The relevance of T2 relaxation time in interpreting MRI apparent diffusion coefficient (ADC) map for musculoskeletal structures
Editorial

The relevance of T2 relaxation time in interpreting MRI apparent diffusion coefficient (ADC) map for musculoskeletal structures

Yi Xiang J. Wáng1, Maria Pilar Aparisi Gómez2,3,4, Fernando Ruiz Santiago5,6, Alberto Bazzocchi7

1Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; 2Department of Radiology, Auckland District Health Board, Auckland, New Zealand; 3Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand; 4Department of Radiology, IMSKE, Valencia, Spain; 5Department of Radiology and Physical Medicine, Faculty of Medicine, University of Granada, Granada, Spain; 6Musculoskeletal Radiology Unit, Hospital Universitario Virgen de Las Nieves, Granada, Spain; 7Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy

Correspondence to: Yi Xiang J. Wáng, MMed, PhD. Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong SAR, China. Email: yixiang_wang@cuhk.edu.hk.

Keywords: Magnetic resonance imaging (MRI); apparent diffusion coefficient (ADC); T2 relaxation time; musculoskeletal

Submitted Oct 04, 2023. Accepted for publication Oct 17, 2023. Published online Oct 20, 2023.

doi: 10.21037/qims-23-1392

Random movement (“molecular diffusion”) of particles comes from the thermal energy that they possess at any given temperature above absolute zero. A self-diffusion coefficient of around 2.3×10−3 mm2/s has been demonstrated earlier by a sample that contains small molecules, for example, water, at approximately 25 ℃ (room temperature) (1). This motion of water molecules can be hampered by the presence of cell membranes and macromolecules, and the in vivo organ apparent diffusion coefficients (ADCs) measured by magnetic resonance imaging (MRI) are expected to be smaller than in vitro water phantom value. On the other hand, in vivo organ ADC is also contributed by tissue perfusion. In body water, such as the case of gallbladder, ADC is measured to be around 3×10−3 mm2/s (2), which is affected by the body temperature, composition of bile fluid, as well as the body bulk motion due to respiration and cardiovascular pulsating, etc.

ADC values of some in vitro phantom results, in vivo muscle, cartilage, intervertebral disc NP and IAF (nucleus pulposus and inner annulus fibrosus) (3), and bone marrow are listed in Table 1 (4-20). Liver, the largest solid organ in the body, has an ADC of around 1.07×10−3 mm2/s (6), and also considering free water has an in vitro ADC of around 2.2×10−3 mm2/s (4,6), we intuitively feel that the ADCs of cartilage (around 1.5×10−3 mm2/s) and disc NP and IAF (around 1.9×10−3 mm2/s) are ‘unrealistically high’. Compared with the liver ADC of 1.07×10−3 mm2/s and spleen ADC of 0.8×10−3 mm2/s (6), muscle ADC, being around 1.55×10−3 mm2/s, also appears to be high. Recently, Wáng et al. (21-25) proposed that in vivo ADC measure is strongly associated with T2 relaxation time (T2 time) [Table 2, Figure 1 (26-44)]. Wáng et al. (24) divide T2 time into short T2 time band (<60 ms), intermediate T2 time band (60–80 ms), and long T2 time band (>80 ms, all 3 T values). For the short T2 time band, there is a negative correlation between T2 time and ADC. For the long T2 time band, there is a positive correlation between T2 time and ADC. A tissue likely measures a low ADC if its T2 time is close to 70 ms. The phenomenon shown in Figure 1 can help explain the counterintuitive ADC values commonly seen in a number of musculoskeletal tissues.

Table 1

A list of ADC values of some phantoms, muscle, cartilage, intervertebral disc NP and IAF, and bone marrow

