Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy

Weishaupt, L.; Sayed, Hisham; Mao, Ximeng; Choo, Richard; Stish, Bradley J.; Enger, Shirin A.; Deufel, Christopher L.

doi:10.1016/j.brachy.2022.02.005

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The Michelson contrast (also known as visibility), C, was measured on B‐mode and C‐mode imaging of needles in phantom using Equation (1):

\begin{equation*}C\ = \ \frac{{{{S}_{needle}} - \ {{S}_{background}}}}{{{{S}_{needle}} + \ {{S}_{background}}}}\end{equation*}

where

{{S}_{needle}}

is the signal along the needle and

{{S}_{background}}

is the signal of the surroundings directly adjacent to the needle 23 Sneedle0.33em${{S}_{needle}}\ $ was obtained from sagittal or axial images using a region of interest (ROI) centered on the needle shaft, with the region constrained to the known needle dimensions in the sagittal image and a circular ROI with twice the radius of the needle for the axial image.…”

Section: Methodsmentioning

confidence: 99%

“…where S needle is the signal along the needle and S background is the signal of the surroundings directly adjacent to the needle. 23 S needle was obtained from sagittal or axial images using a region of interest (ROI) centered on the needle shaft,with the region constrained to the known needle dimensions in the sagittal image and a circular ROI with twice the radius of the needle for the axial image. S background was obtained from sagittal images using the mean of an ROI directly above and below the needle and from axial images using a circular ROI capturing an area with 4 times the needle radius around the needle, excluding the S needle ROI.…”

Section: Contrast Measurementsmentioning

confidence: 99%

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Color VISION for improved ultrasound visualization of brachytherapy needles

Dupere,

Brost,

Hainy

et al. 2024

Medical Physics

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BackgroundHigh dose rate brachytherapy is commonly used in the treatment of prostate cancer. Treatment planning is often performed under transrectal ultrasound (US) guidance, but brachytherapy needles can be challenging to digitize due to the presence of poor US conspicuity and imaging artifacts. The plan accuracy and quality, however, are dependent on the proper visualization of the needles with millimeter accuracy.PurposeThis work describes a technique for generating a color overlay of needle locations atop the grayscale US image. Prototype devices were developed to produce vibrations in the brachytherapy needles that generate a high contrast color Doppler (CD) signal that highlights the needle locations with superior contrast and reduced artifacts. Denoted by the acronym color VISION (Vibrationally Induced Shimmering for Identifying an Object's Nature), the technology has the potential to improve applicator conspicuity and facilitate automated applicator digitization.MethodsThree prototype vibrational devices with frequencies between 200–450 Hz were designed in‐house and evaluated with needle implants in a phantom and cadaveric male pelvis using: (1) an actuator attached to the front of a prostate needle template; (2) an actuator attached to the top of the needle template; and (3) a hand‐held actuator with a stylet, inserted directly into a needle's inner lumen. Acquired images were postprocessed in MATLAB to evaluate the potential for automated digitization.ResultsAll prototype devices produced localized shimmering in implanted brachytherapy needles in both the axial and sagittal planes. The template mounted actuators provided better vibrational coupling and ease of operation than the stylet prototype. The Michelson contrast, or visibility, of the shimmering CD signal was 100% compared with ≤40% for B‐mode imaging of a single needle. Proof‐of‐principle for automated applicator digitization using only the CD signal was demonstrated.ConclusionsThe color VISION prototype devices successfully coupled mechanical vibrations into brachytherapy needles to generate US CD shimmering and accurately highlight brachytherapy needle locations. The high contrast and natively registered signal are promising for future work to automate the needle digitization and provide a real‐time visual overlay of the applicator on the B‐mode US image.

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“…The Michelson contrast (also known as visibility), C, was measured on B‐mode and C‐mode imaging of needles in phantom using Equation (1):

\begin{equation*}C\ = \ \frac{{{{S}_{needle}} - \ {{S}_{background}}}}{{{{S}_{needle}} + \ {{S}_{background}}}}\end{equation*}

where

{{S}_{needle}}

is the signal along the needle and

{{S}_{background}}

Section: Methodsmentioning

confidence: 99%

Section: Contrast Measurementsmentioning

confidence: 99%

Color VISION for improved ultrasound visualization of brachytherapy needles

Dupere,

Brost,

Hainy

et al. 2024

Medical Physics

Self Cite

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“…The advantage of DL is the ability to recognize novel scenes by automatically extracting labeled features through learning of generalized features in training samples [16][17][18]. Deep learning plays a major role in brachytherapy [19]; some studies have focused on the automatic applicators reconstruction in IGBT workflow based on DL methods [20][21][22][23][24][25][26][27]. However, geometric metrics and subjective assessment were always selected to evaluate the performance of DL models in previous studies, with few studies reporting dosimetric differences in auto-reconstruction of applicators.…”

Section: Purposementioning

confidence: 99%

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

Xie¹,

Wang²,

Chen³

et al. 2023

jcb

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The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images.Material and methods: A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis. Results:The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences.Conclusions: DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.

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