Tumor Diagnosis with MR Spectroscopy.
Arend Heerschap1

1Radiology, Radboud University Nijmegen, Nijmegen, Netherlands

Synopsis

This lecture provides a general introduction in the possibilities for diagnosis of tumors with MR spectroscopy. It focuses on the application of the 1H nucleus for MR spectroscopy and briefly discusses technical considerations for performing 1H MRS in the clinic. The major two topics being addressed are 1H MRS of brain tumors and prostate cancer. The lecture then concludes with some general remarks about current status and future prospects of clinical MR spectroscopy.

Introduction

Magnetic resonance is a major imaging modality in the diagnosis of cancer. In clinical practice MR is commonly used to obtain anatomical information, but the great versatility of the technique offers many possibilities to acquire physiologic or metabolic information. To increase the specificity and sensitivity of conventional MRI, essentially searching for anatomical abnormalities, these functional approaches are increasingly being included in clinical examinations of patients with tumors. For instance, perfusion weighted imaging (PWI) to assess vascular functionality, diffusion weighted imaging (DWI) to assess tissue characteristics associated with water movement or MR spectroscopy (MRS) to assess tissue metabolism and physiology.

In contrast to PWI and DWI, which only measure the signal of 1H nuclei in water, a broad range of (bio-) molecules can be viewed by MRS, either by employing the signals of 1H or those of other nuclei such as 31P, 13C or 19F in these molecules. The specific property of MRS that makes this possible is the chemical shift (or chemical shift dispersion), which causes unique resonance frequencies for nuclei in different molecular groups. Specific spectral profiles in an MR spectrum, obtained from a location in the body, reflect the identity of (bio-)chemicals present at that location. Additionally, the physiological state and environment of these molecules may affect their spectral profiles and the intensity of the spectral signals reflects their tissue levels. As the tissue content of biomolecules is orders lower than that of water, their signals have much lower intensities. In practice their detection requires tissue levels of more than 0.1 – 1 mM, which are mostly metabolites. For this reason MRS is not suitable for high resolution anatomical imaging. Instead it is used to assess metabolism or physiology at a cruder spatial scale in single or multiple selected locations to allow for spatial mapping of metabolites. Thus MRS offers a direct view on the (dynamic) levels of some metabolites and compounds and their physiological state and environment in body tissues.

In metabolomics MR spectra are also obtained of ex-vivo tumor tissues (biopsies) by high resolution Magic Angle Spectroscopy (hrMAS) or of body fluids or tissue extracts of cancer patients by high resolution NMR. These methods are not the scope of this paper, which focuses on in vivo MRS.

One of the first applications to a patient with a tumor was the detection of an abnormal 31P MR spectrum of a rhabdosarcoma as compared to that of adjacent muscle1. A typical tumor feature was the relatively high level of the so-called phosphomonoester peak, which contains signals from phosphorylated choline and ethanolamine2. When 1H MRS was applied to patients with brain tumors a relatively high signal was observed for a peak at about 3.2 ppm on the MRS chemical shift scale3. From NMR studies of biopsy extracts this peak turned out to be mainly composed of the resonances originating from protons in methyl groups of “choline compounds” such as choline, phospho-choline and glycerophophocholine of which the 1H chemical shift dispersion is too small to be resolved in the in vivo spectrum. MRS studies of other human tumors also revealed the presence of relatively high levels of choline compounds, which attracted much attention as it could serve as a potential biomarker in cancer diagnosis (detection, grading and staging) and treatment response2, 4. MRS of human tumors also has uncovered abnormal tissue levels of other metabolites reflecting changes in metabolism, morphology or physiology. Several of these metabolites are specific for the tissue from which the tumor originates, but abnormal levels are generally not specific for tumor growth only and may occur in other pathologies. However, a typical composition of several metabolite resonances together, in a metabolic profile, may be characteristic for a particular tumor growth.