Authors Materials/tissues Mean ADC (×10−3 mm2/s) Magnet# b value (s/mm2)§§
Kalaitzakis et al. (4) Water phantom 2.20 1.5 T 0–1,500, 10 b values
Kalaitzakis et al. (4) 5% sucrose solution phantom 1.88 1.5 T 0–1,500, 10 b values
Gatidis et al. (5) Water phantom 2.15 3.0 T 0–1,000, 10 b values
Gatidis et al. (5) Polyethylene glycol (10 mM) phantom 1.86 3.0 T 0–1,000, 10 b values
Kim et al. (6) Liver## 1.07 3.0 T 0, 800
Kim et al. (6) Spleen 0.79 3.0 T 0, 800
Sandberg et al. (7) Muscles (11.2 years) 1.48 3.0 T 50, 600
Chen et al. (8) Paraspinal muscle (57.0 years) 1.55 3.0 T 50, 800
Padhani et al. (9) Psoas muscle 1.39 1.5 T 50, 800 (or 900)
Raya et al. (10) Muscle (review) 1.60 3.0 T
Zbýň et al. (11) Knee articular cartilage 1.90 3.0 T 50, 500, 100
Ukai et al. (12) Knee articular cartilage (51.5 years) 1.40 3.0 T 0, 600
Raya et al. (10) Articular cartilage (review) 1.50 3.0 T
Hamaguchi et al. (13) Disc NP and IAF (33.4 years)¶¶ 1.78 3.0 T 0, 1,000
Shen et al. (14) Disc NP and IAF (24.3 years)¶¶ 1.99 1.5 T 0, 800
Niu et al. (15) Non-degenerated NP and IAF (20–29 years) 2.16 1.5 T 0, 500
Niinimäki et al. (16) Non-degenerated NP (49 years) 1.65 1.5 T 0, 500
Sandberg et al. (7) Red bone marrow (11.2 years)§ 0.86 3.0 T 50, 600
Padhani et al. (9) Red bone marrow§ 0.68 3.0 T 50, 800 (or 900)
Zbýň et al. (11) Bone marrow (knee)§ 0.53 3.0T 50, 500, 100
Padhani et al. (9) Yellow bone marrow§ 0.38 1.5 T 50, 800 (or 900)
Byun et al. (17) Sacrum yellow bone marrow (70 years)§ 0.21 1.5 T 0, 650
Raya et al. (10) Bone marrow (review)§ 0.45 3.0 T –

#, it is generally considered that diffusion is per se not a nuclear magnetic resonance phenomenon. Magnetic field strength should have little impact on ADC values measured (18,19); ##, older age is commonly associated with higher liver iron content and higher fat content, both can lead to lower ADC measure (20); , muscle fascia contain fat. In a defined muscle region, fat portion in the fascia may increase in older subjects, and this leads to lower muscle ADC measure; ¶¶, discs of mixed degeneration grading; §, bone marrow ADC depends on the ratio of red marrow to yellow marrow. The data of Sandberg et al. (7) may be closer to pure red marrow ADC; §§, ADC measure is affected by b value selection during data acquisition and the noise levels, however, besides muscle, perfusion contribution to ADC is small for most of the skeletal tissues. ADC, apparent diffusion coefficient; NP, nucleus pulposus; IAF, inner annulus fibrosus.

Table 2

A list of T2 time values of some musculoskeletal structure and disorders and tumors of the body and brain

Authors Tissues Mean T2 (ms) Magnet# (T)
Wall et al. (26) Liver 45 0.35
de Bazelaire et al. (27) Liver (31.5 years) 34 3.0
de Bazelaire et al. (27) Liver (31.5 years) 46 1.5
Bogaert et al. (28) Liver (47.1 years) 46 1.5
Wall et al. (26) Muscle 29 0.35
de Bazelaire et al. (27) Paravertebral muscle (31.5 years) 29 3
Lang et al. (29) Leg muscle in rat 33 2.0
Pettersson et al. (30) Muscle 32 0.15
Gold et al. (31) Muscle (27–38 years) 32 3.0
Gold et al. (31) Muscle (27–38 years) 35 1.5
Raya et al. (10) Muscle (review) 32 3.0
Gold et al. (31) Knee articular cartilage (27–38 years) 37 3
Gold et al. (31) Knee articular cartilage (27–38 years) 42 1.5
Roth et al. (32) Knee articular cartilage (16 years) 38 3.0
Ukai et al. (12) Knee articular cartilage (51.5 years) 40 3.0
Ruiz Santiago et al. (33) Patellar cartilage (16–45 years) 41 1.5
Raya et al. (10) Articular cartilage (review) 37 3.0
Niu et al. (15) Non-degenerated discs NP and IAF (20–29 years) 164 1.5
Wang et al. (34) Non-degenerated discs NP and IAF (32 years) 130 3.0
Yang et al. (35) Non-degenerated discs NP and IAF (44 years) 138 3.0
Stelzeneder et al. (36) Non-degenerated discs NP (19 years) 238 3.0
Bouhsina et al. (37) Non-degenerated discs NP and IAF in dog 249 1.5
Wall et al. (26) Abscess various body sites 81 0.35
Pettersson et al. (30) Chondrosarcoma 120 0.15
Pettersson et al. (30) Malignant fibrous histiocytoma 92 0.15
Pettersson et al. (30) Osteogenic sarcoma 75 0.15
Lang et al. (29) Osteogenic sarcoma—rat model 73 2.0
Arita et al. (38) Active prostate cancer bone metastasis 82 3.0
Jung et al. (39) Breast cancer 90 3.0
Baohong et al. (40) Parotid gland cancer 97 3.0
Hepp et al. (41) Prostate cancer 80 3.0
Gu et al. (42) Grade II glioma 164 3.0
Gu et al. (42) High-grade glioma 127 3.0
Oh et al. (43) Gliomas 160 1.5
Oh et al. (43) Meningiomas/metastases 125 1.5

