An explanation for the triphasic dependency of apparent diffusion coefficient (ADC) on T2 relaxation time: the multiple T2 compartments model

For in vivo diffusion weighted imaging (DWI), the apparent diffusion coefficient (ADC) has been considered to reflect tissue diffusion. ADC is calculated according to:

ADC=ln[S(b1)/S(b2)]b2−b1[1]

where b₂ and b₁ refers to a high b-value and a low b-value respectively, where S(b₂) and S(b₁) denote the image signal-intensity acquired at the high b-value and low b-value respectively. When the low b-value is 0, ADC is calculated according to

ADC=ln[S(b0)/S(b2)]b2[2]

where b₂ and b₀ refers to the high b-value and b=0 s/mm² respectively, where S(b₀) and S(b₂) denote the image signal-intensity acquired at the b-factor value of b=0 and the high b-value, respectively.

ADC can also be calculated using the three b-values (such as b=0, 50, 800 s/mm²), according to the formula:

ADC3b=−3∑i=13bi⋅ln[S(bi)]−∑i=13bi⋅∑i=13ln[S(bi)]3∑i=13bi2−(∑i=13bi)2[3]

Where b_i is the ith b value (unit: s/mm²), S(b_i) is the signal intensity at b_i.

Recently, we proposed that in vivo ADC measure is strongly associated with T2 relaxation time (T2) (1-5). T2 can be divided into short T2 band [<60 milliseconds (ms)], intermediate T2 band (60–80 ms), and long T2 band (>80 ms, all 3-T values). For the short T2 time band, there is a negative correlation between T2 and ADC. For the long T2 time band, there is a positive correlation between T2 and ADC. A tissue likely measures a low ADC if its T2 is close to 70 ms. On the other hand, a tissue is likely to measure a high ADC if its T2 is far away from 70 ms (Figures 1-3) (1-10). This observation initially appears to be puzzling. However, from Eq. [2], it can be seen that ADC value is high when the DWI signal difference between the b=0 image and the high b-value image is large. Thus, this phenomenon can be ‘simplistically remembered’ that, ADC value is high when: (I) the signal decay between b=0 image and high b-value image is fast such as the case for tissues with short T2, or (II) tissue has a very long T2 and appear high signal on b=0 image so that there is large amounts of signal to decay between b=0 image and high b-value image. ADC will measure a low value when tissue T2 is in the intermediate range of 60–80 ms. Note that, an application of the diffusion gradients will lead to a decrease in observed T2 for tissues, which can be interpreted as an application of diffusion gradients is associated with a longer time of echo (TE) for data acquisition (11).

Figure 1 Relationship between T2 and ADC for various tissues, 3-T data. With parotid gland tumors, ADC from lower to higher ranking: Warthin’s tumor, malignant tumor, benign tumor, PA. Note the linear correlation between T2 of parotid gland tumors and their ADC. Dotted arrow denotes susceptibility T2* black-out, which is observed with structures having a very short intrinsic T2 signal due to very short T2*. In this graph, dotted arrow is for illustration only, and does not reflect true quantitative values for susceptibility T2* black-out. This graph is adapted with permission from (4). ADC, apparent diffusion coefficient; ms, millisecond; PA, pleomorphic adenoma.

Figure 2 Relationship between T2 and ADC. Normal brain tissue and brain lymphoma are based on 3-T data (6). Brain glioma is based on 1.5-T data (7). Abscess is based on an estimation (3). The dotted blue arrow shows, as brain tissue turns into abscess or lymphoma, T2 increases and ADC decreases. The dotted orange arrow shows as glioma T2 increases, glioma ADC increases. This graph is reproduced with permission from (5). ADC, apparent diffusion coefficient; ms, millisecond.

Figure 3 The T2-ADC relationship for uterine myometrium (red circle label) is considered to be at the first phase with T2 shorter than 60 ms [see (5)]. In the analyses of DeMulder et al. (8), Bura et al. (9), and Barral et al. (10), myometrium tumors with shorter T2 (i.e., hypointense to myometrium) are associated with higher ADC (not restricted on ADC map, blue oval and red arrow 1 in this graph), myometrium tumors with longer T2 (i.e., hyperintense to myometrium) are associated with lower ADC (restricted on ADC map, blue oval and red arrow 2 in this graph). Cystic degenerated and myxoid degenerated tumors have very long T2 (i.e., highly hyperintense) and higher ADC (not restricted on ADC map, blue oval and red arrow 3). STUMP and lipoleiomyoma have heterogeneous T2-weighted signal and were termed as with ‘undetermined ADC’. The frame of this figure is based on Figure 1. Data in this figure are summarized from Tab. 2 by DeMulder et al. (8), Tab. 1 by Bura et al. (9), and Tab. 1 by Barral et al. (10). This graph is reproduced with permission from (5). ADC, apparent diffusion coefficient; LM, leiomyoma; ms, millisecond; STUMP, smooth muscle tumors of uncertain malignant potential.

