This publication received support from the Shaanxi Provincial Key Research and Development Plan: 2023-ZDLSF-56 and the Shaanxi Provincial &#34;Scientist + Engineer&#34; Team Construction: 2022KXJ-019.

This study was approved by the Institutional Review Board of Sunsimiao Hospital affiliated with the Beijing University of Chinese Medicine. The patient was recruited from the Department of Thyroid, Sunsimiao Hospital. The patient underwent a thyroid ultrasound examination and gave informed consent for the study. In this investigation, 4D ultrasound data acquired using a handheld device were utilized to reconstruct triplanar views of the thyroid gland. Furthermore, real-time synchronous color Doppler imaging was achieved. The software tools used in this research are listed in the Table of Materials.
1. Data collection and preparation
<ol>
	<li>Using a portable handheld ultrasound device, place the linear array transducer transversely on the patient&#39;s neck to image the thyroid in the cross-sectional plane. Slide the probe slowly and steadily along the length of the thyroid while maintaining probe contact and orientation.</li>
	<li>Acquire a sequence of transverse B-mode images visualizing thyroid morphology at a frame rate of 33 Hz.</li>
	<li>Concurrently, apply color Doppler to detect blood flow in the gland and vessels. Scan from the superior to inferior thyroid poles to cover the entire gland. The resulting dynamic imaging sequence comprises consecutive transverse slices that form two 4D data sets.</li>
	<li>Loading and browsing of 4D B-mode ultrasound data
	<ol>
		<li>Copy all DICOM data to a customized working directory. 
		NOTE: The working directory is the same in both the operating system and MATLAB. Press Enter after typing each line to run the command in MATLAB.</li>
		<li>Import the B-mode US data file into MATLAB using the dicomread function, and use the size function to view the dimensions of the data.
		<ol>
			<li>Open MATLAB on the computer.</li>
			<li>In the Command window, type: 
			VB0 = dicomread(&#39;fname.dcm&#39;); 
			Where &#39;fname.dcm&#39; could be replaced with the actual filename of the DICOM data. This will read in the DICOM file and store the image data in the variable VB0.</li>
			<li>To view the size of the loaded data, type: 
			size(VB0), 
			NOTE: The 4D data imported here had dimensions of 768 pixels x 1024 pixels x 3 x 601 layers. The 768 pixels x 1024 pixels x 3 corresponds to a standard RGB image, where each pixel is represented by three channels with 24-bit depth. The 601 layers indicate the total number of scanned slices.</li>
		</ol>
		</li>
		<li>Call the US_B_Show function to convert the 4D matrix data into a continuous grayscale video sequence to be played back continuously for detailed examination (see Figure 1).
		<ol>
			<li>To convert this 4D ultrasound data matrix VB0 imported in Step 1.4.2.2 using the dicomread function on the DICOM files into a continuously playing grayscale video sequence, call the US_B_Show function by typing the following command in the MATLAB Command window: 
			US_B_Show(VB0) 
			Where VB0 is the 4D matrix variable containing the ultrasound data imported previously.</li>
		</ol>
		</li>
		<li>The GUI in Figure 1 shows playback buttons for pause, forward, rewind, etc.
		<ol>
			<li>Press the play button to initiate continuous video playback of the frame sequence. Use the pause and play control tool icons for flexible navigation of any frame. Use the zoom in/out buttons to dynamically magnify or minify the images during playback and the default zoom button to reset to the original 1x view.</li>
			<li>Click on the inspect pixel values button and move the mouse over a region to overlay crosshairs with pixel coordinates and intensities for localized analysis. 
			NOTE: These interactive controls enable flexible inspection of ultrasound data characteristics across both space and time.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Loading and browsing of 4D color Doppler ultrasound data
	<ol>
		<li>Import the color Doppler ultrasound data file into MATLAB using the dicomread function, and use the size function to view the dimensions of the data. 
		NOTE: The 4D data imported here had dimensions of 768 pixels x 1024 pixels x 3 x 331 layers. The 768 pixels x 1024 pixels x 3 corresponds to a standard RGB image, with red and blue representing blood flow in different directions. The 331 layers indicate the total number of scanned slices.</li>