Data from tumors of the body and brain represent a few random selections for illustration only. #, there is a notion that T2 time does not change much over the range of field strengths used for routine clinical MR imaging (0.2 to 3.0 T) (44); , the value of 34 ms for liver at 3.0 T is likely underestimated, i.e., liver T2 time at 3.0 T may be longer. NP, nucleus pulposus; IAF, inner annulus fibrosus; MR, magnetic resonance.

Figure 1 Relationship between T2 time and ADC at 3 T. The graph is initially from Wáng and Ma (24). Data sources for liver, spleen, parotid gland tumors, and prostate also see Wáng and Ma (24). T2 time for the liver is assumed to be 42 ms (Table 2). Data points for muscle, cartilage, and intervertebral disc are newly added (values based on Tables 1,2). There are large variations for reported intervertebral disc T2 time and ADC values, thus mean values for the discs are presented simplistically (the data from dog not counted). For data with T2 time <60 ms, there is a negative correlation between T2 time and ADC. For data with T2 time >80 ms, there is a positive correlation between T2 time and 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. ADC, apparent diffusion coefficient.

In experimental studies, it was suggested that the hepatic blood volume including that of the large vessels is about 25 mL/100 g, whereas this value is 3 mL/100 g in skeletal muscle (45). Though we would think that the ADC of 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, however, muscles have a shorter T2 time than the liver (Table 2). In the study of Wall et al. (26), muscle measured an ADC of 29 ms whereas liver measured an ADC of 45 ms at 0.35 T. In the study of de Bazelaire et al. (27), muscle measured an ADC of 29 ms whereas liver measured an ADC of 46 ms at 1.5 T. The phenomenon as demonstrated in Figure 1 shows, with liver data as the reference, the shorter T2 time of muscles is associated with an increased ADC value for the muscle (relative to the liver). Figure 1 also helps to explain that cartilage and disc NP and IAF measure very high ADC not because these tissues have true high tissue diffusivity, but instead because of their T2 times being both away from the intermediate T2 time band of 60–80 ms (at 3 T). Moreover, cartilage and disc NP and IAF demonstrate high ADC due to the opposite reasons, with cartilage having a relatively short T2 time and non-degenerated disc NP and IAF having a long T2 time (Table 2).

A few musculoskeletal lesions also demonstrate unusual ADC values. Pyogenic abscess fluid (i.e., pus) tends to demonstrate a very low ADC (e.g., 0.63×10−3 mm2/s) regardless of the location of the abscess (46-49). It is counterintuitive that abscess pus, being fluid or semi-fluid, has a very low ADC measure. Recently Wáng noted that (25), abscess pus having a T2 time of about half that of body water (around 80 ms) contributes to very low ADC measured by MRI [Figure 1, Table 3 (9,17,41,43,48-59)]. Abscess pus may not have truly restricted diffusion compared with many other in vivo solid tissues. Morán et al. (50) and Einarsdóttir et al. (51) reported myxoma ADC values of 2.38×10−3 and 2.80×10−3 mm2/s respectively, which are quite high. Quantitative data on T2 time for musculoskeletal myxoid remain limited, however, it is known that myxoid substance has a long T2 time (as noted with bright signal on T2 weighted images). Myxoma has a high ADC likely due to myxoid substance’s long T2 time. Another disease type is chondrosarcoma. Chondrosarcoma has a long T2 time (e.g., 120 ms) and high ADC measure [e.g., 2.3×10−3 mm2, Table 3 (53)]. It is unlikely that chondrosarcoma has a true high tissue diffusivity. T2 shine-through refers to high signal on diffusion weighted images that is not due to restricted diffusion, but rather to long T2 time in some tissue or body fluid (52). It is considered that this T2 shine-through error can be avoided with assessment of the high b value images and the corresponding ADC map. The ADC map is considered to have corrected the T2 shine-through (52). Thus, the ADC measure of lesions such as myxoma cannot be explained by the T2 shine-through effect.