In this letter, we attempt to explain the T2 dependency of ADC with the concept of the Intravoxel Incoherent Motion (IVIM) model. The IVIM model considering the T2 effect is expressed as (11-14):

S(b,TE)=S0[PF⋅e−TE/T2⋅e−b⋅Dfast+(1−PF)⋅e−TE/T2⋅e−b⋅Dslow][4]

where S₀ is considered a scaling term independent of both diffusion and ‘T2 effect’ [which is defined as magnetic resonance imaging (MRI) signal differences contributed by T2 difference], and it is implicitly assumed that repetition time is long enough to ensure no significant modulation of the signal from incomplete T1 relaxation. D_slow is the diffusion coefficient representing the slow ‘pure’ molecular diffusion (unaffected by perfusion). The perfusion fraction (PF) represents the fraction of the compartment related to (micro)circulation, which can be understood as the proportional ‘incoherently flowing fluid’ (i.e., blood) volume. D_fast is the perfusion-related diffusion coefficient representing the incoherent microcirculation within the voxel, which holds information for blood perfusion’s speed.

A number of IVIM studies have shown that, if the TE is increased for DWI data acquisition, which can be seen as a shortening of T2 in MRI signal measurement in the transverse plane (i.e., faster signal decay, see Eq. [4]), there is an associated increase of PF (Figure 4) (12,13,15). Note that D_fast and PF are most commonly positively correlated (16). We can take it that, within a region-of-interest (ROI) or within a voxel, the T2 is not homogeneous, instead within a ROI or within a voxel there are multiple T2 elements (including T2 of arterial blood and T2 of venous blood). These T2 elements can be conceptually classified into shorter T2 elements (together referred to as ’shorter T2 compartment’ in this letter) and longer T2 elements (together referred to as ‘longer T2 compartment’ in this letter). In in vivo tissues, there are likely multiple T2 compartments. There are very short T2 compartments and very long T2 compartments. For simplicity, in this letter, we only summarily consider the ‘shorter T2 compartment’ and ‘longer T2 compartment’. From the measurement of MRI signal, shorter T2 compartment can be considered equivalent to perfusion (fast diffusion) compartment (fast initial signal decay in the IVIM model), and longer T2 compartment can be considered equivalent to diffusion compartment (Figure 5). For IVIM modeling, the initial fast signal decay at lower b-values can be due to either perfusion or due to shorter T2 elopements. Therefore, longer TE or shorter T2 is associated with increased PF (increased initial signal decay in the IVIM model). Hereby shorter T2 can be due to a higher portion of shorter T2 elements or the T2 relaxation times are actually shorter or a combination of both. On the other hand, an increase of T2 is associated with an increase of D_slow and decreased measure of the fast component [Figure 6 (13,15,17-20), Figure 7 (20-22), Figure 8 (23), Figure 9 (24-29), Figure 10 (28,30-32)]. Similarly, in a mouse study at 7.0 T with implanted tumors, an increase of the diffusion gradient separation time was associated with an increase of PF and a decrease of a D_slow variant which considered the non-Gaussian diffusion kurtosis model (Figure 11) (33). A mouse brain tissue study at 11.7 T with a triexponential model also showed similar results (Figure 12) (34). Thus, Eq. [4] can be re-written as:

S(b,TE)=S0(Ffast⋅e−TE/T2shorter⋅e−b⋅Dfast+Fslow⋅e−TE/T2longer⋅e−b⋅Dslow)[5]

Figure 4 IVIM studies shows, if the TE is increased for DWI data acquisition which can be seen as a reduction of T2, then measured PF increases. Data are from Lemke et al. (12), Jerome et al. (13), and Führes et al. (15). DWI, diffusion weighted imaging; IVIM, Intravoxel Incoherent Motion; ms, millisecond; PF, perfusion fraction; TE, time of echo.