		<li>Use the US_C_Show function to convert the 4D matrix data into a continuous color video sequence to be played back continuously for detailed examination (see Figure 2). 
		NOTE The GUI in Figure 2 has the same set of interactive controls and operations as described previously in step 1.4.4 for Figure 1.</li>
	</ol>
	</li>
</ol>
2. Synchronous observation of B-mode and color Doppler ultrasound
NOTE: The 4D B-Mode Ultrasound Data shown in Figure 1 and the 4D Color Doppler Ultrasound Data shown in Figure 2 contain the same absolute time stamps in the fourth dimension along the temporal axis. This field is recorded in the DICOM metadata as FrameTimeVector. Based on the time values in this field, Figure 1 and Figure 2 can be synchronized in real time.
<ol>
	<li>After reading the two 4D files using the dicomread command, execute the Synchronize_B_C function with the two 4D matrices as inputs. 
	NOTE: Figure 3 shows the resulting video that can still be played back continuously. The difference now is that the 4D B-Mode Ultrasound Data and the 4D Color Doppler Ultrasound Data are synchronized in real time within the same video frames. The GUI in Figure 3 has the same set of interactive controls and operations as described previously in step 1.4.4 for Figure 1.</li>
</ol>
3. Synchronous triplanar reconstruction for thyroid
NOTE: To enable more precise localization and quantification of lesions, this study performed triplanar reconstruction of the thyroid gland from the acquired 4D ultrasound data, with real-time interactivity. This allows clinicians to rapidly and accurately pinpoint lesions, laying a solid foundation for subsequent quantification of the affected regions.
<ol>
	<li>Call the thyroid_triplanar function with the 4D B-mode ultrasound data from Figure 1 as input to derive the three orthogonal planes (coronal, sagittal, and axial) as shown in Figure 4.</li>
	<li>The crosshair interaction in Figure 4 enables real-time inspection of different parts of the thyroid gland. Click and drag the center of the crosshair for an arbitrary 3D examination of the thyroid anatomy reconstructed from ultrasound. 
	NOTE: The GUI in Figure 4 also enables adjustment of the grayscale intensity range, contrast, and brightness of the triplanar views.</li>
	<li>Press and drag the left mouse button over any region of the images for real-time modification of brightness and contrast levels. Release the mouse button to confirm and finalize the adjustments.</li>
</ol>
4. Synchronous triplanar reconstruction for 3D blood flow field
NOTE: Reconstructing the synchronous triplanar views for the 3D blood flow field based on 4D color Doppler ultrasound data is also clinically important for characterizing Hashimoto&#39;s Thyroiditis (HT).
<ol>
	<li>Call the thyroid_3D_blood function with the 4D C-mode ultrasound data from Figure 2 as input to derive the three orthogonal planes (coronal, sagittal, and axial) as shown in Figure 5.</li>
	<li>The crosshair interaction in Figure 5 enables real-time inspection of different parts of the thyroid gland. Click and drag the center of the crosshair for an arbitrary 3D examination of the thyroid anatomy reconstructed from ultrasound. 
	NOTE: The GUI in Figure 5 also enables adjustment of the grayscale intensity range, contrast, and brightness of the triplanar views.</li>
	<li>Press and drag the left mouse button over any region of the images for real-time modification of brightness and contrast levels. Release the mouse button to confirm and finalize the adjustments.</li>
</ol>
5. Synchronization of B-mode triplanar views and color Doppler triplanar views
NOTE: Building upon the triplanar views shown in Figure 4, synchronizing the corresponding color Doppler flow images to the lesion locations would undoubtedly facilitate the diagnosis and quantification of the pathological progression in Hashimoto&#39;s Thyroiditis (HT).
<ol>
	<li>Drag the crosshair interaction in Figure 4 to locate the region of interest, and execute US_B2C to obtain the corresponding location in the color Doppler triplanar views.</li>
	<li>Drag the crosshair interaction in Figure 5 to locate the region of interest, and execute US_C2B to obtain the corresponding location in the B-mode triplanar views. 
	NOTE: Figure 6 lays a solid ultrasonographic foundation for the precise localization and definitive diagnosis of Hashimoto&#39;s thyroiditis (HT) lesions.</li>
</ol>