Table 3

A list of ADC values of some musculoskeletal disorders and tumors of the body and brain

Authors Materials/tissues Mean ADC (×10−3 mm2/s) Magnet# b value (s/mm2)§§§
Subhawong et al. (48) Abscess in musculoskeletal soft tissue§ 0.63 3.0 T 50, 400, 800
Erdogan et al. (49) Abscess in brain 0.69 1.5 T 0, 1,000
Morán et al. (50) Myxoma 2.38 1.5 T 0,300, 600, 1,000
Einarsdóttir et al. (51) Myxoma 2.80 1.5 T 0, 600
Subhawong et al. (52) Myxoid liposarcoma§ 2.31 Unknown Unknown
Hayashida et al. (53) Chondrosarcoma 2.29 1.5 T 0, 500, 1,000
Ahlawat et al. (54) Enchondroma§§ 1.80 3.0 T 50, 400, 800
Subhawong et al. (48) Ewings sarcoma§ 0.80 3.0 T 50, 400, 800
Ahlawat et al. (54) Osteosarcoma## 0.80 3.0 T 50, 400, 800
Feuerlein et al. (55) Soft-tissue tumors (mixed)## 0.85 1.5 T 0, 150, 500, 1,000
Padhani et al. (9) Multiple myeloma 0.88 1.5 T 50, 800 (or 900)
Padhani et al. (9) Breast cancer bone marrow Met 0.94 1.5 T 50, 800 (or 900)
Byun et al. (17) Sacrum Met (mixed) 0.78 1.5 T 0, 650
Balliu et al. (56) Vertebral malignancies (mixed) 0.92 1.5 T 0, 500
Feuerlein et al. (55) Liver malignancies (mixed)## 0.81 1.5 T 0, 150, 500, 1,000
Feuerlein et al. (55) Colon/rectum malignancies (mixed)## 0.92 1.5 T 0, 150, 500, 1,000
Feuerlein et al. (55) Uterus/ovaries malignancies (mixed)## 0.77 1.5 T 0, 150, 500, 1,000
Feuerlein et al. (55) Skeletal Met (mixed)## 0.81 1.5 T 0, 150, 500, 1,000
Hepp et al. (41) Prostate cancer 0.76 3.0 T 50, 500, 1,000, 2,000
Surov et al. (57) Breast cancer liver Met 0.86 1.5 T 0, 600
Thormann et al. (58) Hepatocellular carcinoma 0.93 1.5 T 0, 500
Oh et al. (43) Gliomas 1.28 1.5 T 0, 1,000
Oh et al. (43) Meningiomas/Met 1.10 1.5 T 0, 1,000
Stadnik et al. (59) Gliomas 1.14 1.5 T 0, 300, 1,200

Data from tumors of the body and brain represent a few random selections for illustration only. #, it is generally considered that diffusion is per se not a nuclear magnetic resonance phenomenon. Magnetic field strength should have little impact on ADC values measured (18,19); §, result of a single case only; §§, mineralization leading to lower ADC measures; ##, with limited case number; §§§, ADC measure is affected by b value selection during data acquisition and the noise levels, however, besides muscle, perfusion contribution to ADC is small for most of the skeletal tissues. ADC, apparent diffusion coefficient; Met, metastasis.