Figure 5 Considering the IVIM model, from MRI signal point of view, the shorter T2 compartment is equivalent to the perfusion compartment and the longer T2 compartment is equivalent to the slow diffusion compartment. For a tissue with an aggregate T2 value <60 ms (A), for example if T2 changes from 40 to 60 ms [such as the case of liver (PF₀) developed hepatocellular carcinoma (PF₁)], fast compartment measure decreases while slow compartment measure increases (if we do not consider other contributing factors); however, the decreased measure of the fast compartment dominates and the net effect for the composite ADC is a decrease measure. For a tissue with aggregate T2 value >80 ms (B), for example if T2 changes from 170 to 90 ms [such as the case for the prostate peripheral zone (PF₀) developed prostate cancer (PF₁)], fast compartment measure increases while slow compartment measure decreases (if we do not consider other contributing factors); however, the decrease measure of the slow compartment dominates and the net effect for ADC is a decrease measure. Dotted red lines denote ADC. The lines in this graph are for illustration only, as the exact signal changes of different tissues with increasing b-values vary and will be contributed by other factors. Note that, liver parenchyma has a T2 of around 40 ms; hepatocellular carcinoma has T2 of around 60 ms; prostate cancer has T2 of around 90 ms, and prostate peripheral zone cancer has T2 of around 170 ms. ADC, apparent diffusion coefficient; IVIM, Intravoxel Incoherent Motion; MRI, magnetic resonance imaging; ms, millisecond; PF, perfusion fraction.

Figure 6 IVIM studies shows, if the TE is increased for data acquisition which can be seen as a reduction of T2, then measured D_slow is decreased. The 4 studies data are based on the works of Cercueil et al. (17), Kuai et al. (18), Wurnig et al. (19), and Riexinger et al. (20), and these data were listed in the article reported by Riexinger et al. (20). Other data are from (13,15). For the 4 studies data, as different data processing methods will lead to different D_slow value, the comparison is shown for demonstration only. However, in the study of Riexinger et al., the long TE of 100 ms is likely associated with a truly lower D_slow than other studies with shorter TEs. Taking together Figure 4 and Figure 6, it appears that, for TE ranges less than 90 ms, ‘longer’ TE promotes more PF measure than depresses D_slow measure. IVIM, Intravoxel Incoherent Motion; ms, millisecond; PF, perfusion fraction; TE, time of echo.

Figure 7 Compared with measures at 1.5 T, liver IVIM measures at 3.0 T are associated with a higher PF and lower D_slow. The data are based on the work by Riexinger et al. (20). This observation, as suggested by the authors (20), may be attributed to the shorter T2 at 3.0 T than at 1.5 T. For demonstration purpose, liver T2 at 1.5 and 3.0 T are assumed to be 46 and 34 ms respectively. Therefore, data in this graph show T2 is negatively associated with PF and positively associated with D_slow. Similar trends have been reported by other researchers such as the work of Cui et al. (21), and reviewed by Li et al. (22). IVIM, Intravoxel Incoherent Motion; ms, millisecond; PF, perfusion fraction.

Figure 8 Shortening of prostate peripheral zone and prostate cancer T2 leads to an increase of PF and a reduction of D_slow, and with a reduction of ADC. The data are from the report by Mazaheri et al. (23). Diffusion weighted imaging was conducted before and after administration of a gadolinium agent which shortens T2. The results are consistent with the results shown in Figures 4,6,7. The changes for D_slow and ADC are more substantial for cancer tissue, likely due to the greater uptake of gadolinium agent in the cancer tissue than in the normal prostate peripheral zone. The milder changes in D_slow still lead to an overall increase in ADC, likely reflecting the large F_slow. For both pre-EC and post-EC scans, PF is overestimated in this study due to the limited b-values applied (23). ADC, apparent diffusion coefficient; EC, enhanced contrast; PF, perfusion fraction.