In clinical applications of MRS the 1H nucleus has played a dominant role, mainly because this nucleus is present in most body metabolites and can be detected with high sensitivity. In addition protons can be employed relatively easy on common clinical MRI machines, which are dedicated to the detection of protons in water and fat. However, other MR sensitive nuclei such as 13C, 31P, 19F and 23Na may provide highly relevant and unique information on tumor metabolism and physiology5,6,7, but their application in a clinical setting has been limited up till now. As higher field MR systems are being introduced in the clinic it becomes more attractive to make use of these nuclei in clinical examinations of tumors. This paper will focus on the main applications of MRS in tumor diagnosis using the 1H nucleus, in particular in brain and prostate.

Technical considerations for clinical 1H MRS

Magnets and radiofrequency coils.

Optimal signal to noise ratios (SNR) and chemical shift dispersion with well resolved resonances are important aspects of a successful clinical 1H MRS examination. As both increase with field strength, 1H MRS at the highest possible field strength seems desirable and hence the use of 3T MR systems, which is the highest field strength commonly available in the clinic, is widely advocated for this purpose. In addition 7T MR systems are being increasingly applied in clinical applications. The improved performance of higher field systems sometimes may be disappointingly low because of practical problems occurring at these fields such as increased chemical shift dispersion error, shorter T2 values of 1H spin systems, increased B1variation in the body and more susceptibility problems. Various approaches are being used to overcome some of these problems, in particular for brain applications8,9.

At higher field the correction of susceptibility variations (shimming) may be a major challenge for some body parts and failure to address this properly will impact on the robustness of 1H MRS examinations in the clinic.

As the same 1H radiofrequency (RF) coils as for MR imaging can be used, no dedicated measures seem necessary. For 1H MRS the use of surface coils may be more advantage in certain applications such as for the prostate (endorectal coils). Phased array coils provide opportunities to improve selectivity, SNR and/or speed of acquisition of 1H MR spectra, in particular at higher fields.

In vivo 1H MRS data acquisition

Robust acquisition methods to obtain localized 1H MR spectra from single and multiple volumes are already available since the 1990’s for clinical applications at 1.5T in the brain. Clinical useful methods have become available more recently for prostate and breast applications. 1H MR spectroscopy of a single voxel (SVS) is the easiest and quickest way to obtain metabolic information of tumor tissue. Commonly, localized MR spectra are obtained by so-called STEAM or PRESS pulse sequences at long echo time (about 270 ms), intermediate echo time (about 135 ms) or short echo time (about 30 ms or less). At increasing echo times signals of different compounds are differentially attenuated by T2* relaxation and J-modulation. In practice this means that at long echo times less metabolite signals are visible, but as the resonances are also less cluttered data processing becomes easier. Because PRESS gives a 2 fold higher SNR than STEAM, this sequence is preferred for localized 1H MRS10. For very short echo times (about 10 ms or less) STEAM may still be used because of a favorable pulse timing or other sequences such as SPECIAL11. As the cubic shape of the voxel usually does not fit to the lesion volume also methods to select arbitrary shaped voxel are being developed12.

To measure viable tumor tissue by single voxel spectroscopy (SVS) the voxel (typically with a volume between 1 and 8 cc) is positioned by MRI guidance using T1 and T2 weighted images. As these images may be non-specific for the presence of tumor tissue often also a T1 weighted MRI is obtained after intravenous application of a contrast agent containing Gadolinium (Gd), to detect areas with abnormal vascularity, representing active growing tumor cores. For instance, in the brain the blood brain barrier (BBB) may be disrupted by tumor growth thus causing signal enhancement in T1-weighted MRI as Gd can spread in the interstitial space after its intravenous application. Assuming that these enhancing areas contain the viable part of the tumor, the placement of voxels for SVS is then guided by hyperintense areas on Gd enhanced MR images. MRS signals may be affected by the presence of Gd if the MRS measurement is performed shortly after contrast application13, but at short echo times the effects on spectral signals are usually acceptable. A more serious drawback of this approach is that enhancement may not occur despite the presence of tumor, for instance because of co-opting tumor growth14, or signal enhancement may be rather non-specific, requiring detailed analysis of time dependent uptake of the contrast agent to identify tumor presence, such as done in the prostate15. Moreover, tumor vascularity may “normalize” during treatment16 and assessment with Gd enhanced MRI may give the false impression that the tumor has disappeared. A way to circumvent ambiguities in volume selection is to use a multi-voxel or MR spectroscopic imaging (MRSI) approach, by which usually a large volume is selected also covering non-tumor tissue, which is divided up in smaller voxels by phase/frequency encoding methods, with volumes typically ~ 0.5 cc or more. For each metabolite a 2 or 3D spatial map can be reconstructed from signals in spectra of each voxel. In this way also the heterogeneous nature of tumors (necrosis, viable tumor tissue etc.) can be assessed. A 3D acquisition will give the best tissue coverage, for instance as used for the prostate10. For larger objects, as the whole brain, conventional 3D acquisition may be too time consuming and faster alternatives at higher fields are explored for clinical practice17.

