Evaluation of attenuation correction method for head holder in brain perfusion single-photon emission computed tomography.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2024-02-09 DOI:10.1007/s12194-024-00778-x
Masahiro Nakashima, Yuta Yamazaki
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引用次数: 0

Abstract

Head holder attenuation affects brain perfusion single-photon emission computed tomography (SPECT) image quality. Here, we proposed a head holder-attenuation correction (AC) method using attenuation coefficient maps calculated by Chang's method from CT images. Then, we evaluated the effectiveness of the head holder-AC method by numerical phantom and clinical cerebral perfusion SPECT studies. In the numerical phantom, the posterior counts were 10.7% lower than the anterior counts without head holder-AC method. However, by performing head holder-AC, the posterior count recovered by approximately 6.8%, approaching the true value. In the clinical study, the normalized count ratio was significantly increased by performing the head holder-AC method in the posterior-middle cerebral artery, posterior cerebral artery and cerebellum regions. There were no significant increases in other regions. The head holder-AC method can correct the counts attenuated by the head holder.

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评估脑灌注单光子发射计算机断层扫描中头部支架的衰减校正方法。
头架衰减会影响脑灌注单光子发射计算机断层扫描(SPECT)图像的质量。在此,我们提出了一种头架-衰减校正(AC)方法,该方法使用张氏方法从 CT 图像中计算出的衰减系数图。然后,我们通过数值模型和临床脑灌注 SPECT 研究评估了头架-AC 方法的有效性。在数值模型中,后方计数比未使用头架-AC 方法的前方计数低 10.7%。然而,通过头架-AC,后方计数恢复了约 6.8%,接近真实值。在临床研究中,在大脑中后动脉、大脑后动脉和小脑区域采用头枕-AC 法后,归一化计数比明显增加。其他区域则无明显增加。头枕-AC 法可以纠正因头枕而衰减的计数。
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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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Acknowledgment. Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.
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