Figure 9 T2 is negatively associated with PF and positively associated with D_slow. For parotid gland tumors, it is well characterized that PA has long T2 and high ADC, WT has short T2 and low ADC, and the T2 and ADC of MT lie between WT and PA (see Figure 1). The parotid tumors T2 values are based on the report by Baohong et al. (24), and IVIM measured are based on the report by Ma et al. (25). For parotid gland, similar results have been described by other authors such as Yabuuchi et al. (26). That tissue perfusion is ‘WT > MT > PA’ has also been recently described by Yao et al. (27). The prostate peripheral zone and prostate cancer T2 values are based on the report by Han et al. (28), and IVIM measures are based on the report by Pang et al. (29). For prostate, it is well characterized that prostate peripheral zone has long T2 and high ADC, and prostate cancer has shorter T2 and lower ADC. Note that all the tissues in this graph have an aggregate T2 >80 ms; therefore, the slow compartment dominates. Despite the higher PF for WT and prostate cancer, still the composite metric of ADC is lower for WT and prostate cancer, likely reflecting the large F_slow for these tissues. ADC, apparent diffusion coefficient; ca, cancer; IVIM, Intravoxel Incoherent Motion; ms, millisecond; MT, malignant tumor (parotid gland); PF, perfusion fraction; PA, pleomorphic adenoma (parotid gland); WT, Warthin’s tumor (parotid gland).

Figure 10 For prostate and prostate cancer, T2 is negatively associated with D_fast and positively associated with D_slow. Despite the higher D_fast for prostate cancer, still the composite metric of ADC is lower for prostate cancer, likely reflecting the large F_slow for its tissue. Normal prostate measures are from the peripheral zone. The prostate peripheral zone and prostate cancer T2 values are based on the report by Han et al. (28). IVIM data in this graph are based on the reports by Shinmoto et al. (30), Valerio et al. (31), and Pesapane et al. (32). To fit into this graph, the D_fast measures reported by Valerio et al. are halved in this figure. ca, cancer; ADC, apparent diffusion coefficient; IVIM, Intravoxel Incoherent Motion; ms, millisecond.

Figure 11 A mouse study at 7.0 T with implanted tumor models shows an increase of the diffusion gradient separation time (effective diffusion time) was associated with an increase of PF and a decrease of a D_slow variant. The D_slow variant was calculated considering the non-Gaussian diffusion kurtosis model. The data are from the report by Iima et al. (33). HCC, hepatocellular carcinoma; ms, millisecond; PF, perfusion fraction.

Figure 12 A mouse study at 11.7 T for brain tissue shows an increase of the diffusion gradient separation time (diffusion time) was associated with an increase of PF and a decrease of D_slow. The data were fitted with a triexponential model. The data are from the report by Wu and Zhang (34). PF, perfusion fraction; ms, millisecond.

Where F_fast reflects the compartment portion associated with shorter T2 and F_slow reflects the compartment portion associated with longer T2, and (F_fast + F_slow) =1. For F_fast, to be consistent with existing literature, we continue to use the abbreviation PF. Thus, PF in this letter may also represent the fast signal decay fraction associated with shorter T2 rather than the actual PF. It can be seen that, if all other variables can be fixed, then a decrease in shorter T2 corresponds to an increase in PF (if only shorter T2 and PF are the variables), and an increase in longer T2 corresponds to an increase in D_slow (if only longer T2 and D_slow are the variables). However, since a change in shorter T2 or longer T2 can affect other variables such as S₀ and S_(b), only experimental studies can further confirm the changes of IVIM parameters caused by T2 change. Note that, during IVIM analysis, we observed that the perfusion compartment and the diffusion compartment are mutually constrained (35-40). For example, the iron deposition and the resulting shorter T2 lead to lower D_slow and artificially higher PF and D_fast in older subjects’ liver and spleen than in younger subjects’ liver and spleen (35,40). And this may explain younger men’s liver has lower D_slow and higher PF than younger women’s liver. Due to the menstrual cycle, pre-menopausal women have lower liver iron level than men. The mutually constrained fast component measure and slow component measure suggests the ratio S_(b)/S₀ is indeed at least to a degree ‘stable’. If S_(b)/S₀ is ‘more stable’ or ‘with lesser change’, then Eq. [5] suggests fast component measure and slow component measure can be negatively correlated (such as an increase of PF can be compensated by a decrease of D_slow so that S_(b)/S₀ will be stable).

ADC is the composite of perfusion metrics (PF and D_fast) and diffusion metric (F_slow and D_slow). If the 1st b-value is 0, and b₂ is the 2nd b-value, then from Eq. [2] and Eq. [4], ADC calculation can be approximated according to (ignoring the difference between T2_shorter and T2_longer):

ADC=Dslow+Dfast−b2−1ln[1−Fslow+Fslowe−b2⋅(Dfast)][6]

Eq. [6] shows ADC is positively correlated with D_slow and D_fast, and negatively correlated with F_slow (thus, positively correlated with PF).

