Rakvongthai Yothin, Fahey Frederic, Borvorntanajanya Korn, Tepmongkol Supatporn, Vutrapongwatana Usanee, Zukotynski Katherine, El Fakhri Georges, Ouyang Jinsong
Division of Nuclear Medicine, Faculty of Medicine, Department of Radiology, Chulalongkorn University, Bangkok, Thailand.
Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, USA.
Med Phys. 2017 Apr;44(4):1437-1444. doi: 10.1002/mp.12167.
To improve the performance for localizing epileptic foci, we have developed a joint ictal/inter-ictal SPECT reconstruction method in which ictal and inter-ictal SPECT projections are simultaneously reconstructed to obtain the differential image.
We have developed a SPECT reconstruction method that jointly reconstructs ictal and inter-ictal SPECT projection data. We performed both phantom and patient studies to evaluate the performance of our joint method for epileptic foci localization as compared with the conventional subtraction method in which the differential image is obtained by subtracting the inter-ictal image from the co-registered ictal image. Two low-noise SPECT projection datasets were acquired using Tc and a Hoffman head phantom at two different positions and orientations. At one of the two phantom locations, a low-noise dataset was also acquired using a Tc-filled 3.3-cm sphere with a cold attenuation background identical to the Hoffman phantom. These three datasets were combined and scaled to mimic low-noise clinical ictal (three different lesion-to-background contrast levels: 1.25, 1.55, and 1.70) and inter-ictal scans. For each low-noise dataset, 25 noise realizations were generated by adding Poisson noise to the projections. The mean and standard deviation (SD) of lesion contrast in the differential images were computed using both the conventional subtraction and our joint methods. We also applied both methods to the 35 epileptic patient datasets. Each differential image was presented to two nuclear medicine physicians to localize a lesion and specify a confidence level. The readers' data were analyzed to obtain the localized-response receiver operating characteristic (LROC) curves for both the subtraction and joint methods.
For the phantom study, the difference between the mean lesion contrast in the differential images obtained using the conventional subtraction versus our joint method decreases as the iteration number increases. Compared with the conventional subtraction approach, the SD reduction of lesion contrast at the 10 iteration using our joint method ranges from 54.7% to 68.2% (P < 0.0005), and 33.8% to 47.9% (P < 0.05) for 2 and 4 million total inter-ictal counts, respectively. In the patient study, our joint method increases the area under LROC from 0.24 to 0.34 and from 0.15 to 0.20 for the first and second reader, respectively. We have demonstrated improved performance of our method as compared to the standard subtraction method currently used in clinical practice.
The proposed joint ictal/inter-ictal reconstruction method yields better performance for epileptic foci localization than the conventional subtraction method.
为了提高癫痫病灶定位的性能,我们开发了一种发作期/发作间期联合单光子发射计算机断层扫描(SPECT)重建方法,该方法同时重建发作期和发作间期的SPECT投影以获得差异图像。
我们开发了一种联合重建发作期和发作间期SPECT投影数据的SPECT重建方法。我们进行了体模和患者研究,以评估我们的联合方法在癫痫病灶定位方面的性能,并与传统的减法方法进行比较,传统减法方法是通过从配准后的发作期图像中减去发作间期图像来获得差异图像。使用锝和霍夫曼头部体模在两个不同位置和方向获取了两个低噪声SPECT投影数据集。在两个体模位置中的一个位置,还使用一个充满锝的3.3厘米球体获取了一个低噪声数据集,其冷衰减背景与霍夫曼体模相同。将这三个数据集进行组合和缩放,以模拟低噪声临床发作期(三种不同的病变与背景对比度水平:1.25、1.55和1.70)和发作间期扫描。对于每个低噪声数据集,通过向投影中添加泊松噪声生成25种噪声实现情况。使用传统减法方法和我们的联合方法计算差异图像中病变对比度的平均值和标准差(SD)。我们还将这两种方法应用于35例癫痫患者数据集。将每个差异图像呈现给两名核医学医师,以定位病变并指定置信水平。对读者的数据进行分析,以获得减法方法和联合方法的定位反应性接收者操作特征(LROC)曲线。
对于体模研究,随着迭代次数的增加,使用传统减法方法与我们的联合方法获得的差异图像中病变对比度平均值之间的差异减小。与传统减法方法相比,使用我们的联合方法在第10次迭代时病变对比度的SD降低范围,对于总发作间期计数为200万和400万时分别为54.7%至68.2%(P < 0.0005)和33.8%至47.9%(P < 0.05)。在患者研究中,我们的联合方法使第一位读者的LROC曲线下面积从0.24增加到0.34,第二位读者的从0.15增加到0.20。与目前临床实践中使用的标准减法方法相比,我们已证明我们方法的性能有所提高。
所提出的发作期/发作间期联合重建方法在癫痫病灶定位方面比传统减法方法具有更好的性能。