Synchronous Visualization of Thyroid Structural and Functional Information

<ol>
	<li>Ragusa, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hashimotos'+thyroiditis:+Epidemiology,+pathogenesis,+clinic+and+therapy.">Hashimotos' thyroiditis: Epidemiology, pathogenesis, clinic and therapy.</a> Best Pract Res Clin Endocrinol Metab. 33 (6), 101367 (2019).</li><li>Ralli, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hashimoto's+thyroiditis:+An+update+on+pathogenic+mechanisms,+diagnostic+protocols,+therapeutic+strategies,+and+potential+malignant+transformation.">Hashimoto's thyroiditis: An update on pathogenic mechanisms, diagnostic protocols, therapeutic strategies, and potential malignant transformation.</a> Autoimmun Rev. 19 (10), 102649 (2020).</li><li>Soh, S., Aw, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laboratory+testing+in+thyroid+conditions+-+pitfalls+and+clinical+utility.">Laboratory testing in thyroid conditions - pitfalls and clinical utility.</a> Ann Lab Med. 39 (1), 3-13 (2019).</li><li>Cansu, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+value+of+3D+power+Doppler+ultrasound+in+the+characterization+of+thyroid+nodules.">Diagnostic value of 3D power Doppler ultrasound in the characterization of thyroid nodules.</a> Turk J Med Sci. 49, 723-729 (2019).</li><li>Haugen, B. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=2015+American+Thyroid+Association+Management+Guidelines+for+adult+patients+with+thyroid+nodules+and+differentiated+thyroid+cancer:+The+American+Thyroid+Association+Guidelines+Task+Force+on+Thyroid+Nodules+and+Differentiated+Thyroid+Cancer.">2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer.</a> Thyroid. 26 (1), 1-133 (2016).</li><li>Acharya, U. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnosis+of+Hashimoto's+thyroiditis+in+ultrasound+using+tissue+characterization+and+pixel+classification.">Diagnosis of Hashimoto's thyroiditis in ultrasound using tissue characterization and pixel classification.</a> Proc Inst Mech Eng H. 227 (7), 788-798 (2013).</li><li>Zhang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning+to+diagnose+Hashimoto's+thyroiditis+from+sonographic+images.">Deep learning to diagnose Hashimoto's thyroiditis from sonographic images.</a> Nat Commun. 13 (1), 3759 (2022).</li><li>Huang, J., Zhao, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+diagnosis+progress+of+ultrasound+imaging+technology+in+thyroid+diffuse+diseases.">Quantitative diagnosis progress of ultrasound imaging technology in thyroid diffuse diseases.</a> Diagnostics. 13 (4), 700 (2023).</li><li>Gasic, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relationship+between+low+vitamin+D+levels+with+Hashimoto+thyroiditis.">Relationship between low vitamin D levels with Hashimoto thyroiditis.</a> Srp Arh Celok Lek. 151 (5-6), 296-301 (2023).</li><li>Sultan, S. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+3D+ultrasound+reliable+for+the+evaluation+of+carotid+disease?+A+systematic+review+and+meta-analysis.">Is 3D ultrasound reliable for the evaluation of carotid disease? A systematic review and meta-analysis.</a> Med Ultrason. 25 (2), 216-223 (2023).</li><li>Arsenescu, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+ultrasound+reconstructions+of+the+carotid+artery+and+thyroid+gland+using+artificial-intelligence-based+automatic+segmentation-qualitative+and+quantitative+evaluation+of+the+segmentation+results+via+comparison+with+CT+angiography.">3D ultrasound reconstructions of the carotid artery and thyroid gland using artificial-intelligence-based automatic segmentation-qualitative and quantitative evaluation of the segmentation results via comparison with CT angiography.</a> Sensors. 23 (5), 2806 (2023).</li><li>Kr&#246;nke, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracked+3D+ultrasound+and+deep+neural+network-based+thyroid+segmentation+reduce+interobserver+variability+in+thyroid+volumetry.">Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.</a> PLoS One. 17 (7), e0268550 (2022).</li><li>Hazem, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+of+shear+wave+elastography+in+the+evaluation+of+diffuse+thyroid+diseases+in+children+and+adolescents.">Reliability of shear wave elastography in the evaluation of diffuse thyroid diseases in children and adolescents.</a> Eur J Radiol. 143, 109942 (2021).</li><li>Herickhoff, C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-cost+volumetric+ultrasound+by+augmentation+of+2D+systems:+design+and+prototype.">Low-cost volumetric ultrasound by augmentation of 2D systems: design and prototype.</a> Ultrasound Imaging. 40 (1), 35-48 (2017).</li><li>Seifert, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+thyroid+volume+determination+by+stitched+3D-ultrasound+data+sets+in+patients+with+structural+thyroid+disease.">Optimization of thyroid volume determination by stitched 3D-ultrasound data sets in patients with structural thyroid disease.</a> Biomedicines. 11 (2), 381 (2023).</li></ol>

