The appropriate combination is
Case 287
【Progress】
By the way, ADC values of normal brain, thalamus, gray matter and white matter were 0.797, 0.837 and 0.881, respectively.
【Discussion】
When radiation technologist skips creation of ADC map after producing Diffusion Weighted Imaging session on MRI, how do you know an ADC value of region of interest (ROI) ?
Let us go back to definition of ADC value. ADC values are calculated using 2 or more acquisitions with different diffusion weightings. The data is drawn using log curve: the longitudinal axis is raw data of signal intensity on ROI and the transverse axis of b degree. It implies that b0 indicates the data before motion probe gradient (MPG) and b1000 or b2000 indicates the data after adding strength of MPG. Then, a line formed by the data of b0 to b1000 or to b2000. An ADC value equals to a slope of a line using two acquisition data of b0 and b1000 or b0 and b2000 (ADC = Loge Sb/Sb0)(Fig. 5)(1, 2). In brain MRI, the strength of b1000 is used, while in prostatic gland MRI, b2000 is used.
Based on basis of the theory, calculation for ADC value can be obtained using SMART phone. Namely, when you hit the words of calculation method and MRIADC values on screen of SMART phone, you can get a calculator with Loge curve which enables to hit the raw data of ROI on DWI with b0 and b1000, inducing to get the calculated data of Loge. Then, the calculated data of b1000 is subtracted by that of b0. In the end, the subtracted data is divided by 1000, leading to ADC value.
To my surprise in this week, ADC values of brain parenchyma are relatively low; 0.797 in thalamus, 0.837 in cerebral cortex and 0.881 in subcortex white matter. It indicates water molecules are relatively repressed in normal brain parenchyma. Meanwhile, in our cases shown in Case 13, ADC of malignant lymphoma was ADC 0.5 10-3 mm2/s at b1000, meningioma ADC 0.7, Metastatic brain tumor ADC 0.9, Glioblastoma ADC 1.1, and Neurinoma ADC 1.4 (4 – 6). It implies metastatic brain tumor, glioblastoma and neurinoma do not appear as high signal intensity on MRI with DWI. That is why MRI with DWI is rated low for diagnosis of brain tumor, compared to PETCT that malignant brain tumors consume energy based on glucose. Brain tumors with high signal intensity should be of ADC lowering compared to brain parenchyma. Brain malignant lymphoma and meningioma are applicable as high signal intensity on MRIDWI because the ADC value 0.5 and 0.7, respectively (4-6).
In our cases, ADC value of epidermoid tumor was 0.713 and that of meningioma was 0.769, indicative of high signal intensity and slightly high signal intensity on DWI, while that of hamartoma was 1.146 and that of neurinoma: was 1.362, indicative no high signal intensity on DWI. It is known that CP angle tumors include neurinoma, meningioma and epidermoid tumor. Although it might be possible to differentiate them based on the pattern of images with T1WI, T2WI and Gd-enhanced T1WI, the ADC values of CP angle tumors might be useful to differentiate them because the differentiation of water molecules diffusion.
【Summary】
We presented four cases with: Epidermoid cyst, ADC value 0.713; Hamartoma, ADC value 1.146; Meningioma, ADC value 0.769: Neurinoma, ADC value 1.362. It is borne in mind that ADC values of thalamus, gray matter and white matter were 0.797, 0.837 and 0.881, respectively. Further, in Case 13 previously reported in Hannann Municipal Hospital, the ADC value of malignant lymphoma was ADC 0.5 10-3 mm2/s at b1000, meningioma ADC 0.7, Metastatic brain tumor ADC 0.9, Glioblastoma ADC 1.1, and Neurinoma ADC 1.4. It indicates that of these tumors, the tumors whose ADC values are less than normal brain parenchyma are malignant lymphoma, meningioma and epidermoid tumor. To my surprise, metastatic tumors and glioblastoma are not demonstrated high signal intensity on DWIMRI, indicative of DWIMRI rating low for differential diagnosis on brain tumors. However, ADC values are useful to identify malignant lymphoma, meningioma and epidermoid tumor rather than other tumors.
【References】
1.Aoki S et al. Diffusion MRI 3rd edition. Syujunnsha 2013 (Japanese)
2.Kita M. Personal communication in Fuchu Hospital
3.Shim WH et al. Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion for Differentiating among Glioblastoma, Metastasis, and Lymphoma Focusing on Diffusion-Related Parameter. PLoS One. 2015 30;10:e0134761. doi: 10.1371/journal.pone.0134761. eCollection 2015.
4.Doskaliyev A et al. Lymphomas and glioblastomas: differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T. Eur J Radiol. 2012;81:339-344. doi: 10.1016/j.ejrad.2010.11.005. Epub 2010 Dec 3.
5.Xu XQ et al. Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient. Int J Neurosci. 2016 Apr 6:1-8. [Epub ahead of print]
6.Sener RN, et al. Diffusion magnetic resonance imaging of solid vestibular schwannomas J Comput Assist Tomogr. 2003 ;27:249-52.
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