Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms

Sjoerdsma, Marloes; Verstraeten, Sabine; Maas, Esther J.; Vosse, Frans N. van de; Sambeek, Marc R.H.M. van; Lopata, R.G.P.

doi:10.1016/j.ultrasmedbio.2022.09.005

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…This remains a challenge, with US quality being limited by the use of time-resolved data (lower spatial resolution), abdominal fat (less US signal reaching the AAA) and bowel gas (shadow artifacts). Other limitations of US could be overcome with new US techniques, such as multi-aperture US imaging, combining proximal and distal images to increase the field-of-view [23] or combining images from different angles to improve the visibility of the whole AAA circumference [24]. With improvement of the image quality, segmentation performance is expected to improve, also at the proximal and distal ends.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning

Maas,

Awasthi,

Van Pelt

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2D ultrasound to estimate their rupture risk. Recent studies have shown that 3-dimensional and mechanical AAA parameters might be better predictors for aneurysm growth and rupture than the diameter. These parameters can be obtained with time-resolved 3D ultrasound (3D+t US), which requires robust and automatic segmentation of AAAs from 3D+t US. This study proposes and validates a deep learning (DL) approach for automatic segmentation of AAAs. 500 AAA patients were included for follow-up 3D+t US imaging, resulting in 2495 3D+t US images. Segmentation masks for model training were obtained using a conventional automatic segmentation algorithm ('nonDL'). Four different DL models were trained and validated by (1) comparison to CT and (2) reader scoring. Performance of the nonDL and different DL segmentation strategies were evaluated by comparing Hausdorff distance, Dice scores, accuracy, sensitivity, and specificity with a sign test. All DL models had higher median Dice scores, accuracy, and sensitivity (all p < 0.003) compared to nonDL segmentation. The full image-resolution model without data augmentation showed the highest median Dice score and sensitivity (p < 0.001). Applying the DL model on an independent test group produced fewer poor segmentation scores of 1 to 2 on a five-point scale (8% for DL, 18% for nonDL). This demonstrates that a robust and automatic segmentation algorithm for segmenting abdominal aortic aneurysms from 3D+t US images was developed, showing improved performance compared to conventional segmentation.

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Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning

Maas,

Awasthi,

Van Pelt

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Despite this limited field of view, the shoulders of the aneurysm were visible in 126 out of 172 patients. This drawback of ultrasound could be overcome by making multiple proximal and distal 3D + t US images, and registering and fusing these images [ 40 ], leading to a larger part of the AAA being visible.…”

Section: Discussionmentioning

confidence: 99%

3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth

et al. 2023

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The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.

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“…In Petterson et al (2021) , this concept of multi-perspective ultrasound imaging, in which an ultrasound probe was physically translated over the abdomen, improved lateral contrast of the abdominal aorta in healthy volunteers. In a later study ( Sjoerdsma et al, 2023 ), 3D multi-perspective ultrasound imaging was also performed in patients with an abdominal aortic aneurysm. Also in obstetric ( Zimmer et al, 2023 ) and cardiac applications ( Rajpoot et al, 2011 ; Mulder et al, 2014 ), the use of multi-perspective imaging has shown to improve the visibility of the imaged structures.…”

Section: Introductionmentioning

confidence: 99%

In vivo bistatic dual-aperture ultrasound imaging and elastography of the abdominal aorta

van Hal,

de Hoop,

van Sambeek

et al. 2024

Front. Physiol.

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Introduction: In this paper we introduce in vivo multi-aperture ultrasound imaging and elastography of the abdominal aorta. Monitoring of the geometry and growth of abdominal aortic aneurysms (AAA) is paramount for risk stratification and intervention planning. However, such an assessment is limited by the lateral lumen-wall contrast and resolution of conventional ultrasound. Here, an in vivo dual-aperture bistatic imaging approach is shown to improve abdominal ultrasound and strain imaging quality significantly. By scanning the aorta from different directions, a larger part of the vessel circumference can be visualized.Methods: In this first-in-man volunteer study, the performance of multi-aperture ultrasound imaging and elastography of the abdominal aortic wall was assessed in 20 healthy volunteers. Dual-probe acquisition was performed in which two curved array transducers were aligned in the same imaging plane. The transducers alternately transmit and both probes receive simultaneously on each transmit event, which allows for the reconstruction of four ultrasound signals. Automatic probe localization was achieved by optimizing the coherence of the trans-probe data, using a gradient descent algorithm. Speckle-tracking was performed on the four individual bistatic signals, after which the respective axial displacements were compounded and strains were calculated.Results: Using bistatic multi-aperture ultrasound imaging, the image quality of the ultrasound images, i.e., the angular coverage of the wall, was improved which enables accurate estimation of local motion dynamics and strain in the abdominal aortic wall. The motion tracking error was reduced from 1.3 mm ± 0.63 mm to 0.16 mm ± 0.076 mm, which increased the circumferential elastographic signal-to-noise ratio (SNRe) by 12.3 dB ± 8.3 dB on average, revealing more accurate and homogeneous strain estimates compared to single-perspective ultrasound.Conclusion: Multi-aperture ultrasound imaging and elastography is feasible in vivo and can provide the clinician with vital information about the anatomical and mechanical state of AAAs in the future.

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Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms

Cited by 9 publications

References 45 publications

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning

3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth

In vivo bistatic dual-aperture ultrasound imaging and elastography of the abdominal aorta

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