A thorough description of the technical details of various acquisition methods is beyond the scope of this paper, but some aspects relevant to 1H MRS of cancer will be addressed below.

Assessment of brain tumors by in vivo 1H MRS

It has been demonstrated in numerous studies that 1H MRS may contribute to MR examinations in the management of brain tumors3b. This MR method provides unique metabolic information that can be diagnostic for tumor presence, type and grade, and may serve as a tool, in the planning and evaluation of treatments and in the prediction of tumor progression and treatment response.

The main metabolites visible in 1H MR spectra of the human brain obtained at long, intermediate and short echo times are N-Acetylaspartate (NAA), creatine (Cr) and choline (Cho). Their dominating resonances originate from the methyl protons in these compounds. As both creatine and choline also occur in phosphorylated forms of which the methyl resonances usually cannot be resolved separately from their not phosphorylated counterparts, it is common to use the abbreviations tCr (total creatine) and tCho (total choline) in the assessment of their resonances. Most characteristic for tumor tissue in the brain is an increase in the tCho level as compared to unaffected tissue. The tCr content is decreased in some brain tumors. A reduction in NAA is a general observation in adult brain tumors, which reflects the replacement of healthy neuronal tissue by tumor tissue. NAA reduction is a rather non-specific observation as it also occurs in other brain pathologies with neuronal damage. In MR spectra of tumor tissue often a doublet signal for lactate may be observed. This observation initially raised much interest, as lactate is an end product of aerobic glycolysis, which is an important trait of more aggressive tumors. However, a lactate signal may also be seen due to the presence of regional hypoxia or because it is not cleared from tumor tissue due to accumulation in necrotic or cystic regions, which is not necessarily associated with increased glycolysis18. Lipid signals often occur in high grade tumors and as these co-resonate with the methyl protons of lactate editing methods are required19. At shorter echo time additional signals are visible in MR spectra of brain tissue, such as of glutamate, glutamine and myo-inositol, that may also have diagnostic potential. For this reason short echo time acquisitions may be most useful for diagnosis, while long echo times with simpler spectra that can be used for rapid spatial mapping in MRSI, may be more useful for treatment planning and evaluation.

In the clinical assessment of brain tumors the application SVS is quick and easy, but with MRSI it is possible to obtain clinically relevant spatial information of metabolite distributions. For instance, the use of MRSI for spatial mapping and the analysis of resonances of multiple compounds may help to determine the grade of glial tumors20. Information on the heterogeneous nature of brain tumors is also important for biopsy guidance and for the planning, monitoring and evaluation of treatments, such as surgery, radiotherapy and chemotherapy. These procedures are commonly based on T2 weighted and Gd enhanced T1 weighted MR images. Therefore, it is of particular importance that MRS can show gross abnormalities outside Gd enhancing and T2 lesions, as commonly occurring in gliomas21. MRS can help in stereotactic procedures to obtain biopsies from the proper tumor locations, i.e. the most malignant parts22 or to delineate the tumor lesion for surgery or radiation treatment21b, 23. In all these cases the use of 3D multivoxel MRS approaches is essential. The tCho signal is most often used as marker to assess response to therapy of brain tumors by MRS24. Of clinical interest is the possibility to discriminate between tumor recurrence and radiation necrosis25. The use of MRSI seems best for this purpose25d. To avoid interference of lipid signals from the skull area generally a cubic volume is pre-selected for MRSI; sometimes this may hamper the complete inclusion of tumors located close to the skull. Other methods not using volume pre-selection or proper data processing may be helpful in these cases26.

