Efforts have been made to employ the nuclear magnetic resonance (NMR)-biochemical correlation concept or a combination of MR imaging (MRI) and MR spectroscopy (MRS) as an established diagnostic tool for medical practice in clinical settings. Recent reviews and meta-analyses indicate the great possibility of using integrated multimodal multiparametric MRI and MRS for deep learning (DL) of soft-tissue pathophysiology, enabling improved decision-making and disease progression monitoring in precision medicine. Recent guidelines and clinical trials suggest the need for DL of the biophysical and biochemical nature of the brain, breast, prostate, liver, and heart tissue from digital spectromics analysis, along with other molecular imaging modalities. The current opinions, based on recent recommendations, available literature on evidence-based MR spectromics, clinical trials, and meta-analyses on high-resolution MRI and MRS suggest that utilizing MRI and MRS signals as theranostic biomarkers for various soft tissues can demonstrate NMR-biochemical correlation and employ MRI with MRS as adjunct real-time tools, generating robust, and fast tissue digital images with metabolic screening. The integration of DL features can aid in evaluating patient disease diagnosis and therapy within a clinical setting, considering the available medical practices and their limitations.
Sharma R, 1995, Studies on NMR Relaxation Times and NMR-Biochemical Correlation in Medicine. Ph. D Dissertation Submitted to AIIMS/Indian Institute of Technology, New Delhi.
Mountford CE, Doran SJ, Lean CL, et al., 2004, Proton MRS can determine the pathology of human cancers with a high level of accuracy. Chem Rev, 104: 3677–3704. https://doi.org/10.1021/cr030410g
Boada FE, Christensen JD, Huang-Hellinger FR, et al., 1994, Quantitative in vivo tissue sodium concentration maps: The effects of biexponential relaxation. Magn Reson Med, 32: 219–213. https://doi.org/10.1002/mrm.1910320210
Tkac I, Gruetter R, 2005, Methodology of 1H NMR spectroscopy of the human brain at very high magnetic fields. Appl Magn Reson, 27: 139–157. https://doi.org/10.1007/BF03166960
Geppert C, Dreher W, Leibfritz D, 2003, PRESS-based proton single-voxel spectroscopy and spectroscopic imaging with very short echo times using asymmetric RF pulses. MAGMA, 16: 144–148. https://doi.org/10.1007/s10334-003-0016-6
Seeger U, Klose U, Mader I, et al., 2003, Parameterized evaluation of macromolecules and lipids in proton MR spectroscopy of brain diseases. Magn Reson Med, 49: 19–28. https://doi.org/10.1002/mrm.10332
Kanowski M, Kaufmann J, Braun J, et al., 2004, Quantitation of simulated short echo time 1H human brain spectra by LCModel and AMARES. Magn Reson Med, 51: 904–912. https://doi.org/10.1002/mrm.20063
Sharma R, 2002, Serial amino-neurochemicals analysis in progressive lesion analysis of multiple sclerosis by magnetic resonance imaging and proton magnetic resonance spectroscopic imaging. Magn Reson Med Sci, 1: 169–173.
Grutter R, Rothman DL, Novotny EJ, et al., 1992, Detection and assignment of glucose signal in 1H NMR difference spectra of human brain. Magn Reson Med, 27: 183–188. https://doi.org/10.1002/mrm.1910270118
Rothman DL, Hanstock CC, Petroff OA, et al., 1992, Localized 1H NMR spectra of glutamate in human brain. Magn Reson Med, 25: 94–106. https://doi.org/10.1002/mrm.1910250110
Portais JC, Pianet J, Allard M, et al., 1991, Magnetic resonance spectroscopy and mretabolism. Applications of proton and 13C NMR study of glutamate metabolism in cultured glial cells and human brain in vivo. Biochemie, 73: 93–97. https://doi.org/10.1016/0300-9084(91)90080-k
Zia B, Bogia DP, 2020, Fast Fourier Transform and Convolution in Medical Image Reconstruction. Available from: https://www.intel.com/content/www/us/en/developer/ articles/technical/fast-fourier-transform-and-convolution-in-medical-image-reconstruction.html [Last accessed on 2019 Nov 30].
Iqbal Z, Nguyen D, Thomas MA, et al., 2021, Deep learning can accelerate and quantify simulated localized correlated spectroscopy. Sci Rep, 11: 8727.