Figure 5 illustrates that, for tissues with aggregate T2 value <60 ms, if T2 increases from 40 to 60 ms, fast compartment measure decreases while slow compartment measure increases (if we do not consider other contributing factors); however, the decreased measure of fast compartment dominates and the net effect for the composite ADC is a decreased measure. Despite the pathohistological evidence of groupwise higher vasculature in hepatocellular carcinoma (HCC), HCC has been measured paradoxically with a lower PF by IVIM (14). For tissues with aggregated T2 value >80 ms, if T2 decreases from 170 to 90 ms, fast compartment measure increases while slow compartment measure decreases (if we do not consider other contributing factors); however, the decrease of the slow compartment dominates and the net effect for the composite ADC is a decreased measure. The observation as shown in Figure 5 can explain many of the apparently paradoxical observations for ADC measures. For example, articular cartilage has a high ADC of around 1.5×10⁻³ mm²/s and a T2 of around 37 ms at 3.0 T [see discussion in (4)], thus its high ADC is likely due to its dominant shorter T2 compartment. Chondrosarcoma has a high ADC of around 2.3×10⁻³ mm² and a T2 of around 120 ms [see discussion in (4)], its high ADC measure is likely due to its dominant longer T2 compartment. In the cases of liver lesions, HCC ADC has been noted to have restricted diffusion relative to the liver. However, as HCCs are mostly associated with increased blood supply and increased proportion of arterial blood supply and higher water content (i.e., edema, as shown with higher signal on T2-weighted image and with lower density on X-ray computed tomography), it is unlikely that HCC has true lower diffusion, instead the lower HCC ADC is due to the longer T2 of HCC relative to the liver. Schmid-Tannwald et al. (41) reported that the mean ADC value of hypervascular liver metastases was paradoxically lower than the mean ADC value of hypovascular metastases. This could be due to the fact that hypervascular liver metastases have a higher proportion of the longer blood T2 contributed to the lower ADC measure [blood has longer T2 than that of liver metastases, see discussion in (14)].

In addition to T2 (which is often dominant), ‘true tissue diffusion’ indeed contributes to ADC (2). This is also evidenced by the success of the diffusion tensor imaging technique. When a cluster of voxels has a homogeneous T2 value, then their dominant diffusion direction can still be measured. In addition to the proportions of shorter T2 compartment and longer T2 compartment and ‘true tissue diffusion’, other factors such as ‘true vessel volume’ and macroscopic motion also contribute to the composite ADC measure. In an IVIM study of uterine fibroid, after continuous intravenous infusion of oxytocin which is known to decrease uterine fibroid blood flow, Sainio et al. (42) reported that all three IVIM parameters (i.e., PF, D_slow, D_fast) of the uterine fibroid decreased. In the case of kidney, kidney ADC is higher than can be predicted by T2, this could be due to the kidney being associated with higher true diffusion and higher true vessel volume [see discussion in (2)]. Kidney medulla and cortex have a long T2 of around 138 and 121 ms respectively at 3 T (2,43). Recently, Stabinska et al. (44) reported that, by increasing the diffusion gradient separation time, the measured kidney medulla and cortex PF both increased, while the measured kidney medulla and cortex D_slow both decreased, and the net ADC increased (Figure 13). In the case of liver aging, there is a shortening of liver T2 (45), a decrease of liver D_slow (35), and an artificial increase of liver PF and D_fast (35). However, the net liver ADC still decreases (46). There could be a decrease of liver true diffusion with aging, and note that there is also a decrease of true vessel volume with aging as measured by histology (47) and by the perfusion metric diffusion derived vessel density (DDVD) (35).

Figure 13 IVIM study at 3.0 T shows, when the effective diffusion time (diffusion gradient separation time) was increased for kidney medulla DWI data acquisition, PF increased, D_slow decreased, and ADC increased. The kidney cortex diffusion metrics showed the same pattern. Data are from Stabinska et al. (44). ADC, apparent diffusion coefficient; DWI, diffusion weighted imaging; IVIM, Intravoxel Incoherent Motion; ms, millisecond; PF, perfusion fraction.