As the analysis of MRS data of brain tumors may be rather complex to less experienced users in clinical routine and also because the proper analysis of large datasets from multi-voxel assessments is manually unpractical, there is need for decision support systems. Much attention has been given to automated and objective processing and classifying of voxels of MRS data by a variety of approaches23a, 27. In some studies MRI information has been included which generally improves the performance of the decision support systems28. To develop reliable and clinically useful classifiers of tumor type and grade it is necessary to have a sufficiently large dataset available of these tumors, which may need multi-centre efforts, especially in the case of the rarer tumors. In such mult-centre projects9a, 18b, 27d, 29 data should be acquired with some flexibility in acquisition modes and parameter settings, as the details of these will not be exactly the same on different MR systems. Quality control of MR systems, and of spectral, clinical and histopathological data are part of these projects30. To further expand the diagnostic potential also non-MR modalities may be included. A recent development of importance is the possibility to detect the onco-metabolite 2-hydroxyglutarate (2-HG) as a metabolic biomarker for a mutation in the IDH1 gene, which is now also being explored in treatment evaluations31. This is of particular interest as the WHO classification of brain tumors is about to be drastically modified based on classes of genetic mutations in brain tumors.

In vivo 1H MRS to assess prostate cancer

Abnormal levels of the prostate specific antigen (PSA) in serum is the most important biomarker used as an indication that prostate cancer (PCa) may be present, but it has a low specificity. In the detection of PCa the analysis of ultrasound-guided biopsies from the prostate plays a major role, but due to sampling errors, often-negative biopsies occur despite positive PSA levels. Thus better imaging of the presence of cancer foci is desirable to guide biopsies. The stage of PCa (occurrence of extra prostatic cancer) is often decisive in therapy-decision, but is currently mostly estimated from the so-called Partin tables of clinical findings; thus the addition of imaging might improve individual assessments. Proper localization of cancer tissue is also of interest for new focal therapies of local disease. Functional imaging to localize active tumor can be important for treatment assessment (e.g. recurrence) and may help to predict aggressive progression of localized PCa. For this purpose multi-parametric MRI is becoming an important approach in the diagnosis of prostate cancer32. The key methods are T2 weighted MRI and DWI with additionally dynamic contrast and 1H MRSI. From these a diagnostic score is summarized using the so-called PIRADS classification33. The clinical application of 1H MRSI in this setting depends on its practicality and reliability34.

After the introduction of endorectal coils it became possible to obtain in vivo 1H MR spectra of small volumes of the prostate with sufficient signal to noise35. The dominant peaks observed in these spectra are from protons in citrate, creatine and choline compounds. Compared to healthy peripheral or BPH tissue the signals of citrate were reduced and those of choline compounds often increased in cancer tissue and thus it was obvious that the tCho over citrate peaks could serve as a metabolic biomarker for prostate cancer. Signals of protons in polyamines are observed resonating in-between the creatine signal at about 3 ppm and the tCho peak at 3.2 ppm. As citrate mainly occurs in the ducts of the prostate a lower citrate may indicate altered metabolism as well as a reduction of luminal space in cancer tissue. Because tumor tissue can occur anywhere in the prostate it is clear that multi-voxel methods are essential in further clinical studies. As the prostate is relatively small and embedded in adipose tissue this requires advanced RF pulses and sequences to suppress interfering strong lipid signals. In this way 2 and 3-dimensional MR spectroscopic imaging sequences have been realized10b, 35a, 36 of which 3D variants are most favored as these can cover the whole prostate. Useful three-dimensional acquisitions can be performed in less than 10 minutes at a true spatial resolution down to about 0.3 cc. In the analysis of the data of patients it should be taken into account that different regions of the healthy prostate such as peripheral zone, central zone, and areas close to the urethra and at the seminal vesicles close to the base of the prostate have different amplitudes for citrate, creatine and “choline”compound signals. In addition MRS has to be performed sufficiently delayed with respect to the time of biopsy to avoid possible interference of a hemorrhage with spectral quality, due to magnetic field or morphological distortions.