The software tool for thyroid disease precision quantification, listed in the Table of Materials of this study as Thyroid Disease Precision Quantification V1.0, is a product of Beijing Intelligent Entropy Science &#38; Technology Co., Ltd. The intellectual property rights of this software tool belong to the company. The authors have no conflicts of interest to declare.

Critical steps in the protocol 
While Figure 1 and Figure 2 have value for inspection and diagnosis, determining lesion location and views from other perspectives requires expert experience. For the diagnosis of Hashimoto&#39;s thyroiditis (HT), synchronizing Figure 1 and Figure 2 in real time is also an important and critical step. Protocol step 3.3 is one of the key steps where, ...

Hashimoto's thyroiditis (HT), the most frequent autoimmune thyroid disorder (AITD), is the leading cause of hypothyroidism in iodine-sufficient areas of the world1. It is characterized by lymphocytic infiltration and autoantibodies against thyroid antigens, leading to the destruction of thyroid architecture and hypothyroidism2. Staging of HT aims to assess the severity and guide treatment decisions. It relies on a combination of biochemical markers such as thyroid stimulating hormone (TSH) and thyroid autoantibodies3, as well as ultrasonographic features visible on thyroid ultrasound4,5,6.
On ultrasound examination, HT demonstrates characteristic findings, including diffusely decreased echogenicity, heterogeneous echotexture, micronodularity, and increased blood flow on color Doppler6,7. However, conventional two-dimensional (2D) grayscale ultrasound lacks quantitative methods for systematically analyzing these features for HT staging8. The assessment of vascularity changes is also limited to qualitative visual inspection in 2D mode. The complex three-dimensional (3D) architecture of the thyroid gland further hampers thorough evaluation using conventional 2D slicing9,10. These factors lead to imaging blind spots and misinterpretation, resulting in low sensitivity and specificity, especially for less experienced practitioners11,12.
Conventional handheld ultrasound scanning integrates real-time acquisition and diagnosis. This coupled workflow reliance increases the likelihood of oversight errors during scanning. The lack of spatial localization and tracking also makes lesion identification and monitoring imprecise12,13. Dedicated 3D ultrasound systems have emerged to address these limitations and have shown promising results14,15. However, most 3D ultrasound technologies require complex mechanical scanning mechanisms and specialized transducers, leading to high costs and barriers to adoption.
To overcome the limitations of conventional 2D and 3D ultrasound techniques, this study proposes a novel 3D reconstruction and visualization solution tailored for thyroid examination. Using widely available handheld ultrasound, multiple 2D sweeps are first acquired to scan the entire thyroid gland. 3D volumetric reconstruction is then realized by spatial registration and fusion of the 2D sequences. Concurrently, color Doppler frames are coregistered to create vascularity maps visualizing blood flow changes. The reconstructed 3D grayscale volumes and colored vascularity maps are finally integrated into a single platform, enabling synchronized multi-planar visualization and combined structural-functional inspection.
This proposed 3D fusion technique provides a systematic and comprehensive evaluation of the complex thyroid morphology from different aspects. By minimizing blind spots and enabling global overview, it could help improve diagnostic accuracy and reduce oversight errors, especially benefiting novice practitioners. The multi-modal visualization also facilitates rapid and precise localization of lesions, holding promise for early diagnosis and treatment of thyroid nodules and tumors. Moreover, the method introduces quantitative 3D feature analysis which has not been investigated for HT staging before. With wide adoption, it has the potential to standardize and objectify the currently experience-dependent ultrasound diagnosis procedures. By synergistically integrating handheld 3D reconstruction, multi-modal fusion, quantitative feature analysis, and flexible visualization into a streamlined workflow, this low-cost, easy-to-use technique represents a diagnostically powerful leap from conventional 2D ultrasound for advancing thyroid examination.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks&#160;</td>
			<td>2023B</td>
			<td>Computing and visualization&#160;</td>
		</tr>
		<tr>
			<td>Tools for Thyroid Disease Precision Quantification</td>
			<td>Intelligent Entropy</td>
			<td>Thyroid-3D V1.0</td>
			<td>Beijing Intelligent Entropy Science &#38; Technology Co Ltd. 
			Modeling for Thyroid Disease</td>
		</tr>
	</tbody>
</table>