Yang J, Lei D, Qin K, et al., 2021, Using deep learning to classify pediatric posttraumatic stress disorder at the individual level. BMC Psychiatry, 21: 535. https://doi.org/10.1186/s12888-021-03503-9
Balakrishnan R, Valdés Hernández MDC, Farrall AJ, 2021, Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review. Comput Med Imaging Graph, 88: 101867. https://doi.org/10.1016/j.compmedimag.2021.101867
Terpstra ML, Maspero M, Sbrizzi A, et al., 2022, A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning. Med Image Anal, 80: 102509. https://doi.org/10.1016/j.media.2022.102509
Chen D, Wang Z, Guo D, 2020, Review and prospect: Deep learning in nuclear magnetic resonance spectroscopy. Chemistry, 26: 10391–10401. https://doi.org/10.1002/chem.202000246
Li X, Strasser B, Neuberger U, et al., 2022, Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma. Neurooncol Adv, 4: vdac071. https://doi.org/10.1093/noajnl/vdac071
Migdadi L, Lambert J, Telfah A, et al., 2021, Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR). Comput Struct Biotechnol J, 19: 5047–5058.
Sarma MK, Nagarajan R, Macey PM, et al., 2014, Accelerated echo-planar J-resolved spectroscopic imaging in the human brain using compressed sensing: A pilot validation in obstructive sleep apnea. AJNR Am J Neuroradiol, 35: S81–S89. https://doi.org/10.3174/ajnr.A3846
Marshall I, Thrippleton MJ, Bastin ME, et al., 2018, Characterisation of tissue-type metabolic content in secondary progressive multiple sclerosis: A magnetic resonance spectroscopic imaging study. J Neurol, 265: 1795–1802. https://doi.org/10.1007/s00415-018-8903-y
Filippi M, Rocca MA, Rovaris M, 2002, Clinical trials and clinical practice in multiple sclerosis: Conventional and emerging magnetic resonance imaging technologies. Curr Neurol Neurosci Rep, 2: 267–276. https://doi.org/10.1007/s11910-002-0086-2
Zuo J, Joseph GB, Li X, et al., 2012, In vivo intervertebral disc characterization using magnetic resonance spectroscopy and T1ρ imaging: Association with discography and Oswestry Disability Index and Short Form-36 Health Survey. Spine (Phila Pa 1976), 37: 214–221. https://doi.org/10.1097/BRS.0b013e3182294a63
Tartaglia MC, Arnold DL, 2006, The role of MRS and fMRI in multiple sclerosis. Adv Neurol, 98: 185–202.
Miller DH, Albert PS, Barkhof F, et al., 1996, Guidelines for the use of magnetic resonance techniques in monitoring the treatment of multiple sclerosis. US National MS Society Task Force. Ann Neurol, 39: 6–16. https://doi.org/10.1002/ana.410390104
Binesh N, Yue K, Fairbanks L, et al., 2002, Reproducibility of localized 2D correlated MR spectroscopy. Magn Reson Med, 48: 942–948. https://doi.org/10.1002/mrm.10307
Jung JA, Coakley FV, Vigneron DB, et al., 2004, Prostate depiction at endorectal MR spectroscopic imaging: Investigation of a standardized evaluation system. Radiology, 233: 701–708. https://doi.org/10.1148/radiol.2333030672
Boesch SM, Wolf C, Seppi K, et al., 2007, Differentiation of SCA2 from MSA-C using proton magnetic resonance spectroscopic imaging. J Magn Reson Imaging, 25: 564–569. https://doi.org/10.1002/jmri.20846
Kahleova H, Petersen KF, Shulman GI, et al., 2020, Effect of a low-fat vegan diet on body weight, insulin sensitivity, postprandial metabolism, and intramyocellular and hepatocellular lipid levels in overweight adults: A randomized clinical trial. JAMA Netw Open, 3: e2025454. https://doi.org/10.1001/jamanetworkopen.2020.25454
Smits M, 2021, MRI biomarkers in neuro-oncology. Nat Rev Neurol, 17: 486–500. https://doi.org/10.1038/s41582-021-00510-y
Galanaud D, Haik S, Linguraru MG, et al, 2010, Combined diffusion imaging and MR spectroscopy in the diagnosis of human prion diseases. AJNR Am J Neuroradiol, 31: 1311–1318.https://doi.org/10.3174/ajnr.A2069
Reardon DA, Ballman KV, Buckner JC, et al., 2014, Impact of imaging measurements on response assessment in glioblastoma clinical trials. Neuro Oncol, 16 Suppl 7: vii24–35. https://doi.org/10.1093/neuonc/nou286
Auer DP, 2009, In vivo imaging markers of neurodegeneration of the substantia nigra. Exp Gerontol, 44: 4–9. https://doi.org/10.1016/j.exger.2008.08.051
Bulik M, Kazda T, Slampa P, et al., 2015, The diagnostic ability of follow-up imaging biomarkers after treatment of glioblastoma in the temozolomide era: Implications from proton MR spectroscopy and apparent diffusion coefficient mapping. Biomed Res Int, 2015: 641023. https://doi.org/10.1155/2015/641023
Lombardo F, Frijia F, Bongioanni P, et al., 2009, Diffusion tensor MRI and MR spectroscopy in long lasting upper motor neuron involvement in amyotrophic lateral sclerosis. Arch Ital Biol, 147: 69–82.
