Noel C.F. Codella

Respiratory and cardiac self-gated free-breathing cardiac CINE imaging with multiecho 3D hybrid radial SSFP acquisition

A respiratory and cardiac self‐gated free‐breathing three‐dimensional cine steady‐state free precession imaging method using multiecho hybrid radial sampling is presented. Cartesian mapping of the k‐space center along the slice encoding direction provides intensity‐weighted position information, from which both respiratory and cardiac motions are derived. With in plan radial sampling acquired at every pulse repetition time, no extra scan time is required for sampling the k‐space center. Temporal filtering based on density compensation is used for radial reconstruction to achieve high signal‐to‐noise ratio and contrast‐to‐noise ratio. High correlation between the self‐gating signals and external gating signals is demonstrated. This respiratory and cardiac self‐gated, free‐breathing, three‐dimensional, radial cardiac cine imaging technique provides image quality comparable to that acquired with the multiple breath‐hold two‐dimensional Cartesian steady‐state free precession technique in short‐axis, four‐chamber, and two‐chamber orientations. Functional measurements from the three‐dimensional cardiac short axis cine images are found to be comparable to those obtained using the standard two‐dimensional technique. Magn Reson Med 63:1230–1237, 2010.

Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI

An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 ± 6.2 mL (mean ± standard deviation) and -0.9 ± 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 ± 6.9 mL and -1.0 ± 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.

Effects of papillary muscles and trabeculae on left ventricular quantification: increased impact of methodological variability in patients with left ventricular hypertrophy.

Background Accurate quantification of left ventricular mass and ejection fraction is important for patients with left ventricular hypertrophy. Although cardiac magnetic resonance imaging has been proposed as a standard for these indices, prior studies have variably included papillary muscles and trabeculae in myocardial volume. This study investigated the contribution of papillary muscles and trabeculae to left ventricular quantification in relation to the presence and pattern of hypertrophy. Methods Cardiac magnetic resonance quantification was performed on patients with concentric or eccentric hypertrophy and normal controls (20 per group) using two established methods that included papillary muscles and trabeculae in myocardium (method 1) or intracavitary (method 2) volumes. Results Among all patients, papillary muscles and trabeculae accounted for 10.5% of ventricular mass, with greater contribution with left ventricular hypertrophy than normals (12.6 vs. 6.2%, P < 0.001). Papillary muscles and trabeculae mass correlated with ventricular wall mass (r = 0.53) and end-diastolic volume (r = 0.52; P < 0.001). Papillary muscles and trabeculae inclusion in myocardium (method 1) yielded smaller differences with a standard of mass quantification from linear ventricular measurements than did method 2 (P < 0.001). Method 1 in comparison with method 2 yielded differences in left ventricular mass, ejection fraction and volume in all groups, especially in patients with hypertrophy: the difference in ventricular mass index was three-fold to six-fold greater in hypertrophy than normal groups (P < 0.001). Difference in ejection fraction, greatest in concentric hypertrophy (P < 0.001), was independently related to papillary muscles and trabeculae mass, ventricular wall mass, and smaller ventricular volume (R2 = 0.56, P < 0.001). Conclusion Established cardiac magnetic resonance methods yield differences in left ventricular quantification due to variable exclusion of papillary muscles and trabeculae from myocardium. The relative impact of papillary muscles and trabeculae exclusion on calculated mass and ejection fraction is increased among patients with hypertrophy-associated left ventricular remodeling.

Left Ventricle: Automated Segmentation by Using Myocardial Effusion Threshold Reduction and Intravoxel Computation at MR Imaging

UNLABELLEDnThis retrospective analysis of existing patient data had institutional review board approval and was performed in compliance with HIPAA. No informed consent was required. The purpose of the study was to develop and validate an algorithm for automated segmentation of the left ventricular (LV) cavity that accounts for papillary and/or trabecular muscles and partial voxels in cine magnetic resonance (MR) images, an algorithm called LV Myocardial Effusion Threshold Reduction with Intravoxel Computation (LV-METRIC). The algorithm was validated in biologic phantoms, and its results were compared with those of manual tracing, as well as those of a commercial automated segmentation software (MASS [MR Analytical Software System]), in 38 subjects. LV-METRIC accuracy in vitro was 98.7%. Among the 38 subjects studied, LV-METRIC and MASS ejection fraction estimations were highly correlated with manual tracing (R(2) = 0.97 and R(2) = 0.95, respectively). Ventricular volume estimations were smaller with LV-METRIC and larger with MASS than those calculated by using manual tracing, though all results were well correlated (R(2) = 0.99). LV-METRIC volume measurements without partial voxel interpolation were statistically equivalent to manual tracing results (P > .05). LV-METRIC had reduced intraobserver and interobserver variability compared with other methods. MASS required additional manual intervention in 58% of cases, whereas LV-METRIC required no additional corrections. LV-METRIC reliably and reproducibly measured LV volumes.nnnSUPPLEMENTAL MATERIALnhttp://radiology.rsnajnls.org/cgi/content/full/248/3/1004/DC1.

