Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment.

PURPOSE: Joint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging. METHODS: The proposed algorithm first localizes the left ventricle using a U-Net architecture. The localized left cavity centroid is extracted and a squared region of interest ("focus box") is created around the resulting pixel. The focus box is then propagated on each image and the sum of the pixel intensity inside is computed. The smallest sum corresponds to the image with the lowest intensity signal within the blood pool and healthy myocardium, which will provide an ideal scar-to-blood contrast. The image's corresponding TI is considered optimal. The U-Net was trained to segment the epicardium in 177 patients with binary cross-entropy loss. The algorithm was validated retrospectively in 152 patients, and the agreement between the algorithm and two magnetic resonance (MR) operators' prediction of TI values was calculated using the Fleiss' kappa coefficient. Thirty focus box sizes, ranging from 2.3mm2 to 20.3cm2, were tested. Processing times were measured. RESULTS: The U-Net's Dice score was 93.0 ± 0.1%. The proposed algorithm extracted TI values in 2.7 ± 0.1 s per patient (vs. 16.0 ± 8.5 s for the operator). An agreement between the algorithm's prediction and the MR operators' prediction was found in 137/152 patients (κ= 0.89), for an optimal focus box of size 2.3cm2. CONCLUSION: The proposed fully-automated algorithm has potential of reducing uncertainties, variability, and workload inherent to manual approaches with promise for future clinical implementation for joint bright- and black-blood MRI.

Pulse arrival time variation as a non-invasive marker of acute response to cardiac resynchronization therapy.

AIMS: Successful cardiac resynchronization therapy (CRT) shortens the pre-ejection period (PEP) which is prolonged in the left bundle branch block (LBBB). In a combined animal and patient study, we investigated if changes in the pulse arrival time (PAT) could be used to measure acute changes in PEP during CRT implantation and hence be used to evaluate acute CRT response non-invasively and in real time. METHODS AND RESULTS: In six canines, a pulse transducer was attached to a lower limb and PAT was measured together with left ventricular (LV) pressure by micromanometer at baseline, after induction of LBBB and during biventricular pacing. Time-to-peak LV dP/dt (Td) was used as a surrogate for PEP. In twelve LBBB patients during implantation of CRT, LV and femoral pressures were measured at baseline and during five different pacing configurations. PAT increased from baseline (277 ± 9 ms) to LBBB (313 ± 16 ms, P < 0.05) and shortened with biventricular pacing (290 ± 16 ms, P < 0.05) in animals. There was a strong relationship between changes in PAT and Td in patients (r2 = 0.91). Two patients were classified as non-responders at 6 months follow-up. CRT decreased PAT from 320 ± 41 to 298 ± 39 ms (P < 0.05) in the responders, while PAT increased by 5 and 8 ms in the two non-responders. CONCLUSION: This proof-of-concept study indicates that PAT can be used as a simple, non-invasive method to assess the acute effects of CRT in real time with the potential to identify long-term response in patients.

Tracking Early Systolic Motion for Assessing Acute Response to Cardiac Resynchronization Therapy in Real Time.

An abnormal systolic motion is frequently observed in patients with left bundle branch block (LBBB), and it has been proposed as a predictor of response to cardiac resynchronization therapy (CRT). Our goal was to investigate if this motion can be monitored with miniaturized sensors feasible for clinical use to identify response to CRT in real time. Motion sensors were attached to the septum and the left ventricular (LV) lateral wall of eighteen anesthetized dogs. Recordings were performed during baseline, after induction of LBBB, and during biventricular pacing. The abnormal contraction pattern in LBBB was quantified by the septal flash index (SFI) equal to the early systolic shortening of the LV septal-to-lateral wall diameter divided by the maximum shortening achieved during ejection. In baseline, with normal electrical activation, there was limited early-systolic shortening and SFI was low (9 ± 8%). After induction of LBBB, this shortening and the SFI significantly increased (88 ± 34%, p < 0.001). Subsequently, CRT reduced it approximately back to baseline values (13 ± 13%, p < 0.001 vs. LBBB). The study showed the feasibility of using miniaturized sensors for continuous monitoring of the abnormal systolic motion of the LV in LBBB and how such sensors can be used to assess response to pacing in real time to guide CRT implantation.

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer.

BACKGROUND: Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. METHOD: A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. RESULT: The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. CONCLUSION: Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.

Shortening of time-to-peak left ventricular pressure rise (Td) in cardiac resynchronization therapy.