Because the signals of tCho and creatine are often not well resolved (at 1.5T) it is common to use the tCho plus creatine over citrate ratio, which ignores possible decreases in creatine in cancer tissue, but this can be taken into account in a refined analysis37. By combining T2 weighted MRI and 1H MRSI sensitivity and specificity in localizing PCa commonly is between 80 and 90% in single site studies, significantly better than T2 MRI alone38. In a cancer detection/localization study at 3T proton MRSI performed worse than DWI in the peripheral zone, but better in the transition zone, which suggests an interesting complementary role for both methods39.The true clinical value of MRSI can only be judged from the results of multi-center trials40. Potential confounders in the detection and localization of PCa tissue are conditions such as prostatitus41 and high grade PIN42, which may mimic metabolism, physiology and morphology of cancer tissue.

MRSI can also contribute to the planning and assessment of various treatments of PCa, such as the detection of recurrence43. 1H MRSI also has been incorporated in new nomograms to identify low-risk PCa44. It is of particular interest that the (tCho + Cr)/citrate ratio is correlated with the Gleason score, which is a measure of tumor aggression45. As yet the overlap between Gleason scores precludes a use in individual decisions but 1H MRSI appears to be particular effective to identify tumors with high Gleasons, which information can be used for focal radiotherapy46. Endorectal MR imaging and spectroscopic imaging at 1.5T of tumor apparency or inapparency in PCa patients who selected active surveillance did not appear to have a prognostic value, similar to conventional biochemical measures47. However, a recent study showed that MRSI findings have predictive value as they are associated with post-radical prostatectomy treatment failure.48

Fig 1. Mapping cancer in the prostate using the ratio of choline over citrate signals.

Over time substantial progress has been made in the implementation of proton MRSI methods for the prostate in the clinic49. However, the translation into a routine clinical tool is still hampered by cumbersome practicality in acquisition and postprocessing and suboptimal reliability. To improve the practicality of postprocessing of MRSI data automatic prostprocessing methods have been implemented including quality filters to remove bad data50. More recently LASER type 3D acquisition sequences with GOIA adiabatic pulses have been implemented for 3T which perform with improved reliability compared to standard PRESS and with a higher SNR at a shorter TE51. Because of the higher SNR they allow to acquire data more rapidly52 and to perform robust 1H MRSI with external phased array coils53, circumventing the use of an endorectal coil, which is a major barrier to the widespread use of MR of the prostate. Three-dimensional 1H MRSI of the prostate at 7T is now also becoming possible, showing much improved SNR and thus has prospects for improved spatial resolution54

Concluding remarks

The potential of MR spectroscopy to contribute to MR examinations in the management of some human tumors has been convincingly demonstrated and a large number of clinical institutions are applying 1H MRS now in the assessment of brain and prostate cancer. The further translation to a widespread routine clinical tool depends on a few key factors. Not only a robust and automated measurement procedure is necessary, but also the rapid and easy digestible display of the results of an examination and proper training of the clinical users are important. And above all it has to be clear that methods are generally applicable and produce reliable and significant clinical results, also compared to other approaches or modalities. Studies using evidence based medicine (EBM) criteria to evaluate the diagnostic and therapy decision-making value of MRS applied to patients suspected to have a brain tumor55 and a critical discussion of the results of these studies56 have made clear that carefully standardized multi-site trials, complying to EBM criteria, are still needed to bring MRS of tumors in general clinical practice57. Thus for the clinical acceptance of MRS it is important that multi-site trials, as described above, are being performed, preferably in a prospective way. In these trials the selection of standardized protocols may be critical.

A particular advantage of MRS is that it provides quantitative data, which is not common practice in the clinical assessment of tumors by most (conventional) MRI methods. A general trend nowadays is to combine MRS data with that of other MR approaches with higher spatial resolution, such as conventional T1 and T2 weighted MRI, perfusion MRI (e.g. dynamic contrast enhanced MRI), and diffusion MRI, as it may improve the diagnostic performance beyond that of each single MR approach. Because of their non-invasive nature it is expected that all these MR approaches together will be particular useful in the diagnosis of tumors based on existing and new (genetic) biomarkers and in the evaluation and prediction of treatment response and disease progress.

