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November 2022 (1)

Emerging imaging techniques for acute pancreatitis

Journal Watch by Ioannis Christofilis

Emerging imaging techniques for acute pancreatitis

Saeed Ghandili, Shahab Shayesteh, Daniel F. Fouladi, Alejandra Blanco, Linda C. Chu

Abdom Radiol (NY). 2020 May;45(5):1299-1307.
doi: 10.1007/s00261-019-02192-z.

Acute pancreatitis (AP) is caused by acute inflammation of the pancreas and adjacent tissue. Current CT and MRI evaluation of AP, included in the revised Atlanta classification of AP, is based on morphologic imaging criteria [1].

Authors suggest that newer MR techniques such as diffusion-weighted imaging (DWI), T1 and T2 mapping can offer quantitative imaging features. DWI is a MR imaging technique that quantifies the diffusion of water molecules within biologic tissues. In AP, pancreatic inflammation and tissue injury restrict diffusion, therefore, the ADC (apparent diffusion coefficient) of AP has been reported to be significantly lower than that of normal pancreas [2,3]. Lower ADC values were also associated with increased severity of AP. DWI is also potentially useful in diagnosing necrotizing pancreatitis, as ADC values have been found to be significantly higher [4]. Finally, DWI has shown incremental value in identifying superimposed infection in pancreatic fluid collections, which have more restricted diffusion and lower ADC values relative to sterile collections [5].

Pancreatic disease states such as edema increase T1 relaxation time and decrease the signal intensity on T1-weighted images. The T1 relaxation time cannot be directly measured on traditional T1-weighted image. On the other hand, T1 mapping directly measures the T1 relaxation time of the region of interest, facilitates comparison of T1 relaxation time of patients with different pancreatic pathologies and allows serial assessment of T1 relaxation time to monitor disease progression and response to treatment, with a single breath hold [6]. Several studies performed at 3T have shown increased T1 relaxation time for patients with chronic pancreatitis (CP) and autoimmune pancreatitis (AIP) compared to normal controls. While there are no currently published reports detailing alterations in T1 relaxation time in patients with AP, based on these preliminary results, one would expect that T1 relaxation time should also be elevated in patients with AP. This needs to be validated in future studies.

Similar to T1 mapping, T2 mapping is a technique that can quantify the transverse relaxation time. Pancreatic edema and inflammatory changes in AP typically account for the observed increased signal intensity on T2-weighted images. T2 mapping offers the potential to quantify the degree of pancreatic edema and inflammatory changes, which may provide diagnostic and prognostic information [7]. A series of preliminary studies, by Vietti Violi et al. etc, showed that T2 mapping may provide quantitative biomarkers indicative of underlying pancreatic disease. These results need to be validated in future studies.

Advances in image post-processing and analytic techniques also offer opportunities for improved disease detection and classification. Both radiomics and artificial neural networks take advantage of the high-dimensional mineable data present in medical images. There is growing evidence that radiomics may provide useful imaging biomarkers for pancreatic tumor detection. A few studies have applied similar radiomics approaches to extract additional diagnostic and prognostic information in patients with AP. Lin et al. reported that contrast-enhanced MRI-based radiomics features were predictive of AP severity in a retrospective study of 259 patients with AP [8]. The results showed that the radiomics model was able to more accurately predict severity of AP in the early stage compared to some of the existing clinical models, as well as, predict complications related to AP, or recurrent episodes of AP.

Another emerging technology that has the potential to improve risk stratification and management in AP is the artificial neural network (ANN). ANNs pass the input values through a number of “hidden layers” of mathematical equations to develop a model that best fits the data. Most of the current literature on ANNs and AP focuses solely on clinical and laboratory data as input, while only one study by Keogan et al. incorporated both radiologic and laboratory data [9]. No published reports have directly used ANNs to extract quantitative imaging features from AP, however, there have been several recent exciting developments in applying ANNs to detect pancreatic ductal adenocarcinoma, which may be translatable to AP.

In conclusion, authors try to underline the significance of recent advances in image acquisition and analysis, which offer the opportunity to go beyond morphologic features. DWI, T1, T2 mapping, radiomics and ANNs, can potentially quantify signal changes reflective of underlying tissue abnormalities for detection and classification of AP, offering the promise of uncovering imaging biomarkers for additional classification and prognostic information.


1. Banks PA, Bollen TL, Dervenis C, Gooszen HG, Johnson CD, Sarr MG, et al. Classification of acute pancreatitis--2012: revision of the Atlanta classification and definitions by international consensus. Gut. 2013;62(1):102-11.
2. Shinya S, Sasaki T, Nakagawa Y, Guiquing Z, Yamamoto F, Yamashita Y. The efficacy of diffusion-weighted imaging for the detection and evaluation of acute pancreatitis. Hepato-gastroenterology. 2009;56(94-95):1407-10.
3. Thomas S, Kayhan A, Lakadamyali H, Oto A. Diffusion MRI of acute pancreatitis and comparison with normal individuals using ADC values. Emergency radiology. 2012;19(1):5-9.
4. de Freitas Tertulino F, Schraibman V, Ardengh JC, do Espírito-Santo DC, Ajzen SA, Torrez FR, et al. Diffusion-weighted magnetic resonance imaging indicates the severity of acute pancreatitis. Abdominal imaging. 2015;40(2):265-71.
5. Borens B, Arvanitakis M, Absil J, El Bouchaibi S, Matos C, Eisendrath P, et al. Added value of diffusion-weighted magnetic resonance imaging for the detection of pancreatic fluid collection infection. European radiology. 2017;27(3):1064-73.
6. Tirkes T, Lin C, Fogel EL, Sherman SS, Wang Q, Sandrasegaran K. T(1) mapping for diagnosis of mild chronic pancreatitis. Journal of magnetic resonance imaging : JMRI. 2017;45(4):1171-6.
7. Vietti Violi N, Hilbert T, Bastiaansen JAM, Knebel JF, Ledoux JB, Stemmer A, et al. Patient respiratory-triggered quantitative T(2) mapping in the pancreas. Journal of magnetic resonance imaging : JMRI. 2019;50(2):410-6.
8. Lin Q, Ji YF, Chen Y, Sun H, Yang DD, Chen AL, et al. Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity. 2020;51(2):397-406.
9. Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, et al. Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Academic radiology. 2002;9(4):410-9.

Dr. Ioannis Christofilis is a fifth-year Radiology Resident at ‘Konstantopouleion’ General Hospital of Nea Ionia, Athens, Greece. He completed his undergraduate Μedical degree at University of Athens, Medical School. His main fields of interests in the area of diagnostic imaging are Abdominal and Thoracic imaging, Cardiovascular imaging and Ultrasonography.    

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