Griffiths JR, Tate AR, Howe FA, et al., 2002, Magnetic Resonance Spectroscopy of cancer-practicalities of multi-centre trials and early results in non-Hodgkin’s lymphoma. Eur J Cancer, 38: 2085–2093. https://doi.org/10.1016/s0959-8049(02)00389-1
Jacobs MA, Stearns V, Wolff AC, et al., 2010, Multiparametric magnetic resonance imaging, spectroscopy and multinuclear (²³Na) imaging monitoring of preoperative chemotherapy for locally advanced breast cancer. Acad Radiol, 17: 1477–1485. https://doi.org/10.1016/j.acra.2010.07.009
Korteweg MA, Veldhuis WB, Visser F, et al., 2011, Feasibility of 7 Tesla breast magnetic resonance imaging determination of intrinsic sensitivity and high-resolution magnetic resonance imaging, diffusion-weighted imaging, and (1)H-magnetic resonance spectroscopy of breast cancer patients receiving neoadjuvant therapy. Invest Radiol, 46: 370–376. https://doi.org/10.1097/RLI.0b013e31820df706
Ashokkumar N, Meera S, Anandan P, et al., 2022, Deep learning mechanism for predicting the axillary lymph node metastasis in patients with primary breast cancer. BioMed Res Int, 2022: 8616535. https://doi.org/10.1155/2022/8616535
Lewis JF, McGorray SP, Pepine CJ, 2002, Assessment of women with suspected myocardial ischemia: Review of findings of the Women’s Ischemia Syndrome Evaluation (WISE) Study. Curr Womens Health Rep, 2: 110–114.
Stivaros S, Garg S, Tziraki M, et al., 2018, Randomised controlled trial of simvastatin treatment for autism in young children with neurofibromatosis Type 1 (SANTA). Mol Autism, 9: 12. https://doi.org/10.1186/s13229-018-0190-z
Kulyabin YY, Bogachev-Prokophiev AV, Soynov IA, et al., 2020, Clinical assessment of perfusion techniques during surgical repair of coarctation of aorta with aortic arch hypoplasia in neonates: A pilot prospective randomized study. Semin Thorac Cardiovasc Surg, 32: 860–871. https://doi.org/10.1053/j.semtcvs.2020.04.015
Magnotta VA, Heo HY, Dlouhy BJ, et al., 2012, Detecting activity-evoked pH changes in human brain. Proc Natl Acad Sci U S A, 109: 8270–8273. https://doi.org/10.1073/pnas.1205902109
Streeter CC, Gerbarg PL, Brown RP, et al., 2020, Thalamic gamma aminobutyric acid level changes in major depressive disorder after a 12-week iyengar yoga and coherent breathing intervention. J Altern Complement Med, 26: 190–197. https://doi.org/10.1089/acm.2019.0234
Löbel U, Hwang S, Edwards A, et al., 2016, Discrepant longitudinal volumetric and metabolic evolution of diffuse intrinsic Pontine gliomas during treatment: Implications for current response assessment strategies. Neuroradiology, 58: 1027–1034. https://doi.org/10.1007/s00234-016-1724-8
Goda JS, Dutta D, Raut N, et al., 2014, Can multiparametric MRI and FDG-PET predict outcome in diffuse brainstem glioma? A report from a prospective phase-II study. Pediatr Neurosurg, 49: 274–281. https://doi.org/10.1159/000366167
Vöglein J, Tüttenberg J, Weimer M, et al., 2011, Treatment monitoring in gliomas: Comparison of dynamic susceptibility-weighted contrast-enhanced and spectroscopic MRI techniques for identifying treatment failure. Invest Radiol, 46: 390–400. https://doi.org/10.1097/RLI.0b013e31820e1511
Gonzalo N, Serruys PW, Barlis P, et al., 2010, Multi-modality intra-coronary plaque characterization: A pilot study. Int J Cardiol, 138: 32–39. https://doi.org/10.1016/j.ijcard.2008.08.030
Chang L, Lee PL, Yiannoutsos CT, et al., 2004, A multicenter in vivo proton-MRS study of HIV-associated dementia and its relationship to age. Neuroimage, 23: 1336–1347. https://doi.org/10.1016/j.neuroimage.2004.07.067
Stern JM, Merritt ME, Zeltser I, et al., 2008, Phase one pilot study using magnetic resonance spectroscopy to predict the histology of radiofrequency-ablated renal tissue. Eur Urol, 5: 433–438. https://doi.org/10.1016/j.eururo.2008.03.106
Medical Advisory Secretariat, 2006, Functional brain imaging: An evidence-based analysis. Ont Health Technol Assess Ser, 6: 1–79.
