Motion correction for myocardial T1 mapping using image registration with synthetic image estimation.

Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and nonischemic cardiomyopathies. Image acquisition using the modified Look-Locker inversion recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this article, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust nonrigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map.

Computer-aided scoring of erb-b2 receptor tyrosine kinase 2 (HER2) gene amplification status in breast cancer.

Background: Identification of HER2 protein overexpression and/or amplification of the HER2 gene are required to qualify breast cancer patients for HER2 targeted therapies. In situ hybridization (ISH) assays that identify HER2 gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of HER2 genes and copies of chromosome 17 to determine HER2 amplification. Methods: To assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of HER2 gene status in breast cancer specimens. The objective of this study was to compare uPath HER2 DISH image analysis vs manual read scoring of VENTANA HER2 DISH-stained breast carcinoma specimens with ground truth (GT) gene status as the reference. Three reader pathologists reviewed 220, formalin-fixed, paraffin-embedded (FFPE) breast cancer cases by both manual and uPath HER2 DISH IA methods. Scoring results from manual read (MR) and computer-assisted scores (image analysis, IA) were compared against the GT gene status generated by consensus of a panel of pathologists. The differences in agreement rates of HER2 gene status between manual, computer-assisted, and GT gene status were determined. Results: The positive percent agreement (PPA) and negative percent agreement (NPA) rates for image analysis (IA) vs GT were 97.2% (95% confidence interval [CI]: 95.0, 99.3) and 94.3% (95% CI: 90.8, 97.3) respectively. Comparison of agreement rates showed that the lower bounds of the 95% CIs for the difference of PPA and NPA for IA vs MR were -0.9% and -6.2%, respectively. Further, inter- and intra-reader agreement rates in the IA method were observed with point estimates of at least 96.7%. Conclusions: Overall, our data show that the uPath HER2 DISH IA is non-inferior to manual scoring and supports its use as an aid for pathologists in routine diagnosis of breast cancer.

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in RAS Wild-Type Metastatic Colorectal Cancer.

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice due to issues with analytic precision and practicality. We investigated whether AREG/EREG IHC could predict benefit from the anti-EGFR agent panitumumab. EXPERIMENTAL DESIGN: Artificial intelligence algorithms were developed to assess AREG/EREG IHC in 274 patients from the PICCOLO trial of irinotecan with or without panitumumab (Ir vs. IrPan) in RAS wild-type mCRC. The primary endpoint was progression-free survival (PFS). Secondary endpoints were RECIST response rate (RR) and overall survival (OS). Models were repeated adjusting separately for BRAF mutation status and primary tumor location (PTL). RESULTS: High ligand expression was associated with significant PFS benefit from IrPan compared with Ir [8.0 vs. 3.2 months; HR, 0.54; 95% confidence interval (CI), 0.37-0.79; P = 0.001]; whereas low ligand expression was not (3.4 vs. 4.4 months; HR, 1.05; 95% CI, 0.74-1.49; P = 0.78). The ligand-treatment interaction was significant (P interaction = 0.02) and remained significant after adjustment for BRAF-mutation status and PTL. Likewise, RECIST RR was significantly improved in patients with high ligand expression (IrPan vs. Ir: 48% vs. 6%; P < 0.0001) but not those with low ligand expression (25% vs. 14%; P = 0.10; P interaction = 0.01). The effect on OS was similar but not statistically significant. CONCLUSIONS: AREG/EREG IHC identified patients who benefitted from the addition of panitumumab to irinotecan chemotherapy. IHC is a practicable assay that may be of use in routine practice.

Mitral annular velocity measurement with cardiac magnetic resonance imaging using a novel annular tracking algorithm: Validation against echocardiography.

BACKGROUND: Doppler based mitral annular velocities are an integral part of echocardiographic left ventricular diastolic function assessment. Although these measurements can be obtained by phase contrast cardiac magnetic resonance imaging (PC-CMR), this approach has limitations. The aims of this study were to assess the accuracy and reproducibility of a high temporal resolution steady-state free precession (SSFP) cine acquisition coupled with semi-automated mitral annular tracking to measure tissue velocity, and compare to echocardiography as the reference method. METHODS: High temporal resolution (17 ms) 4-chamber cines were acquired in 25 volunteers using retrospective and prospective gating on a 3.0 T magnet. Mitral annular early (e') and late (a') tissue velocities were derived using a novel algorithm to semi-automatically detect the mitral valve insertion points and track its motion. Additionally, PC-CMR was used to measure mitral inflow early diastolic (E) velocity. Those measurements were also obtained using echocardiography based pulsed and tissue Doppler techniques, on the same day. RESULTS: Subjects were on average 34 ±â€¯14 years-old (48% male). The lateral annulus e' measurements had the best agreement with echocardiography with a concordance correlation coefficient (CCC) of 0.76 and 0.75 for prospectively and retrospectively gated cine CMR respectively. There was no significant difference in the lateral annular tissue velocities between echocardiography (13.8 ±â€¯3.7 cm/s) and prospective (13.4 ±â€¯3.7 cm/s) or retrospective (14.0 ±â€¯3.7) acquisitions. Similarly, CMR measurement of E/e' (a surrogate marker for LV filling pressures) using the lateral e' velocity showed moderate agreement with echocardiography (CCC of 0.56 and 0.51 for prospective and retrospective acquisitions respectively) without a significant difference in ratios (5.3 ±â€¯1.6 and 5.0 ±â€¯1.3) compared to echocardiography (5.2 ±â€¯1.4). Intra- and inter-observer reproducibility of the CMR-based annular velocity measurements was good. CONCLUSION: Measurements of mitral annular tissue velocities can be obtained from SSFP 4-chamber cine images using a semi-automated annular tracking algorithm, and demonstrates moderate agreement with echocardiography. The semi-automated method can provide quantitative mitral annular velocity measurements directly from conventional cine images, thereby providing additional clinically relevant information. The accuracy of this method in patients with diastolic dysfunction remains to be determined.

