Scholarlink

Objective <p>To determine whether software-based de-filtering can restore the quantitative accuracy of the bone scan index (BSI) and the number of hot spots (HSn) in whole-body scintigraphy images degraded by the Clarity2D noise-reduction filter.</p> Methods <p>In this IRB-approved retrospective study, 101 adults (mean age ± SD: 67 ± 13 years) who underwent <sup>99m</sup>Tc-HMDP whole-body scintigraphy on a cadmium-zinc-telluride (CZT) SPECT/CT system were analyzed. For each patient, three planar datasets were obtained: (i) unfiltered images, (ii) 40%–blend Clarity2D-filtered images, and (iii) software de-filtered images reconstructed with a deep learning-based inverse filter in VSBONE BSI v3.0. Quantitative indices (BSI and HSn) and lesion masks were automatically extracted. Agreement with the unfiltered reference was evaluated using Pearson correlation, Bland–Altman analysis (bias ± 95% limits), Dice coefficient, and the Hausdorff distance (<i>p</i> &lt; 0.05). Additionally, lesion detection accuracy was quantified using intersection over union (IoU)-based matching to calculate precision, recall, and F1-score.</p> Results <p>Clarity2D filtering significantly impaired quantitative concordance (BSI <i>r</i> = 0.23, bias = − 1.55 [–6.20 to 3.10]; HSn <i>r</i> = 0.23, bias = − 14.4 lesions). In contrast, de-filtering restored concordance (BSI <i>r</i> = 0.99, bias = − 0.04 [–0.26 to 0.17]; HSn <i>r</i> = 0.98, bias = − 0.04 lesions) and improved spatial overlap (Dice 0.40 to 0.82) while reducing the median Hausdorff distance from 103 pixels (IQR 85–188) to 39 pixels (IQR 1–40) (all <i>p</i> &lt; 0.001). The de-filtered method demonstrated superior lesion detection accuracy compared to Clarity2D (Precision: 0.77 ± 0.37 vs. 0.19 ± 0.29, Recall: 0.81 ± 0.37 vs. 0.43 ± 0.44, F1-score: 0.78 ± 0.36 vs. 0.23 ± 0.30). Furthermore, de-filtering achieved high inter-case stability (median F1-score: 1.0), whereas Clarity2D showed substantial variability (median F1-score: 0.057).</p> Conclusions <p>The proposed de-filtering algorithm reliably reverses Clarity2D-induced distortions, enabling accurate BSI and HSn measurements and robust lesion detection without additional radiation or acquisition time. This technique has the potential to broaden the clinical adoption of noise-reduction filters while preserving the integrity of downstream quantitative analyses.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Software-based de-filtering restores quantitative accuracy in Clarity2D-enhanced whole-body bone scintigraphy

  • 期刊论文

  • 发表日期：

    2025年12月29日

查看全文
  • Naoto Mochizuki,
  • Tadashi Hara,
  • Masaki Masubuchi,
  • Kanna Furuya,
  • Taichi Nakamura,
  • Zhixiang Wu,
  • Akemi Iwasaka,
  • Shingo Hashimoto,
  • Tsukasa Saida,
  • Takahito Nakajima

摘要

Objective

To determine whether software-based de-filtering can restore the quantitative accuracy of the bone scan index (BSI) and the number of hot spots (HSn) in whole-body scintigraphy images degraded by the Clarity2D noise-reduction filter.

Methods

In this IRB-approved retrospective study, 101 adults (mean age ± SD: 67 ± 13 years) who underwent 99mTc-HMDP whole-body scintigraphy on a cadmium-zinc-telluride (CZT) SPECT/CT system were analyzed. For each patient, three planar datasets were obtained: (i) unfiltered images, (ii) 40%–blend Clarity2D-filtered images, and (iii) software de-filtered images reconstructed with a deep learning-based inverse filter in VSBONE BSI v3.0. Quantitative indices (BSI and HSn) and lesion masks were automatically extracted. Agreement with the unfiltered reference was evaluated using Pearson correlation, Bland–Altman analysis (bias ± 95% limits), Dice coefficient, and the Hausdorff distance (p < 0.05). Additionally, lesion detection accuracy was quantified using intersection over union (IoU)-based matching to calculate precision, recall, and F1-score.

Results

Clarity2D filtering significantly impaired quantitative concordance (BSI r = 0.23, bias = − 1.55 [–6.20 to 3.10]; HSn r = 0.23, bias = − 14.4 lesions). In contrast, de-filtering restored concordance (BSI r = 0.99, bias = − 0.04 [–0.26 to 0.17]; HSn r = 0.98, bias = − 0.04 lesions) and improved spatial overlap (Dice 0.40 to 0.82) while reducing the median Hausdorff distance from 103 pixels (IQR 85–188) to 39 pixels (IQR 1–40) (all p < 0.001). The de-filtered method demonstrated superior lesion detection accuracy compared to Clarity2D (Precision: 0.77 ± 0.37 vs. 0.19 ± 0.29, Recall: 0.81 ± 0.37 vs. 0.43 ± 0.44, F1-score: 0.78 ± 0.36 vs. 0.23 ± 0.30). Furthermore, de-filtering achieved high inter-case stability (median F1-score: 1.0), whereas Clarity2D showed substantial variability (median F1-score: 0.057).

Conclusions

The proposed de-filtering algorithm reliably reverses Clarity2D-induced distortions, enabling accurate BSI and HSn measurements and robust lesion detection without additional radiation or acquisition time. This technique has the potential to broaden the clinical adoption of noise-reduction filters while preserving the integrity of downstream quantitative analyses.