Kettelhack C, Wickede MV, Vogl T, et al., 2002, 31Phosphorus-magnetic resonance spectroscopy to assess histologic tumor response noninvasively after isolated limb perfusion for soft tissue tumors. Cancer, 94: 1557–1564. https://doi.org/10.1002/cncr.10361
de Fátima Vasco Aragão M, Otaduy MCG, de Melo RV, et al., 2007, Multivoxel spectroscopy with short echo time: Choline/N-acetyl-aspartate ratio and the grading of cerebral astrocytomas. Arq Neuropsiquiatr, 65: 286–294. https://doi.org/10.1590/s0004-282x2007000200019
Kallén K, Burtscher IM, Holtås S, 2000, 201Thallium SPECT and 1H-MRS compared with MRI in chemotherapy monitoring of high-grade malignant astrocytomas. J Neurooncol, 46: 173–185. https://doi.org/10.1023/a:1006429329677
Urdzik J, Bjerner T, Wanders A, et al., 2012, The value of pre-operative magnetic resonance spectroscopy in the assessment of steatohepatitis in patients with colorectal liver metastasis. J Hepatol, 56: 640–646. https://doi.org/10.1016/j.jhep.2011.10.006
Watanabe T, Shiino A, Akiguchi I, 2008, Absolute quantification in proton magnetic resonance spectroscopy is superior to relative ratio to discriminate Alzheimer’s disease from Binswanger’s disease. Dement Geriatr Cogn Disord, 26: 89–100. https://doi.org/10.1159/000144044
Wallström J, Geterud K, Kohestani K, et al., 2021, Prostate cancer screening with magnetic resonance imaging: Results from the second round of the göteborg prostate cancer screening 2 trial. Eur Urol Oncol, 5: 54–60. https://doi.org/10.1016/j.euo.2021.09.001
Schmuecking M, Boltze C, Geyer H, et al., 2009, Dynamic MRI and CAD vs. choline MRS: Where is the detection level for a lesion characterisation in prostate cancer? Int J Radiat Biol, 85: 814–824. https://doi.org/10.1080/09553000903090027
Bongiovanni A, Foca F, Oboldi D, et al., 2022, 3-T magnetic resonance-guided high-intensity focused ultrasound (3 T-MR-HIFU) for the treatment of pain from bone metastases of solid tumors. Support Care Cancer, 30: 5737–5745. https://doi.org/10.1007/s00520-022-06990-y
Kaufman MJ, Henry ME, Frederick BB, et al., 2003, Selective serotonin reuptake inhibitor discontinuation syndrome is associated with a rostral anterior cingulate choline metabolite decrease: A proton magnetic resonance spectroscopic imaging study. Biol Psychiatry, 54: 534–539. https://doi.org/10.1016/s0006-3223(02)01828-0
Kondo DG, Sung YH, Hellem TL, et al., 2011, Open-label adjunctive creatine for female adolescents with SSRI-resistant major depressive disorder: A 31-phosphorus magnetic resonance spectroscopy study. J Affect Disord, 135: 354–361. https://doi.org/10.1016/j.jad.2011.07.010
Ramesh K, Mellon EA, Gurbani SS, et al., 2022, A multi-institutional pilot clinical trial of spectroscopic MRI-guided radiation dose escalation for newly diagnosed glioblastoma. Neurooncol Adv, 4: vdac006. https://doi.org/10.1093/noajnl/vdac006
Caivano R, Lotumolo A, Rabasco P, et al., 2013, 3 Tesla magnetic resonance spectroscopy: Cerebral gliomas vs. metastatic brain tumors. Our experience and review of the literature. Int J Neurosci, 123: 537–543. https://doi.org/10.3109/00207454.2013.774395
Sharma R, Narayana PA, Wolinsky JS, 2001, Grey matter abnormalities in multiple sclerosis: Proton magnetic resonance spectroscopic imaging. Mult Scler, 7: 221–226. https://doi.org/10.1177/135245850100700402
Bedell BJ, Narayana PA, Johnston DA, 1996, 3-dimensional MR image registration of the human brain. Magn Reson Med, 35: 384–390. https://doi.org/10.1002/mrm.1910350317
Shoeibi A, Khodatars M, Jafari M, et al., 2021, Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Comput Biol Med. 136: 104697. https://doi.org/10.1016/j.compbiomed.2021.104697
Centers for Medicare & Medicaid Services (CMS), 2004, Decision Memo for Magnetic Resonance Spectroscopy for Brain Tumors (CAG-00141N). Baltimore, MD: CMS.
