Comparison of hemodynamics and root configurations between remodeling and reimplantation methods for valve-sparing aortic root replacement: a pulsatile flow study.

PURPOSE: To compare the characteristics of reimplantation (RI) using grafts with sinuses and remodeling (RM) with/without external suture annuloplasty using a pulsatile flow simulator. METHODS: Porcine aortic roots were obtained from an abattoir, and six models of RM and RI with sinuses were prepared. External suture annuloplasty (ESA) was performed in the RM models to decrease the root diameter to 22 mm (RM-AP22) and 18 mm (RM-AP18). Valve models were tested at mean pulsatile flow and aortic pressure of 5.0 L/min and 120/80 (100) mmHg, respectively, at 70 beats/min. The forward flow, regurgitation, leakage, backflow rates, valve-closing time, and mean and peak pressure gradient (p-PG) were evaluated. Root configurations were examined using micro-computed tomography (micro-CT). RESULTS: The backflow rate was larger in the RM models than in the RI models (RI: 8.56% ± 0.38% vs. RM: 12.64% ± 0.79%; p < 0.01). The RM-AP and RI models were comparable in terms of the forward flow, regurgitation, backflow rates, p-PG, and valve-closing time. The analysis using a micro-CT showed a larger dilatation of the sinus of the Valsalva in the RM groups than in the RI group (Valsalva: RI, 26.55 ± 0.40 mm vs. RM-AP22, 31.22 ± 0.55 mm [p < 0.05]; RM-AP18, 31.05 ± 0.85 mm [p < 0.05]). CONCLUSIONS: RM with ESA and RI with neo-sinuses showed comparable hemodynamics. ESA to RM reduced regurgitation.

Artificial intelligence-based technology to make a three-dimensional pelvic model for preoperative simulation of rectal cancer surgery using MRI.

Aim: A new technique that allows visualization of whole pelvic organs with high accuracy and usability is needed for preoperative simulation in advanced rectal cancer surgery. In this study, we developed an automated algorithm to create a three-dimensional (3D) model from pelvic MRI using artificial intelligence (AI) technology. Methods: This study included a total of 143 patients who underwent 3D MRI in a preoperative examination for rectal cancer. The training dataset included 133 patients, in which ground truth labels were created for pelvic vessels, nerves, and bone. A 3D variant of U-net was used for the network architecture. Ten patients who underwent lateral lymph node dissection were used as a validation dataset. The correctness of the vascular labelling was assessed for pelvic vessels and the Dice similarity coefficients calculated for pelvic bone. Results: An automatic segmentation algorithm that extracts the artery, vein, nerve, and pelvic bone was developed, automatically producing a 3D image of the entire pelvis. The total time needed for segmentation was 133 seconds. The success rate of the AI-based segmentation was 100% for the common and external iliac vessels, but the rates for the vesical vein (75%), superior gluteal vein (60%), or accessory obturator vein (63%) were suboptimal. Regarding pelvic bone, the average Dice similarity coefficient between manual and automatic segmentation was 0.97 (standard deviation 0.0043). Conclusion: Though there is room to improve the segmentation accuracy, the algorithm developed in this study can be utilized for surgical simulation in the treatment of advanced rectal cancer.

Artificial intelligence-based technology for semi-automated segmentation of rectal cancer using high-resolution MRI.

AIM: Although MRI has a substantial role in directing treatment decisions for locally advanced rectal cancer, precise interpretation of the findings is not necessarily available at every institution. In this study, we aimed to develop artificial intelligence-based software for the segmentation of rectal cancer that can be used for staging to optimize treatment strategy and for preoperative surgical simulation. METHOD: Images from a total of 201 patients who underwent preoperative MRI were analyzed for training data. The resected specimen was processed in a circular shape in 103 cases. Using these datasets, ground-truth labels were prepared by annotating MR images with ground-truth segmentation labels of tumor area based on pathologically confirmed lesions. In addition, the areas of rectum and mesorectum were also labeled. An automatic segmentation algorithm was developed using a U-net deep neural network. RESULTS: The developed algorithm could estimate the area of the tumor, rectum, and mesorectum. The Dice similarity coefficients between manual and automatic segmentation were 0.727, 0.930, and 0.917 for tumor, rectum, and mesorectum, respectively. The T2/T3 diagnostic sensitivity, specificity, and overall accuracy were 0.773, 0.768, and 0.771, respectively. CONCLUSION: This algorithm can provide objective analysis of MR images at any institution, and aid risk stratification in rectal cancer and the tailoring of individual treatments. Moreover, it can be used for surgical simulations.

Aortic root geometry following valve-sparing root replacement with reimplantation or remodeling: experimental investigation under static continuous pressure.