The analyses above further support that T2 time is a dominant contributor to ADC measure (24), and call for re-consideration on whether cellularity or high cell density contributes to tumor ADC. It has been perceived that malignant tissues’ ADC is associated with malignant tissues’ being generally more cellular than benign tissues and extra-cellular water molecule diffusion in these tissues is lower with anarchic cellular proliferation. While it is possible that a more malignant tumor will deviate more from the native tissue in composition, thus show more deviation in T2 time from native tissue, and thus so does ADC measure, some studies did not report a correlation between ADC measure and cellularity (59-63). For example, Sadeghi et al. (60) noted that ‘This study, which takes into account the regional heterogeneity of gliomas, does not confirm the inverse correlation between ADC and cell density reported in previous studies. This finding underlines the impact of other determinants of water diffusivity within the complex microenvironment encountered in gliomas. As previously reported, edema, necrosis, and extracellular matrix components constitute some of such parameters that may influence ADC values within gliomas.’ The study of Rosenkrantz et al. (61) on pancreatic cancer showed no associations between ADCs of pancreatic adenocarcinoma and tumour grade or other adverse pathological features. Nonomura et al. (62) reported that there was no ADC difference between normal hematopoietic cell bone marrow without fat infiltration and lymphoma-related hypercellular bone marrow, despite lymphoma tissue had more compacted cells. Table 3 shows ADCs of myeloma or metastatic malignancies in the bone do not demonstrate major ADC difference with other cancerous tissues such as liver malignancies, colon/rectum malignancies, uterus/ovaries malignancies, and prostate cancer. Brain tumors tend to have a relatively higher T2 time and a relatively higher ADC measure. For most of the tumors originated in the liver or pancreas, with increased T2 time which shifts toward 70 ms, these tumors have a reduced ADC relative to native tissues. For prostate cancer with a decreased T2 time which shifts toward 70 ms, prostate cancer also has a reduced ADC relative to the native tissue. With T2 time shifting away from 70 ms, brain tumors mostly are associated with increased ADC (24). For soft tissue tumours, Einarsdóttir et al. (51) reported ADC values of benign soft tissue tumours and sarcomas overlapped and could not be used to differentiate between the bulk of benign and malignant tumours. Maeda et al. (64) also reported that ADCs of benign and malignant soft-tissue tumors were not significantly different. Balliu et al. (56) reported that ADC does not help differentiate spine malignancy from spine infection. Razek et al. (65) reported that soft-tissue malignant tumors tend to have a lower mean ADC value than soft-tissue benign tumors. However, there was huge variation among individual cases depending on the histopathological types. An injection of gadolinium contrast agent, which will shorten T2 time of the tissues, has also been reported to be associated with a lower ADC measure (66,67) without the gadolinium contrast agent actually changing the diffusivity of the tissues. For the cases of prostate cancer and breast cancer, gadolinium agent will slightly shift the T2 times of these tissues toward 70 ms. It is also likely that the in vivo measurement of ADC is contaminated by bulk motion due to physiological motion (such as respiration and cardiovascular pulsing) and the vibration of MR scanner gradients during diffusion data acquisition. The contribution of cellularity to ADC may be of only minor importance in practice.

Another phenomenon of note is the so-called susceptibility T2* ‘black-out’, which is seen with structures having a very short intrinsic T2 signal due to very short T2* associated with iron or calcium content (68,69). It is known that hematomas can have a low ADC value. Susceptibility artifacts such as hemorrhage containing deoxyhemoglobin or hemosiderin result in unreliable ADC value calculations with pseudo-low ADC values (68,70,71). Assessing osteosarcoma or osteoblastic bone metastases can also be challenging sometimes due to the presence of this phenomenon (72).

The analyses in this article re-emphasize the notion that, for interpretation of ADC value of any tissue, this tissue’s T2 time should be always referred (24). Moreover, some authors reported that ADC does not offer superiority over T2 time in a number of diagnostic analyses. For intracerebral tumors, Oh et al. (43) reported T2 values were more useful than ADC for characterizing contrast enhancing tumor and immediate-edema regions of glioma, meningiomas and metastases. Stadnik et al. (59) reported that the diffusion-weighted images and ADC maps of gliomas were less useful than the T2-weighted and contrast-enhanced T1-weighted images in definition of tumor boundaries. The ADC values of solid gliomas, metastases, and meningioma were in the same range. In a study of glioma patients, Kinoshita et al. (73) reported that ADC was unable to show a significant correlation with 11C-methionine uptake (as shown on positron emission tomography) or with tumor cell density; however, a combination of T1 and T2 relaxation time correlated both with methionine uptake and tumor cell density. Cieszanowski et al. (74) reported significantly higher sensitivity and accuracy of T2 time than ADC for diagnosing hepatic malignancy. ADC maps may suffer from alignment errors between images of different b values and also low signal-to-noise ratio from diffusion-weighted imaging. Within the framework of diffusion weighted imaging, a number of pitfalls have also been noted with intravoxel incoherent (IVIM) analysis (21,75-77). The additional benefits of ADC over T2 time or signal intensity on properly T2 weighted images should be carefully studied further for musculoskeletal application.

Acknowledgments

Funding: None.

Footnote

Conflicts of Interest: The authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1392/coif). YXJW serves as the Editor-in-Chief of Quantitative Imaging in Medicine and Surgery. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are 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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Cite this article as: Wáng YXJ, Aparisi Gómez MP, Ruiz Santiago F, Bazzocchi A. The relevance of T2 relaxation time in interpreting MRI apparent diffusion coefficient (ADC) map for musculoskeletal structures. Quant Imaging Med Surg 2023;13(12):7657-7666. doi: 10.21037/qims-23-1392

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