Ustymowicz A, Tarasow E, Zajkowska J, et al., 2004, Proton MR spectroscopy in neuroborreliosis: A preliminary study. Neuroradiology, 46: 26–30. https://doi.org/10.1007/s00234-002-0851-6
Hollingworth W, Medina LS, Lenkinski RE, et al., 2006, A systematic literature review of magnetic resonance spectroscopy for the characterization of brain tumors. AJNR Am J Neuroradiol, 27: 1404–1411.
Hallahan BP, Daly EM, Simmons A, et al., 2012, Fragile X syndrome: A pilot proton magnetic resonance spectroscopy study in premutation carriers. J Neurodev Disord, 4: 23. https://doi.org/10.1186/1866-1955-4-23
Zakian KL, Sircar K, Hricak H, et al., 2005, Correlation of proton MR spectroscopic imaging with Gleason score based on step-section pathologic analysis after radical prostatectomy. Radiology, 234: 804–814. https://doi.org/10.1148/radiol.2343040363
Wetter A, Engl TA, Nadjmabadi D, et al., 2006, Combined MRI and MR spectroscopy of the prostate before radical prostatectomy. AJR Am J Roentgenol, 187: 724–730. https://doi.org/10.2214/AJR.05.0642
Wang P, Guo YM, Liu M, et al., 2008, A meta-analysis of the accuracy of prostate cancer studies which use magnetic resonance spectroscopy as a diagnostic tool. Korean J Radiol, 9: 432–438. https://doi.org/10.3348/kjr.2008.9.5.432
Vedolin L, Schwartz IV, Komlos M, et al., 2007, Brain MRI in mucopolysaccharidosis: Effect of aging and correlation with biochemical findings. Neurology, 69: 917–924. https://doi.org/10.1212/01.wnl.0000269782.80107.fe
Chen HC, Lee LH, Lirng JF, et al., 2022, Radiological hints for differentiation of cerebellar multiple system atrophy from spinocerebellar ataxia. Sci Rep, 12: 10499. https://doi.org/10.1038/s41598-022-14531-0
Dyke JP, Sanelli PC, Voss HU, et al., 2007, Monitoring the effects of BCNU chemotherapy Wafers (Gliadel) in glioblastoma multiforme with proton magnetic resonance spectroscopic imaging at 3.0 Tesla. J Neurooncol, 82: 103–110. https://doi.org/10.1007/s11060-006-9254-6
Filippi M, Rocca MA, Arnold DL, et al., 2006, EFNS guidelines on the use of neuroimaging in the management of multiple sclerosis. Eur J Neurol, 13: 313–325. https://doi.org/10.1111/j.1468-1331.2006.01543.x
De Stefano N, Filippi M, Miller D, et al., 2007, Guidelines for using proton MR spectroscopy in multicenter clinical MS studies. Neurology, 69: 1942–1952. https://doi.org/10.1212/01.wnl.0000291557.62706.d3
Keshari KR, Lotz JC, Link TM, et al., 2008, Lactic acid and proteoglycans as metabolic markers for discogenic back pain. Spine (Phila Pa 1976), 33: 312–317. https://doi.org/10.1097/BRS.0b013e31816201c3
Gornet MG, Peacock J, Claude J, et al., 2019, Magnetic resonance spectroscopy (MRS) can identify painful lumbar discs and may facilitate improved clinical outcomes of lumbar surgeries for discogenic pain. Eur Spine J, 28: 674–687. https://doi.org/10.1007/s00586-018-05873-3
Benoist M, 2019, The Michel Benoist and Robert Mulholland Yearly European Spine journal review: A survey of the “medical” articles in the European Spine Journal, 2018. Eur Spine J, 28: 10–20. https://doi.org/10.1007/s00586-018-5857-9
Morrison WB, Dalinka MK, Daffner RH, et al., 2005, Expert Panel on Musculoskeletal Imaging. Bone Tumors. Reston, VA: American College of Radiology (ACR).