This explanation as shown in Figure 5 may not apply to in vitro studies with phantoms. For example, in a study with phantom, which might have a homogeneous T2 value, Laubach et al. (48) did not show a very low ADC measure for the solution with T2 of around 70 ms. Moreover, body fluids also have no perfusion element and their T2 may be ‘more homogeneously long’. For the case of gallbladder fluid, ADC is measured higher than can be predicted from T2 (Figure 1). Liver cysts are noted to have a higher ADC value than liver hemangioma, though liver hemangiomas are associated with blood flow inside the lesion [for T2 and ADC values see Figure 14 in (49), also see (50-52)]. Note that liver hemangiomas have a very high DDVD measure while the DDVD of liver cysts is close to zero when properly measured (49,53).

Figure 14 Absolute MR DWI signal intensity (arbitrary unit/pixel) of liver on b=2 images and its correlation with D_slow and PF measure. Five cirrhotic livers’ images were acquired at 3 T with 16 b-values of 0, 2, 5, 10, 15, 20, 25, 30, 40, 60, 80, 100, 150, 200, 400, and 600 s/mm², analyzed by segmented fitting and threshold b-value of 60 s/mm², with fitting started from b=2 s/mm² images (b=0 image excluded). Stronger liver signal, which can be seen as equivalent to longer T2, is associated with lower PF and higher D_slow. Adapted from Xiao and Wáng (37). DWI, diffusion weighted imaging; MR, magnetic resonance; ms, millisecond; PF, perfusion fraction.

In this letter, we discuss as if ADC is calculated with two b-values with the low b-value being 0. However, regardless of whether ADC is calculated with b=0 or without b=0 (such as using b=50 and b=800 s/mm²), or with three b-values (such as using b=0, b=50, and b=800 s/mm²), the pattern of triphasic relationship between T2 and ADC always exists (2,4), and the same is true for the relationship between T2 and IVIM parameters (Figure 14) (37). If the first b-value to calculate the ADC is high, then ADC will be more equivalent to IVIM-D_slow. However, D_slow values do not appear to be reasonable as well. For example, in a review article by Englund et al. (54), it was noted that skeletal muscle has a D_slow of 1.46±0.30 mm²/s which is much higher than the liver D_slow of 1.1 mm²/s (20,22). We would think that the D_slow of skeletal muscles will not be higher than that of liver with the liver more richly perfused by hepatic artery and portal vein and with lots of sinusoids and space of Disse. Majority of literature reported a lower D_slow in HCC tissue than in liver parenchyma (22,55). However, HCC is associated with faster blood transit time and higher free water content than liver parenchyma. Liver fibrosis is associated with longer T2 (2,56), and this longer T2 may depressed PF measure and promote D_slow measure. We have commented that IVIM measured liver D_slow may be too high for severe liver fibrosis patients (39). Both the ‘shorter T2 compartment’ and ‘the longer T2 compartment’ consist of a spectrum of varying T2 elements. Though we describe a triphasic dependency of ADC on T2 relaxation time, it is possible that it is actually a biphasic relationship (2). However, due to the ever-existing measurement imprecision and other factors contributing to the ADC, the exact valley bottom of T2 cannot be located. When T2 is around 70 ms, we can assume that the PF is very low due to the slower signal decay compared to that when T2 is shorter (such as 30 ms), on the other hand the D_slow is also sufficiently low when T2 is 70 ms.

Note that this letter is not a systematic review. Associated with various data processing approaches, the current IVIM literature is highly heterogeneous with varying degrees of data quality. For the fast compartment, the discussion in this letter has been mainly on PF, rather than D_fast, as D_fast is generally more difficult to quantify reliably (16,22). Note that, F_slow = (1 − PF), and F_slow is mostly >0.80 in perfusion/diffusion IVIM observations. In the case of liver, which is richly perfused, its PF is around 20% (22). Another point is the discussions on T2 relaxation time in this letter are mostly based on 3-T data.

Diffusion concepts developed from in vitro studies may not be applicable to in vivo phenomena with heterogeneous T1 and T2 elements. As shown in Figures 1-3, in vivo ADC measure is more contributed by T2 than by true tissue diffusion, thus we suggest that we do not routinely use the term ‘diffusion restriction’ when interpreting clinical high b-value DW images and ADC maps. Instead, we may choose to use the term ‘high signal’ on high b-value DW image and ‘low signal’ on ADC map.

Acknowledgments

None.

Footnote

Funding: This work was supported by Hong Kong GRF Project (No. 14112521).

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-195/coif). Y.X.J.W. serves as the Editor-in-Chief of Quantitative Imaging in Medicine and Surgery. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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