AIMS: We tested the hypothesis that shortening of time-to-peak left ventricular pressure rise (Td) reflect resynchronization in an animal model and that Td measured in patients will be helpful to identify long-term volumetric responders [end-systolic volume (ESV) decrease >15%] in cardiac resynchronization therapy (CRT). METHODS: Td was analysed in an animal study (n = 12) of left bundle-branch block (LBBB) with extensive instrumentation to detect left ventricular myocardial deformation, electrical activation, and pressures during pacing. The sum of electrical delays from the onset of pacing to four intracardiac electrodes formed a synchronicity index (SI). Pacing was performed at baseline, with LBBB, right and left ventricular pacing and finally with biventricular pacing (BIVP). We then studied Td at baseline and with BIVP in a clinical observational study in 45 patients during the implantation of CRT and followed up for up to 88 months. RESULTS: We found a strong relationship between Td and SI in the animals (R = 0.84, P < 0.01). Td and SI increased from narrow QRS at baseline (Td = 95 ± 2 ms, SI = 141 ± 8 ms) to LBBB (Td = 125 ± 2 ms, SI = 247 ± 9 ms, P < 0.01), and shortened with biventricular pacing (BIVP) (Td = 113 ± 2 ms and SI = 192 ± 7 ms, P < 0.01). Prolongation of Td was associated with more wasted deformation during the preejection period (R = 0.77, P < 0.01). Six patients increased ESV by 2.5 ± 18%, while 37 responders (85%) had a mean ESV decrease of 40 ± 15% after more than 6 months of follow-up. Responders presented with a higher Td at baseline than non-responders (163 ± 26 ms vs. 121 ± 19 ms, P < 0.01). Td decreased to 156 ± 16 ms (P = 0.02) with CRT in responders, while in non-responders, Td increased to 148 ± 21 ms (P < 0.01). A decrease in Td with BIVP to values similar or below what was found at baseline accurately identified responders to therapy (AUC 0.98, P < 0.01). Td at baseline and change in Td from baseline was linear related to the decrease in ESV at follow-up. All-cause mortality was high among six non-responders (n = 4), while no patients died in the responder group during follow-up. CONCLUSIONS: Prolongation of Td is associated with cardiac dyssynchrony and more wasted deformation during the preejection period. Shortening of a prolonged Td with CRT in patients accurately identifies volumetric responders to CRT with incremental value on top of current guidelines and practices. Thus, Td carries the potential to become a biomarker to predict long-term volumetric response in CRT candidates.

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

Measurements of the left ventricular (LV) pressure trace are rarely performed despite high clinical interest. We estimated the LV pressure trace for an individual heart by scaling the isovolumic, ejection and filling phases of a normalized, averaged LV pressure trace to the time-points of opening and closing of the aortic and mitral valves detected in the individual heart. We developed a signal processing algorithm that automatically detected the time-points of these valve events from the motion signal of a miniaturized accelerometer attached to the heart surface. Furthermore, the pressure trace was used in combination with measured displacement from the accelerometer to calculate the pressure-displacement loop area. The method was tested on data from 34 animals during different interventions. The accuracy of the accelerometer-detected valve events was very good with a median difference of 2 ms compared to valve events defined from hemodynamic reference recordings acquired simultaneously with the accelerometer. The average correlation coefficient between the estimated and measured LV pressure traces was r = 0.98. Finally, the LV pressure-displacement loop areas calculated using the estimated and measured pressure traces showed very good correlation (r = 0.98). Hence, the pressure-displacement loop area can be assessed solely from accelerometer recordings with very good accuracy.

Monitoring cardiac function by accelerometer - detecting start systole from the acceleration signal makes additional ECG recordings for R-peak detection redundant.

A miniaturized accelerometer attached to the heart has been used for monitoring functional parameters such as early systolic velocity and displacement. Currently, processing of the accelerometer signal for derival of these functional parameters depends on determining start systole by detecting the ECG R-peaks. This study proposes an alternative method using only the accelerometer signal to detect start systole, making additional ECG recordings for this purpose redundant. A signal processing method for automatic detection of start systole by accelerometer alone was developed and compared with detected R-peaks in 15 pigs during 5 different interventions showing a difference of 30 ± 17 ms. Furthermore, the derived early systolic velocity and displacement using only accelerometer measurements correlated well (r2=0.91 and 0.82, respectively) with minor differences compared to the current method using ECG R-peaks as time reference. The results show that an accelerometer can be used to monitor cardiac function without the need to measure ECG which can simplify the monitoring system.