Shah N, Sattar A, Benanti M, et al., 2006, Magnetic resonance spectroscopy as an imaging tool for cancer: A review of the literature. J Am Osteopath Assoc, 106: 23–27.
Bartella L, Huang W, 2007, Proton (1H) MR spectroscopy of the breast. Radiographics, 27 Suppl 1: S241–S252. https://doi.org/10.1148/rg.27si075504
Tse GM, Yeung DK, King AD, et al., 2007, In vivo proton magnetic resonance spectroscopy of breast lesions: An update. Breast Cancer Res Treat, 104: 249–255. https://doi.org10.1007/s10549-006-9412-8
Bizzi A, Castelli G, Bugiani M, et al., 2008, Classification of childhood white matter disorders using proton MR spectroscopic imaging. AJNR Am J Neuroradiol, 29: 1270–1275. https://doi.org/10.3174/ajnr.A1106
Umbehr M, Bachmann LM, Held U, et al., 2009, Combined magnetic resonance imaging and magnetic resonance spectroscopy imaging in the diagnosis of prostate cancer: A systematic review and meta-analysis. Eur Urol, 55: 575–590. https://doi.org/10.1016/j.eururo.2008.10.019.
Lee CP, Payne GS, Oregioni A, et al., 2009, A phase I study of the nitroimidazole hypoxia marker SR4554 using 19F magnetic resonance spectroscopy. Br J Cancer, 101: 1860–1868. https://doi.org/10.1038/sj.bjc.6605425
Chuang MT, Liu YS, Tsai YS, et al., 2016, Differentiating radiation-induced necrosis from recurrent brain tumor using MR perfusion and spectroscopy: A meta-analysis. PLoS One, 11: e0141438. https://doi.org/10.1371/journal.pone.0141438
National Comprehensive Cancer Network (NCCN), 2016, Central Nervous System Cancers. NCCN Clinical Practice Guidelines in Oncology, version 1.2016. Fort Washington, PA: NCCN.
Sturrock A, Laule C, Decolongon J, et al., 2010, Magnetic resonance spectroscopy biomarkers in premanifest and early Huntington disease. Neurology, 75: 1702–1710. https://doi.org/10.1212/WNL.0b013e3181fc27e4
Beadle R, Frenneaux M, 2010, Magnetic resonance spectroscopy in myocardial disease. Expert Rev Cardiovasc Ther, 8: 269–277. https://doi.org/10.1586/erc.09.169
Horská A, Barker C, 2010, Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am, 20: 293–310. https://doi.org/10.1016/j.nic.2010.04.003
Westphalen AC, Coakley FV, Roach M 3rd et al., 2010, Locally recurrent prostate cancer after external beam radiation therapy: Diagnostic performance of 1.5-T endorectal MR imaging and MR spectroscopic imaging for detection. Radiology, 256: 485–492. https://doi.org/10.1148/radiol.10092314
Baltzer PA, Dietzel M, 2013, Breast lesions: Diagnosis by using proton MR spectroscopy at 1.5 and 3.0 T -- systematic review and meta-analysis. Radiology, 267: 735–746. https://doi.org/10.1148/radiol.13121856
Mowatt G, Scotland G, Boachie C, et al., 2013, The diagnostic accuracy and cost-effectiveness of magnetic resonance spectroscopy and enhanced magnetic resonance imaging techniques in aiding the localisation of prostate abnormalities for biopsy: A systematic review and economic evaluation. Health Technol Assess, 17: vii–xix, 1–281. https://doi.org/10.3310/hta17200
Gardner A, Iverson GL, Stanwell P, 2014, A systematic review of proton magnetic resonance spectroscopy findings in sport-related concussion. J Neurotrauma, 31: 1–18. https://doi.org/10.1089/neu.2013.3079
Harmon KG, Drezner JA, Gammons M, et al., 2013, American Medical Society for Sports Medicine position statement: Concussion in sport. Br J Sports Med, 47: 15–26. https://doi.org/10.1136/bjsports-2012-091941
Mygland A, Ljostad U, Fingerle V, et al., 2010, European Federation of Neurological Societies. EFNS guidelines on the diagnosis and management of European Lyme neuroborreliosis. Eur J Neurol, 17: 8–16, e1–e4. https://doi.org/10.1111/j.1468-1331.2009.02862.x
Wang W, Hu Y, Lu P, et al., 2014, Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: A systematic review and meta-analysis. PLoS One, 9: e112577. https://doi.org/10.1371/journal.pone.0112577
Wippold FJ 2nd, Brown DC, Broderick DF, et al., 2014, Expert Panel on Neurologic Imaging. ACR Appropriateness Criteria® Dementia and Movement Disorders. Reston, VA: American College of Radiology (ACR).
