Acquisition time reduction in pediatric <sup>99m</sup>Tc‐DMSA planar imaging using deep learning

Ichikawa, Shota; Sugimori, Hiroyuki; Ichijiri, Koki; Yoshimura, Takaaki; Nagaki, Akio

doi:10.1002/acm2.13978

Cited by 4 publications

(1 citation statement)

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“…Deep learning (DL) algorithms can be used as computational tools to automatically detect anomalies in medical images [ 16 , 17 , 18 ]. The use of deep learning models in medical imaging has potential to improve the accuracy and reduce the time and cost of medical imaging analysis [ 19 , 20 ]. It can also be used to identify and classify lesions, detect signs of disease, and predict patient prognosis [ 21 , 22 , 23 , 24 , 25 ].…”

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confidence: 99%

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

et al. 2023

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(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost.

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Section: Introductionmentioning

confidence: 99%

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

et al. 2023

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Acquisition time reduction in pediatric ^99mTc‐DMSA planar imaging using deep learning

Ichikawa

Sugimori

Ichijiri

et al. 2023

J Applied Clin Med Phys

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Purpose Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99mTc‐dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full‐acquisition‐time images from short‐acquisition‐time pediatric 99mTc‐DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. Methods One hundred and fifty‐five cases that underwent pediatric 99mTc‐DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full‐time images from short‐time images. We used the normalized mean squared error (NMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full‐time images. In addition, the renal uptake of 99mTc‐DMSA was calculated, and the difference in renal uptake from the reference full‐time images was assessed using scatter plots with Pearson correlation and Bland–Altman plots. Results The predicted full‐time images from the deep learning models showed a significant improvement in image quality compared to the short‐time images with respect to the reference full‐time images. In particular, the predicted full‐time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4−0.5] %) and the highest PSNR (55.4 [54.7−56.1] dB) and SSIM (0.997 [0.995−0.997]). For renal uptake, an extremely high correlation was achieved in all short‐time and three predicted full‐time images (R2 > 0.999 for all). The Bland–Altman plots showed the lowest bias (−0.10) of renal uptake in ResUnet, while short‐time images showed the lowest variance (95% confidence interval: −0.14, 0.45) of renal uptake. Conclusions Our proposed method is capable of producing images that are comparable to the original full‐acquisition‐time images, allowing for a reduction of acquisition time/injected dose in pediatric 99mTc‐DMSA planar imaging.

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Fast SPECT/CT planar bone imaging enabled by deep learning enhancement

Pan,

Qi,

Meng

et al. 2024

Medical Physics

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BackgroundThe application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of high‐count bone scintigraphy images from low‐count counterparts. Few studies have been conducted and validated on real clinical pairs due to the misalignment inherent in multiple scan procedures.PurposeTo generate high quality whole‐body bone images from 2× and 3× fast scans using deep learning based enhancement method.Materials and MethodsSeventy‐six cases who underwent whole‐body bone scans were enrolled in this prospective study. All patients went through a standard scan at a speed of 20 cm/min, which followed by fast scans consisting of 2× and 3× accelerations at speeds of 40 and 60 cm/min. A content‐attention image restoration approach based on Residual‐in‐Residual Dense Block (RRDB) is introduced to effectively recover high‐quality images from fast scans with fine‐details and less noise. Our approach is robust with misalignment introduced from patient's metabolism, and shows valid count‐level consistency. Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID) are employed in evaluating the similarity to the standard bone images. To further prove our method practical in clinical settings, image quality of the anonymous images was evaluated by two experienced nuclear physicians on a 5‐point Likert scale (5 = excellent) .ResultsThe proposed method reaches the state‐of‐the‐art performance on FID and LPIPS with 0.583 and 0.176 for 2× fast scans and 0.583 and 0.185 for 3× fast scans. Clinic evaluation further demonstrated the restored images had a significant improvement compared to fast scan in image quality, technetium 99m‐methyl diphosphonate (Tc‐99 m MDP) distribution, artifacts, and diagnostic confidence.ConclusionsOur method was validated for accelerating whole‐body bone scans by introducing real clinical data. Confirmed by nuclear medicine physicians, the proposed method can effectively enhance image diagnostic value, demonstrating potential for efficient high‐quality fast bone imaging in practical settings.

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Acquisition time reduction in pediatric ^99mTc‐DMSA planar imaging using deep learning

Cited by 4 publications

References 38 publications

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

Acquisition time reduction in pediatric ^99mTc‐DMSA planar imaging using deep learning

Fast SPECT/CT planar bone imaging enabled by deep learning enhancement

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Acquisition time reduction in pediatric 99mTc‐DMSA planar imaging using deep learning

Cited by 4 publications

References 38 publications

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

Acquisition time reduction in pediatric 99mTc‐DMSA planar imaging using deep learning

Fast SPECT/CT planar bone imaging enabled by deep learning enhancement

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Acquisition time reduction in pediatric ^99mTc‐DMSA planar imaging using deep learning

Acquisition time reduction in pediatric ^99mTc‐DMSA planar imaging using deep learning