A non-invasive assessment of cardiopulmonary hemodynamics with MRI in pulmonary hypertension.

PURPOSE: We propose a method for non-invasive quantification of hemodynamic changes in the pulmonary arteries resulting from pulmonary hypertension (PH). METHODS: Using a two-element Windkessel model, and input parameters derived from standard MRI evaluation of flow, cardiac function and valvular motion, we derive: pulmonary artery compliance (C), mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR), pulmonary capillary wedge pressure (PCWP), time-averaged intra-pulmonary pressure waveforms and pulmonary artery pressures (systolic (sPAP) and diastolic (dPAP)). MRI results were compared directly to reference standard values from right heart catheterization (RHC) obtained in a series of patients with suspected pulmonary hypertension (PH). RESULTS: In 7 patients with suspected PH undergoing RHC, MRI and echocardiography, there was no statistically significant difference (p<0.05) between parameters measured by MRI and RHC. Using standard clinical cutoffs to define PH (mPAP>25mmHg), MRI was able to correctly identify all patients as having pulmonary hypertension, and to correctly distinguish between pulmonary arterial (mPAP>25mmHg, PCWP<15mmHg) and venous hypertension (mPAP>25mmHg, PCWP>15mmHg) in 5 of 7 cases. CONCLUSIONS: We have developed a mathematical model capable of quantifying physiological parameters that reflect the severity of PH.

Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo.

The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.

Motion compensated magnetic resonance reconstruction using inverse-consistent deformable registration: application to real-time cine imaging.

Patient motion is a major limitation for magnetic resonance imaging. Recent theoretical advances incorporate explicit rigid and non-rigid motion compensation into conventional image reconstruction for multi-shot acquisitions and recover motion-free images by solving a general matrix inversion problem. Although the theory has been established, applications are rare due to the challenges of estimating motion field for every pixel of every shot. In this paper we propose a method to overcome this difficulty using the inverse-consistent deformable registration supplying both forward and backward deformations for matrix inversion. We further extend this framework for multi-coil motion compensated image reconstruction using the eigen-mode analysis. Both simulations and in vivo studies demonstrate the effectiveness of our approach.

Registration of cardiac SPECT/CT data through weighted intensity co-occurrence priors.

The introduction of hybrid scanners has greatly increased the popularity of molecular imaging techniques. Many clinical applications benefit from combining complementary information based on the precise alignment of the two modalities. In case the alignment is inaccurate, then this crucial assumption often made for subsequent processing steps will be violated. However, this violation may not be apparent to the physician. In CT-based attenuation correction (AC) for cardiac SPECT/CT data, critical misalignments between SPECT and CT can lead to spurious perfusion defects. In this work, we focus on increasing the accuracy of rigid volume registration of cardiac SPECT/CT data by using prior knowledge. A new weighting scheme for an intensity co-occurrence prior is introduced to assure accurate and robust alignment in the local heart region. Experimental results demonstrate that the proposed method out-performs mutual information registration and shows robustness across a selection of learned distributions acquired from 15 different patients.

Learning based non-rigid multi-modal image registration using Kullback-Leibler divergence.

The need for non-rigid multi-modal registration is becoming increasingly common for many clinical applications. To date, however, existing proposed techniques remain as largely academic research effort with very few methods being validated for clinical product use. It has been suggested by Crum et al. that the context-free nature of these methods is one of the main limitations and that moving towards context-specific methods by incorporating prior knowledge of the underlying registration problem is necessary to achieve registration results that are accurate and robust enough for clinical applications. In this paper, we propose a novel non-rigid multi-modal registration method using a variational formulation that incorporates a prior learned joint intensity distribution. The registration is achieved by simultaneously minimizing the Kullback-Leibler divergence between an observed and a learned joint intensity distribution and maximizing the mutual information between reference and alignment images. We have applied our proposed method on both synthetic and real images with encouraging results.