synchronous triplanar reconstruction integrated with color doppler mapping for precise and rapid localization of thyroid lesions

This paper proposes a novel thyroid examination technique based on five-dimensional (5D) synchronous reconstruction of ultrasound data. The raw temporal sequences are reconstructed into 3D volumetric data reflecting anatomical structure. Triplanar visualization from three orthogonal planes is realized to provide a systematic inspection of the entire gland. Color Doppler imaging is integrated into each triplanar slice to map vascularity changes. This multi-modal fusion enables synchronous display of structural, functional, and blood flow information in the reconstructed 5D space. Compared to conventional scanning, this technique offers the benefits of flexible offline diagnosis, reduced dependency on scanning, enhanced intuitive interpretation, and comprehensive multi-aspect evaluation. By minimizing oversight errors, it could improve diagnostic accuracy, especially for novice practitioners. The proposed 5D fusion method allows rapid and precise localization of lesions for early detection. Future work will explore integration with biochemical markers to further improve diagnostic precision. The technique has considerable clinical value for advancing thyroid examination.

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

As shown in the graphical user interface (GUI) in Figure 1 and Figure 2, the ultrasound scanning sequence can be checked continuously. However, this two-dimensional examination relies heavily on the thyroidologist&#39;s anatomical knowledge to mentally reconstruct the lesion&#39;s location, which is challenging for novices and results in a lack of quantitative consistency. Figure 3 fuses the B-mode grayscale with co...

Watch this Scientific Journal Video about Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis at JoVE.com

Here we present a 5D ultrasound technique combining multi-planar 3D reconstruction and color Doppler fusion, which enables synchronous visualization of thyroid structural and functional information. By minimizing blind spots, this method allows rapid, precise localization of lesions to improve diagnostic accuracy, especially benefiting novice practitioners.

This paper proposes a novel thyroid examination technique based on five-dimensional (5D) synchronous reconstruction of ultrasound data. The raw temporal ...

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382 Views.  Beijing University of Chinese Medicine. Here we present a 5D ultrasound technique combining multi-planar 3D reconstruction and color Doppler fusion, which enables synchronous visualization of thyroid structural and functional information. By minimizing blind spots, this method allows rapid, precise localization of lesions to improve diagnostic accuracy, especially benefiting novice practitioners.