MRSI can be seen as molecular imaging “avant la lettre”, although it does not involve imaging of tailored probes for specific molecular or cellular targets, which is often the restricted definition of molecular imaging. In contrast to common “molecular imaging” approaches MRS assesses the signals of endogenous compounds. Hence, no problems associated with the synthesis and injection of exogenous compounds for targeted diagnostics are involved. The number of endogenous compounds that are visible by in vivo MRS is limited to about 40 (also including the use of other nuclei than 1H), because of sensitivity reasons.

However, new ways are being explored for a substantial sensitivity enhancement of MRS with hyperpolarization of endogenous (or exogenous) compounds. This hyperpolarization is performed outside the body and after the compounds are applied intravenously, their metabolic conversion can be monitored by fast high resolution MRSI, to realize “real time metabolic imaging” for diagnostic purposes in oncology58. It is exciting that this has been proven to be applicable to the human prostate showing increased lactate production from hyperpolarized pyruvate in tumorous areas59. Several groups are now preparing to start with clinical trials of applying hyperpolarized pyruvate to different tumor types. In these developments also PET/MR machines may play a role.

Acknowledgements

I would like to thank the many collaborators that have contributed with excellent work to the oncology MR spectroscopy research of the Biomedical MR group in Nijmegen. Their names can be retrieved from the references below.

References

1. Griffiths, J. R.; Cady, E.; Edwards, R. H.; McCready, V. R.; Wilkie, D. R.; Wiltshaw, E., 31P-NMR studies of a human tumour in situ. Lancet 1983, 1 (8339), 1435-6.

2. Negendank, W.; Li, C. W.; Padavic-Shaller, K.; Murphy-Boesch, J.; Brown, T. R., Phospholipid metabolites in 1H-decoupled 31P MRS in vivo in human cancer: implications for experimental models and clinical studies. Anticancer Res 1996, 16 (3B), 1539-44.

3. (a) Bruhn, H.; Frahm, J.; Gyngell, M. L.; Merboldt, K. D.; Hanicke, W.; Sauter, R.; Hamburger, C., Noninvasive differentiation of tumors with use of localized H-1 MR spectroscopy in vivo: initial experience in patients with cerebral tumors. Radiology 1989, 172 (2), 541-8. (b) Öz G, J. Alger, P. Barker, R Bartha, A Bizzi,C Boesch, P Bolan, K. Brindle, C Cudalbu, A Dinçer, U Dydak, U Emir, J Frahm, R González, S Gruber, R Gruetter, R. Gupta, A Heerschap, A Henning, H. Hetherington, F. Howe, P. Hüppi, R. Hurd, For the MRS Consensus Group. Clinical Proton MR Spectroscopy in Central Nervous System Disorders. Radiology: 2014, 270 (3), 658

4. Gillies, R. J.; Morse, D. L., In vivo magnetic resonance spectroscopy in cancer. Annu Rev Biomed Eng 2005, 7, 287-326.

5. van Laarhoven, H. W.; Punt, C. J.; Kamm, Y. J.; Heerschap, A., Monitoring fluoropyrimidine metabolism in solid tumors with in vivo (19)F magnetic resonance spectroscopy. Crit Rev Oncol Hematol 2005, 56 (3), 321-43.

6. Arias-Mendoza, F.; Smith, M. R.; Brown, T. R., Predicting treatment response in non-Hodgkin's lymphoma from the pretreatment tumor content of phosphoethanolamine plus phosphocholine. Acad Radiol 2004, 11 (4), 368-76.

7. (a) Wijnen, J. P.; Van der Graaf, M.; Scheenen, T. W.; Klomp, D. W.; de Galan, B. E.; Idema, A. J.; Heerschap, A., In vivo 13C magnetic resonance spectroscopy of a human brain tumor after application of 13C-1-enriched glucose. Magn Reson Imaging 2010, 28 (5), 690-7; (b) Biller, A.; Badde, S.; Nagel, A.; Neumann, J. O.; Wick, W.; Hertenstein, A.; Bendszus, M.; Sahm, F.; Benkhedah, N.; Kleesiek, J., Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression. AJNR Am J Neuroradiol 2016, 37 (1), 66-73.