Automated Segmentation of Routine Clinical Cardiac Magnetic Resonance Imaging for Assessment of Left Ventricular Diastolic Dysfunction

Background—Cardiac magnetic resonance (CMR) is established for assessment of left ventricular (LV) systolic function but has not been widely used to assess diastolic function. This study tested performance of a novel CMR segmentation algorithm (LV-METRIC) for automated assessment of diastolic function. Methods and Results—A total of 101 patients with normal LV systolic function underwent CMR and echocardiography (echo) within 7 days. LV-METRIC generated LV filling profiles via automated segmentation of contiguous short-axis images (204±39 images, 2:04±0:53 minutes). Diastolic function by CMR was assessed via early:atrial filling ratios, peak diastolic filling rate, time to peak filling rate, and a novel index—diastolic volume recovery (DVR), calculated as percent diastole required for recovery of 80% stroke volume. Using an echo standard, patients with versus without diastolic dysfunction had lower early:atrial filling ratios, longer time to peak filling rate, lower stroke volume–adjusted peak diastolic filling rate, and greater DVR (all P<0.05). Prevalence of abnormal CMR filling indices increased in relation to clinical symptoms classified by New York Heart Association functional class (P=0.04) or dyspnea (P=0.006). Among all parameters tested, DVR yielded optimal performance versus echo (area under the curve: 0.87±0.04, P<0.001). Using a 90% specificity cutoff, DVR yielded 74% sensitivity for diastolic dysfunction. In multivariate analysis, DVR (odds ratio, 1.82; 95% CI, 1.13 to 2.57; P=0.02) was independently associated with echo-evidenced diastolic dysfunction after controlling for age, hypertension, and LV mass (&khgr;2=73.4, P<0.001). Conclusions—Automated CMR segmentation can provide LV filling profiles that may offer insight into diastolic dysfunction. Patients with diastolic dysfunction have prolonged diastolic filling intervals, which are associated with echo-evidenced diastolic dysfunction independent of clinical and imaging variables.

Rapid and accurate left ventricular chamber quantification using a novel CMR segmentation algorithm: A clinical validation study†

To evaluate the clinical performance of a novel automated left ventricle (LV) segmentation algorithm (LV‐METRIC) that involves no geometric assumptions.

Impact of diastolic dysfunction severity on global left ventricular volumetric filling - assessment by automated segmentation of routine cine cardiovascular magnetic resonance

ObjectivesTo examine relationships between severity of echocardiography (echo) -evidenced diastolic dysfunction (DD) and volumetric filling by automated processing of routine cine cardiovascular magnetic resonance (CMR).BackgroundCine-CMR provides high-resolution assessment of left ventricular (LV) chamber volumes. Automated segmentation (LV-METRIC) yields LV filling curves by segmenting all short-axis images across all temporal phases. This study used cine-CMR to assess filling changes that occur with progressive DD.Methods115 post-MI patients underwent CMR and echo within 1 day. LV-METRIC yielded multiple diastolic indices - E:A ratio, peak filling rate (PFR), time to peak filling rate (TPFR), and diastolic volume recovery (DVR80 - proportion of diastole required to recover 80% stroke volume). Echo was the reference for DD.ResultsLV-METRIC successfully generated LV filling curves in all patients. CMR indices were reproducible (≤ 1% inter-reader differences) and required minimal processing time (175 ± 34 images/exam, 2:09 ± 0:51 minutes). CMR E:A ratio decreased with grade 1 and increased with grades 2-3 DD. Diastolic filling intervals, measured by DVR80 or TPFR, prolonged with grade 1 and shortened with grade 3 DD, paralleling echo deceleration time (p < 0.001). PFR by CMR increased with DD grade, similar to E/e (p < 0.001). Prolonged DVR80 identified 71% of patients with echo-evidenced grade 1 but no patients with grade 3 DD, and stroke-volume adjusted PFR identified 67% with grade 3 but none with grade 1 DD (matched specificity = 83%). The combination of DVR80 and PFR identified 53% of patients with grade 2 DD. Prolonged DVR80 was associated with grade 1 (OR 2.79, CI 1.65-4.05, p = 0.001) with a similar trend for grade 2 (OR 1.35, CI 0.98-1.74, p = 0.06), whereas high PFR was associated with grade 3 (OR 1.14, CI 1.02-1.25, p = 0.02) DD.ConclusionsAutomated cine-CMR segmentation can discern LV filling changes that occur with increasing severity of echo-evidenced DD. Impaired relaxation is associated with prolonged filling intervals whereas restrictive filling is characterized by increased filling rates.