Spencer AE, Uchida M, Kenworthy T, et al., 2014, Glutamatergic dysregulation in pediatric psychiatric disorders: A systematic review of the magnetic resonance spectroscopy literature. J Clin Psychiatry, 75: 1226–1241.
Wang H, Tan L, Wang HF, et al., 2015, Magnetic resonance spectroscopy in Alzheimer’s disease: Systematic review and meta-analysis. J Alzheimers Dis, 46: 1049–1070. https://doi.org/10.3233/JAD-143225
Voevodskaya O, Poulakis K, Sundgren P, et al., 2019, Swedish BioFINDER Study Group. Brain myoinositol as a potential marker of amyloid-related pathology: A longitudinal study. Neurology, 92: e395–e405. https://doi.org/10.1212/WNL.0000000000006852
Chen WS, Li JJ, Hong L, et al., 2016, Diagnostic value of magnetic resonance spectroscopy in radiation encephalopathy induced by radiotherapy for patients with nasopharyngeal carcinoma: A meta-analysis. Biomed Res Int, 2016: 5126074. https://doi.org/10.1155/2016/5126074
Zeng G, Penninkilampi R, Chaganti J, et al., 2020, Meta-analysis of magnetic resonance spectroscopy in the diagnosis of hepatic encephalopathy. Neurology, 94: e1147–e1156. https://doi.org/10.1212/WNL.0000000000008899
Zheng D, Guo Z, Schroder PM, et al., 2017, Accuracy of MR imaging and MR spectroscopy for detection and quantification of hepatic steatosis in living liver donors: A meta-analysis. Radiology, 282: 92–102. https://doi.org/10.1148/radiol.2016152571
Wang D, Li Y, 2015, 1H magnetic resonance spectroscopy predicts hepatocellular carcinoma in a subset of patients with liver cirrhosis: A randomized trial. Medicine (Baltimore), 94: e1066. https://doi.org/10.1097/MD.0000000000001066
Zhao X, Xu M, Jorgenson K, 2016, Neurochemical changes in patients with chronic low back pain detected by proton magnetic resonance spectroscopy: A systematic review. Neuroimage Clin, 13: 33–38.
Zhang L, Li H, Hong P, 2016, Proton magnetic resonance spectroscopy in juvenile myoclonic epilepsy: A systematic review and meta-analysis. Epilepsy Res, 121: 33–38. https://doi.org/10.1016/j.eplepsyres.2016.01.004
Cevik N, Koksal A, Dogan VB, et al., 2016, Evaluation of cognitive functions of juvenile myoclonic epileptic patients by magnetic resonance spectroscopy and neuropsychiatric cognitive tests concurrently. Neurol Sci, 37: 623–627. https://doi.org/10.1007/s10072-015-2425-5
Yang M, Sun J, Bai HX, et al., 2017, Diagnostic accuracy of SPECT, PET, and MRS for primary central nervous system lymphoma in HIV patients: A systematic review and meta-analysis. Medicine (Baltimore), 96: e6676. https://doi.org/10.1097/MD.0000000000006676
Younis S, Hougaard A, Vestergaard MB, et al., 2017, Migraine and magnetic resonance spectroscopy: A systematic review. Curr Opin Neurol, 30: 246–262. https://doi.org/10.1097/WCO.0000000000000436
Lai D, Sharma R, Wolinsky JS, et al., 2003, A comparative study of correlation coefficients in spatially MRSI-observed neurochemicals from multiple sclerosis patients. J Appl Stat, 30: 1221–1229.
Wu Y, 2020, Clinical Features, Diagnosis, and Treatment of Neonatal Encephalopathy. UpToDate. Waltham, MA: UpToDate.