The differences in aortic root geometry associated with various valve-sparing root replacement (VSRR) techniques have not fully been understood. We evaluated the root configuration of current VSRR techniques by developing in vitro test apparatus. Six fresh porcine hearts were used for each model. The aortic root remodeling control group involved replacement of the ascending aorta with diameter reduction of sino-tubular junction (STJ) (C1). The aortic valve reimplantation control group involved replacement of the ascending aorta alone (C2). VSRR included remodeling without (RM) or with annuloplasty (RM + A) and reimplantation with a tube (RI) or a handmade neo-Valsalva graft (RI + V). The root geometry of each model in response to closing hydraulic pressures of 80 and 120 mmHg was investigated using echocardiography. Among the VSRR models, RM yielded the largest aorto-ventricular junction (AVJ), which was similar to those in non-VSRR models [mean AVJ diameter (mm) at 80 mmHg; RM = 25.1 ± 1.5, RM + A = 20.9 ± 0.7, RI = 20.7 ± 0.9, RI + V = 20.8 ± 0.4]. RI + V yielded the largest Valsalva size and largest ratio of Valsalva/AVJ, which was similar to the control group [mean Valsalva diameter (mm) at 80 mmHg; RM = 28.4 ± 1.4, RM + A = 25.8 ± 1.3, RI = 23.6 ± 1.0, RI + V = 30.5 ± 0.8, ratio of Valsalva/AVJ at 80 mmHg; RM = 1.14 ± 0.06, RM + A = 1.24 ± 0.06, RI = 1.15 ± 0.06, RI + V = 1.47 ± 0.05]. The STJ diameter at 80 mmHg was numerically smaller with RM + A (22.4 ± 1.2 mm) than with RM (24.8 ± 2.3 mm, p = 0.11). There were no significant differences in AVJ, Valsalva, or STJ distensibility or ellipticity between procedures. Current modifications, including annuloplasty for remodeling or reimplantation in the setting of neo-Valsalva graft, yield near-physiological root geometries.

Underfilled Balloon-Expandable Transcatheter Aortic Valve Implantation With Ad Hoc Post-Dilation　- Pulsatile Flow Simulation Using a Patient-Specific Three-Dimensional Printing Model.

BACKGROUND: Underfilled transcatheter aortic-valve implantation with ad hoc post-dilation is a therapeutic option for patients with borderline annuli to avoid acute complication. The effects of this technique on valve leaflet behavior, hydrodynamic performances, and paravalvular leakage (PVL) using patient-specific three-dimensional (3D) aortic-valve models were investigated. Methods and Results: A female octogenarian patient was treated with this technique by using a 23-mm Sapien-XT. Patient-specific models were constructed from pre-procedure computed tomography (CT) data. Change in aortic annulus areas during systolic/diastolic phases and post-procedure stent areas were adjusted to those of the patient. The following was performed: (1) -3 cc initial and -2 cc underfilled post-dilation to the scale-down model by adjusting percent oversizing; and (2) -1 cc initial underfilling, nominal volume, and repeat nominal volume post-dilation using the patient-specific model. Underfilling was associated with higher %PVL. Observation using a high-speed camera revealed distorted leaflets after underfilled implantation, with a longer valve-closing time and smaller effective orifice areas, especially in the -3 cc underfilled implantation. Micro-CT analysis revealed that the transcatheter valves shifted to the opposite side of the large annulus calcification after post-dilation and reduced the malapposition there. CONCLUSIONS: Excessive underfilled implantation showed unacceptable acute hemodynamics. Abnormal leaflet motions after underfilled implantation raised concerns about durability. Flow simulations using patient-oriented 3D models could help to investigate hemodynamics, leaflet motions, and the PVL mechanism.

Quantitative assessment of paravalvular leakage after transcatheter aortic valve replacement using a patient-specific pulsatile flow model.

BACKGROUND: Quantitative assessment of post-transcatheter aortic valve replacement (TAVR) aortic regurgitation (AR) remains challenging. We developed patient-specific anatomical models with pulsatile flow circuit and investigated factors associated with AR after TAVR. METHODS: Based on pre-procedural computed tomography (CT) data of the six patients who underwent transfemoral TAVR using a 23-mm SAPIEN XT, anatomically and mechanically equivalent aortic valve models were developed. Forward flow and heart rate of each patient in two days after TAVR were duplicated under mean aortic pressure of 80mmHg. Paravalvular leakage (PVL) volume in basal and additional conditions was measured for each model using an electromagnetic flow sensor. Incompletely apposed tract between the transcatheter and aortic valves was examined using a micro-CT. RESULTS: PVL volume in each patient-specific model was consistent with each patient's PVL grade, and was affected by hemodynamic conditions. PVL and total regurgitation volume increased with the mean aortic pressure, whereas closing volume did not change. In contrast, closing volume increased proportionately with heart rate, but PVL did not change. The minimal cross-sectional gap had a positive correlation with the PVL volumes (r=0.89, P=0.02). The gap areas typically occurred in the vicinity of the bulky calcified nodules under the native commissure. CONCLUSIONS: PVL volume, which could be affected by hemodynamic conditions, was significantly associated with the minimal cross-sectional gap area between the aortic annulus and the stent frame. These data may improve our understanding of the mechanism of the occurrence of post-TAVR PVL.