Improved left ventricular mass quantification with partial voxel interpolation: in vivo and necropsy validation of a novel cardiac MRI segmentation algorithm.

Background— Cardiac magnetic resonance (CMR) typically quantifies LV mass (LVM) by means of manual planimetry (MP), but this approach is time-consuming and does not account for partial voxel components— myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM.nnMethods and Results— LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (ASPV) and full voxel (ASFV) measurements. Methods were independently compared with LVM quantified on echocardiography (echo) and an ex vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time versus MP (0:21±0:04 versus 4:18±1:02 minutes; P <0.001). ASFV mass (136±35 g) was slightly lower than MP (139±35; Δ=3±9 g, P <0.001). Both methods yielded similar proportions of patients with LV remodeling ( P =0.73) and hypertrophy ( P =1.00). Regarding partial voxel segmentation, ASPV yielded higher LVM (159±38 g) than MP (Δ=20±10 g) and ASFV (Δ=23±6 g, both P <0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between ASPV and ASFV correlated with larger voxel size (partial r =0.37, P <0.001) even after controlling for LV chamber volume ( r =0.28, P =0.002) and total LVM ( r =0.19, P =0.03). Among patients, ASPV yielded better agreement with echo (Δ=20±25 g) than did ASFV (Δ=43±24 g) or MP (Δ=40±22 g, both P <0.001). Among laboratory animals, ASPV and ex vivo results were similar (Δ=1±3 g, P =0.3), whereas ASFV (6±3 g, P <0.001) and MP (4±5 g, P =0.02) yielded small but significant differences with LVM at necropsy.nnConclusions— Automated segmentation of myocardial partial voxels yields a 14–17% increase in LVM versus full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.Background— Cardiac magnetic resonance (CMR) typically quantifies LV mass (LVM) by means of manual planimetry (MP), but this approach is time-consuming and does not account for partial voxel components— myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. Methods and Results— LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (ASPV) and full voxel (ASFV) measurements. Methods were independently compared with LVM quantified on echocardiography (echo) and an ex vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time versus MP (0:21±0:04 versus 4:18±1:02 minutes; P<0.001). ASFV mass (136±35 g) was slightly lower than MP (139±35; &Dgr;=3±9 g, P<0.001). Both methods yielded similar proportions of patients with LV remodeling (P=0.73) and hypertrophy (P=1.00). Regarding partial voxel segmentation, ASPV yielded higher LVM (159±38 g) than MP (&Dgr;=20±10 g) and ASFV (&Dgr;=23±6 g, both P<0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between ASPV and ASFV correlated with larger voxel size (partial r=0.37, P<0.001) even after controlling for LV chamber volume (r=0.28, P=0.002) and total LVM (r=0.19, P=0.03). Among patients, ASPV yielded better agreement with echo (&Dgr;=20±25 g) than did ASFV (&Dgr;=43±24 g) or MP (&Dgr;=40±22 g, both P<0.001). Among laboratory animals, ASPV and ex vivo results were similar (&Dgr;=1±3 g, P=0.3), whereas ASFV (6±3 g, P<0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy. Conclusions— Automated segmentation of myocardial partial voxels yields a 14–17% increase in LVM versus full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.

Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST). An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within −1.6 ± 8.7u2009mL, −1.4 ± 7.8u2009mL, and 1.0 ± 5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle.

Left ventricle segmentation using graph searching on intensity and gradient and a priori knowledge (lvGIGA) for short‐axis cardiac magnetic resonance imaging

To develop and evaluate an automated left ventricle (LV) segmentation algorithm using Graph searching based on Intensity and Gradient information and A priori knowledge (lvGIGA).