Zou R, Xiong T, Zhang L, et al., 2018, Proton magnetic resonance spectroscopy biomarkers in neonates with hypoxic-ischemic encephalopathy: A systematic review and meta-analysis. Front Neurol, 9: 732. https://doi.org/10.3389/fneur.2018.00732
Veeramuthu V, Seow P, Narayanan V, et al., 2018, Neurometabolites alteration in the acute phase of mild traumatic brain injury (mTBI): An in vivo proton magnetic resonance spectroscopy (1H-MRS) study. Acad Radiol, 25: 1167–1177. https://doi.org/10.1016/j.acra.2018.01.005
Eisele A, Hill-Strathy M, Michels L, et al., 2020, Magnetic resonance spectroscopy following mild traumatic brain injury: A systematic review and meta-analysis on the potential to detect posttraumatic neurodegeneration. Neurodegener Dis, 20: 2–11. https://doi.org/10.1159/000508098
Kondratyeva EA, Diment SV, Kondratyev SA, et al., 2019, Magnetic resonance spectroscopy data in the prognosis of consciousness recovery in patients with vegetative state. Zh Nevrol Psikhiatr Im S S Korsakova, 119: 7–14. https://doi.org/10.17116/jnevro20191191017
Finnell DS, 2015, A clinical translation of the article titled, The utility of magnetic resonance spectroscopy for understanding substance use disorders: A systematic review of the literature. J Am Psychiatr Nurses Assoc, 21: 276–278. https://doi.org/10.1177/1078390315598605
Frittoli RB, Pereira DR, Rittner L, et al., 2020, Proton magnetic resonance spectroscopy (1 H-MRS) in rheumatic autoimmune diseases: A systematic review. Lupus, 29: 1873–1884. https://doi.org/10.1177/0961203320961466
Fernandez-Vega N, Ramos-Rodriguez JR, Alfaro F, et al., 2021, Usefulness of magnetic resonance spectroscopy in mesial temporal sclerosis: A systematic review. Neuroradiology, 63: 1395–1405. https://doi.org/10.1007/s00234-021-02704-z
Hovsepian DA, Galati A, Chong RA, et al., 2019, MELAS: Monitoring treatment with magnetic resonance spectroscopy. Acta Neurol Scand, 139: 82–85. https://doi.org/10.1111/ane.13027
Ng YS, Bindoff LA, Gorman GS, et al., 2019, Consensus-based statements for the management of mitochondrial stroke-like episodes. Wellcome Open Res, 4: 201. https://doi.org/10.12688/wellcomeopenres.15599.1
O’Ferrall E, 2021, Mitochondrial Myopathies: Clinical Features and Diagnosis. UpToDate. Waltham, MA: UpToDate.
Weinreb JC, Blume JD, Coakley FV, et al., 2009, Prostate cancer: Sextant localization at MR imaging and MR spectroscopic imaging before prostatectomy-- results of ACRIN prospective multi-institutional clinicopathologic study. Radiology, 251: 122–133. https://doi.org/10.1148/radiol.2511080409
Wang Z, Li Y, Lam F, 2022, High-resolution, 3D multi-TE 1 H MRSI using fast spatiospectral encoding and subspace imaging. Magn Reson Med, 87: 1103–1118.
Li Y, Wang Z, Sun R, et al., 2021, Separation of metabolites and macromolecules for short-TE 1H-MRSI using learned component-specific representations. IEEE Trans Med Imaging, 40: 1157–1167. https://doi.org/10.1109/TMI.2020.3048933
Li Y, Wang Z, Lam F, 2022, SNR enhancement for multi-TE MRSI using joint low-dimensional model and spatial constraints. IEEE Trans Biomed Eng, 69: 3087–3097. https://doi.org/10.1109/TBME.2022.3161417
Dong S, Hangel G, Chen EZ, et al., 2022, Flow-based visual quality enhancer for super-resolution magnetic resonance spectroscopic imaging. In: Mukhopadhyay A, Oksuz I, Engelhardt S, et al., (eds) Deep Generative Models. DGM4MICCAI 2022. Lecture Notes in Computer Science. Vol. 13609. Cham: Springer. https://doi.org/10.1007/978-3-031-18576-2_1
Li X, Strasser B, Jafari-Khouzani K, et al., 2020, Super-resolution whole-brain 3D MR spectroscopic imaging for mapping D-2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1-mutated human gliomas. Radiology, 294: 589–597. https://doi.org/10.1148/radiol.2020191529
Wang L, Chen G, Dai K, 2022, Hydrogen proton magnetic resonance spectroscopy (MRS) in differential diagnosis of intracranial tumors: A systematic review. Contrast Media Mol Imaging, 18: 7242192. https://doi.org/10.1155/2022/7242192
This work is licensed under a Creative Commons Attribution 4.0 International License.