8. Scheenen, T. W. J.; Heerschap, A.; Klomp, D. W. J., Towards H-1-MRSI of the human brain at 7T with slice-selective adiabatic refocusing pulses. Magn. Reson. Mat. Phys. Biol. Med. 2008, 21 (1-2), 95-101. 9. (a) eTUMOUR. http://www.etumour.net/; (b) Henning, A.; Schar, M.; Schulte, R. F.; Wilm, B.; Pruessmann, K. P.; Boesiger, P., SELOVS: brain MRSI localization based on highly selective T1- and B1- insensitive outer-volume suppression at 3T. Magn Reson Med 2008, 59 (1), 40-51.

10. (a) Star-Lack, J.; Nelson, S. J.; Kurhanewicz, J.; Huang, L. R.; Vigneron, D. B., Improved water and lipid suppression for 3D PRESS CSI using RF band selective inversion with gradient dephasing (BASING). Magn Reson Med 1997, 38 (2), 311-21; (b) Scheenen, T. W.; Klomp, D. W.; Roll, S. A.; Futterer, J. J.; Barentsz, J. O.; Heerschap, A., Fast acquisition-weighted three-dimensional proton MR spectroscopic imaging of the human prostate. Magn Reson Med 2004, 52 (1), 80-8.

11. Fuchs, A.; Luttje, M.; Boesiger, P.; Henning, A., SPECIAL semi-LASER with lipid artifact compensation for 1H MRS at 7 T. Magn Reson Med 2013, 69 (3), 603-12.

12. Qin, Q.; Gore, J. C.; Does, M. D.; Avison, M. J.; de Graaf, R. A., 2D arbitrary shape-selective excitation summed spectroscopy (ASSESS). Magn Reson Med 2007, 58 (1), 19-26.
13. (a) Sijens, P. E.; van den Bent, M. J.; Nowak, P. J.; van Dijk, P.; Oudkerk, M., 1H chemical shift imaging reveals loss of brain tumor choline signal after administration of Gd-contrast. Magn Reson Med 1997, 37 (2), 222-5; (b) Murphy, P. S.; Leach, M. O.; Rowland, I. J., Signal modulation in (1)H magnetic resonance spectroscopy using contrast agents: proton relaxivities of choline, creatine, and N-acetylaspartate. Magn Reson Med 1999, 42 (6), 1155-8; (c) Lin, A. P.; Ross, B. D., Short-echo time proton MR spectroscopy in the presence of gadolinium. J Comput Assist Tomogr 2001, 25 (5), 705-12.

14. Leenders, W.; Kusters, B.; Pikkemaat, J.; Wesseling, P.; Ruiter, D.; Heerschap, A.; Barentsz, J.; de Waal, R. M., Vascular endothelial growth factor-A determines detectability of experimental melanoma brain metastasis in GD-DTPA-enhanced MRI. Int J Cancer 2003, 105 (4), 437-43.

15. (a) van Dorsten, F. A.; van der Graaf, M.; Engelbrecht, M. R.; van Leenders, G. J.; Verhofstad, A.; Rijpkema, M.; de la Rosette, J. J.; Barentsz, J. O.; Heerschap, A., Combined quantitative dynamic contrast-enhanced MR imaging and (1)H MR spectroscopic imaging of human prostate cancer. J Magn Reson Imaging 2004, 20 (2), 279-87; (b) Futterer, J. J.; Heijmink, S. W.; Scheenen, T. W.; Veltman, J.; Huisman, H. J.; Vos, P.; Hulsbergen-Van de Kaa, C. A.; Witjes, J. A.; Krabbe, P. F.; Heerschap, A.; Barentsz, J. O., Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. Radiology 2006, 241 (2), 449-58.

