC = .66), FVC (AUC = .58), and LRLGE (AUC = .42).

CONCLUSIONS

In patients with asthma, differences in ventilation patchiness post-salbutamol can be quantified using coarse-texture classifiers that are significantly different in bronchodilator responders.

Anterior cruciate ligament tears treated with percutaneous injection of autologous bone marrow nucleated cells: a case series

Introduction

This was a prospective case series designed to investigate treatment for anterior cruciate ligament (ACL) tears using an injection of autologous bone marrow concentrate.

Methods

Consecutive adult patients presenting to a private outpatient interventional musculoskeletal and pain practice with knee pain, ACL laxity on exam, and magnetic resonance imaging (MRI) evidence of a grade 1, 2, or 3 ACL tears with less than 1 cm retraction were eligible for this study. Eligible patients were treated with an intraligamentous injection of autologous bone marrow concentrate, using fluoroscopic guidance. Pre- and postprocedural sagittal MRI images of the ACLs were analyzed using ImageJ software to objectively quantify changes between pre- and posttreatment scans. Five different types of measurement of ACL pixel intensity were examined as a proxy for ligament integrity. In addition pain visual analog scale (VAS) and Lower Extremity Functional Scale (LEFS) values were recorded at baseline and at 1 month, 3 months, 6 months, and annually postinjection. Objective outcomes measured were pre- to post-MRI measurement changes, as analyzed by the ImageJ software. Subjective outcomes measured were changes in the VAS and LEFS, and a self-rated percentage improvement.

Results

Seven of ten patients showed improvement in at least four of five objective measures of ACL integrity in their postprocedure MRIs. In the entire study group, the mean gray value, median, raw integrated density, and modal gray value all decreased toward low-signal ACLs (P=0.01, P=0.02, P=0.002, and P=0.08), indications of improved ligament integrity. Seven of ten patients responded to the self-rated metrics follow up. The mean VAS change was a decrease of 1.7 (P=0.25), the mean LEFS change was an increase of 23.3 (P=0.03), and mean reported improvement was 86.7%.

Conclusion

Based on this small case series, autologous bone marrow concentrate shows promise in the treatment of grade 1, 2, and possibly grade 3 ACL tears without retraction. Further investigation using a controlled study design is warranted.

Quantitative analysis of colorectal lesions observed on magnified endoscopy images

BACKGROUND

Various surface mucosal pit patterns, as recognized by endoscopists, correlate with the histologic features of colorectal cancers. We investigated whether magnified endoscopy images of these pit patterns could be analyzed quantitatively and thus facilitate computer-aided diagnosis of colorectal lesions.

METHODS

We applied both texture analysis and scale-invariant feature transform (SIFT) descriptors and discriminant analysis to magnified endoscopy images of 165 neoplastic colorectal lesions (pit patterns: type III(L)/IV, n = 44; type V(I)-mildly irregular, n = 36; type V(I)-severely irregular, n = 45; type V(N), n = 40) [histologic findings: tubular adenoma (TA), n = 56; carcinoma with intramucosal or even scant submucosal invasion (M/SM-s), n = 52, carcinoma with massive submucosal invasion (SM-m), n = 57]. We analyzed differences in pit pattern values and corresponding histologic values to determine whether the values were diagnostically meaningful.

RESULTS

Gray-level difference matrix (GLDM) inverse difference moment and spatial gray-level dependence matrix (SGLDM) local homogeneity values differed significantly between type III(L)/IV and type V(N) pit patterns. Values differed significantly for each analyzed feature between type III(L)/IV and type V(I)-severely irregular patterns and were high but descending for type III(L)/IV, type V(I)-mildly irregular, and type V(I)-severely irregular pit patterns (in that order). Similarly, texture analysis yielded high but descending values for TA, M/SM-s, and SM-m (in that order). Furthermore, SIFT descriptors and discriminant analysis yielded differences that were superior to those obtained by texture analyses.

CONCLUSIONS

Computer analysis of magnified endoscopy images for the diagnosis of colorectal lesions appears feasible. We anticipate further developments in the computer-aided diagnosis of pit patterns on magnified endoscopy images.