[show abstract][hide abstract] ABSTRACT: A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient. The performance of the method was assessed by comparing estimated contours to references using different metrics. Results showed that the segmentation procedure is fast, accurate, independent on the operator as well as on the specific geometrical characteristics of the prosthetic component, and able to compensate for amount of blurring and illumination gradient. Importantly, the method allows a strong reduction of required user interaction time when compared to traditional segmentation techniques. Its effectiveness and robustness in different image conditions, together with simplicity and fast implementation, make this prosthetic component segmentation procedure promising and suitable for multiple clinical applications including assessment of in vivo joint kinematics in a variety of cases.
Medical & Biological Engineering 03/2012; 50(6):631-40. · 1.76 Impact Factor
[show abstract][hide abstract] ABSTRACT: We present the development and testing of a semi-automated tool to support the diagnosis of left ventricle (LV) dysfunctions from cardiac magnetic resonance (CMR). CMR short-axis images of the LVs were obtained in 15 patients and processed to detect endocardial and epicardial contours and compute volume, mass and regional wall motion (WM). Results were compared with those obtained from manual tracing by an expert cardiologist. Nearest neighbour tracking and finite-element theory were merged to calculate local myocardial strains and torsion. The method was tested on a virtual phantom, on a healthy LV and on two ischaemic LVs with different severity of the pathology. Automated analysis of CMR data was feasible in 13/15 patients: computed LV volumes and wall mass correlated well with manually extracted data. The detection of regional WM abnormalities showed good sensitivity (77.8%), specificity (85.1%) and accuracy (82%). On the virtual phantom, computed local strains differed by less than 14 per cent from the results of commercial finite-element solver. Strain calculation on the healthy LV showed uniform and synchronized circumferential strains, with peak shortening of about 20 per cent at end systole, progressively higher systolic wall thickening going from base to apex, and a 10° torsion. In the two pathological LVs, synchronicity and homogeneity were partially lost, anomalies being more evident for the more severely injured LV. Moreover, LV torsion was dramatically reduced. Preliminary testing confirmed the validity of our approach, which allowed for the fast analysis of LV function, even though future improvements are possible.
Interface focus: a theme supplement of Journal of the Royal Society interface 06/2011; 1(3):384-95. · 2.21 Impact Factor
[show abstract][hide abstract] ABSTRACT: Dynamic, ECG-gated, steady-state free precession short-axis images were obtained (GE Healthcare, 1.5T) in 8-12 slices in 15 patients with previous myocardial infarction. An expert cardiologist provided the reference values for: 1) left ventricular (LV) volumes and mass, by manually tracing endo and epicardial contours; 2) regional wall motion (WM) interpretation, by grading (normal, abnormal) three slices selected at apical, mid and basal level. Custom software based on image noise distribution and on image gradient was applied, from which end-diastolic (ED) and end-systolic (ES) volumes and mass were computed, as well as regional fractional area change (RFAC), from which automated classification of regional WM abnormality was defined. Comparison with reference values was performed by: 1) linear regression and Bland-Altman analyses for LV volumes and mass; 2) levels of agreement between the cardiologist WM grades and the automated classification. Optimal correlations (r<sup>2</sup>>;.97) and no bias were found for ED and ES volumes, while LV mass resulted in a good correlation (ED: r<sup>2</sup> = .81; ES: r<sup>2</sup> = .74) with a minimal overestimation (ED: 15.2g; ES: 8.7g) and narrow 95% limits of agreement (ED: ±30g; ES: ±33g). The automated interpretation resulted in high sensitivity, specificity, and accuracy (78%, 85%, 82%, respectively) of WM abnormalities. Combined automated endo and epicardial border detection from MRI images provides reliable measurements of LV dimensions and regional WM classification.
[show abstract][hide abstract] ABSTRACT: We developed a method for automated quantification of myocardial perfusion from cardiac magnetic resonance (CMR) images. Our approach uses region-based and edge-based level set techniques for endocardial and epicardial border detection combined with non-rigid registration achieved by a 2D multi-scale cross-correlation and contour adaptation. This method was tested on 66 short-axis image sequences (Philips 1.5T) obtained in 11 patients at rest and during vasodilator stress at 3 levels of the left ventricle during first pass of a Gadolinium-DTPA bolus. Myocardial ROIs were automatically defined and contrast enhancement curves were constructed throughout the image sequence. Analysis of one sequence required <;1 min and resulted in endo- and epicardial boundaries that were judged accurate. Curves obtained during stress showed the typical pattern of first-pass perfusion with SNR of 19±4, as well as increased contrast inflow rate (0.031±0.013 vs 0.014±0.004 sec<sup>-1</sup>) and higher peak-to-peak amplitude (0.20±0.05 vs 0.14±0.03) compared to resting curves. Despite the extreme dynamic nature of contrast enhanced image sequences and respiratory motion, fast automated detection of myocardial segments and quantification of tissue contrast results in time curves with excellent noise levels, which reflect the expected effects of stress.
[show abstract][hide abstract] ABSTRACT: Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMRI) is the technique of choice to detect myocardial scars and assess myocardial viability. In clinical practice, this analysis is performed qualitatively or by manually tracing the enhanced area in each acquired slice. The purpose of this study was to test and validate a technique for automated localization and quantification of scar extent. CMRI data in patients with previous myocardial infarction were analyzed using custom software from which the myocardium was automatically identified from steady-state free precession images and registered on LGE-CMRI data. Scar tissue was defined as myocardium with signal intensity ≥ 80% of its maximum and quantified on each slice. Scar location and extent were assessed and compared with expert analysis. Preliminary results showed that automatic localization of scar from LGE-CMRI is feasible and scar quantification is accurate and reliable.