16. (a) Leenders, W. P.; Kusters, B.; Verrijp, K.; Maass, C.; Wesseling, P.; Heerschap, A.; Ruiter, D.; Ryan, A.; de Waal, R., Antiangiogenic therapy of cerebral melanoma metastases results in sustained tumor progression via vessel co-option. Clin Cancer Res 2004, 10 (18 Pt 1), 6222-30; (b) Jain, R. K., Antiangiogenic therapy for cancer: current and emerging concepts. Oncology (Williston Park) 2005, 19 (4 Suppl 3), 7-16. 17. Posse, S.; Otazo, R.; Dager, S. R.; Alger, J., MR spectroscopic imaging: principles and recent advances. J Magn Reson Imaging 2013, 37 (6), 1301-25.

18. (a) Kugel, H.; Heindel, W.; Ernestus, R. I.; Bunke, J.; du Mesnil, R.; Friedmann, G., Human brain tumors: spectral patterns detected with localized H-1 MR spectroscopy. Radiology 1992, 183 (3), 701-9; (b) Negendank, W. G.; Sauter, R.; Brown, T. R.; Evelhoch, J. L.; Falini, A.; Gotsis, E. D.; Heerschap, A.; Kamada, K.; Lee, B. C.; Mengeot, M. M.; Moser, E.; Padavic-Shaller, K. A.; Sanders, J. A.; Spraggins, T. A.; Stillman, A. E.; Terwey, B.; Vogl, T. J.; Wicklow, K.; Zimmerman, R. A., Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study. J Neurosurg 1996, 84 (3), 449-58. 19. Li, X.; Vigneron, D. B.; Cha, S.; Graves, E. E.; Crawford, F.; Chang, S. M.; Nelson, S. J., Relationship of MR-derived lactate, mobile lipids, and relative blood volume for gliomas in vivo. AJNR Am J Neuroradiol 2005, 26 (4), 760-9.

20. (a) Stadlbauer, A.; Gruber, S.; Nimsky, C.; Fahlbusch, R.; Hammen, T.; Buslei, R.; Tomandl, B.; Moser, E.; Ganslandt, O., Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging. Radiology 2006, 238 (3), 958-69; (b) Law, M.; Cha, S.; Knopp, E. A.; Johnson, G.; Arnett, J.; Litt, A. W., High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002, 222 (3), 715-21.

21. (a) McKnight, T. R.; von dem Bussche, M. H.; Vigneron, D. B.; Lu, Y.; Berger, M. S.; McDermott, M. W.; Dillon, W. P.; Graves, E. E.; Pirzkall, A.; Nelson, S. J., Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence. J Neurosurg 2002, 97 (4), 794-802; (b) Pirzkall, A.; McKnight, T. R.; Graves, E. E.; Carol, M. P.; Sneed, P. K.; Wara, W. W.; Nelson, S. J.; Verhey, L. J.; Larson, D. A., MR-spectroscopy guided target delineation for high-grade gliomas. Int J Radiat Oncol Biol Phys 2001, 50 (4), 915-28; (c) Ganslandt, O.; Stadlbauer, A.; Fahlbusch, R.; Kamada, K.; Buslei, R.; Blumcke, I.; Moser, E.; Nimsky, C., Proton magnetic resonance spectroscopic imaging integrated into image-guided surgery: correlation to standard magnetic resonance imaging and tumor cell density. Neurosurgery 2005, 56 (2 Suppl), 291-8; discussion 291-8.

22. (a) Dowling, C.; Bollen, A. W.; Noworolski, S. M.; McDermott, M. W.; Barbaro, N. M.; Day, M. R.; Henry, R. G.; Chang, S. M.; Dillon, W. P.; Nelson, S. J.; Vigneron, D. B., Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. AJNR Am J Neuroradiol 2001, 22 (4), 604-12; (b) Hall, W. A.; Truwit, C. L., 1.5 T: spectroscopy-supported brain biopsy. Neurosurg Clin N Am 2005, 16 (1), 165-72, vii; (c) Stadlbauer, A.; Moser, E.; Gruber, S.; Nimsky, C.; Fahlbusch, R.; Ganslandt, O., Integration of biochemical images of a tumor into frameless stereotaxy achieved using a magnetic resonance imaging/magnetic resonance spectroscopy hybrid data set. J Neurosurg 2004, 101 (2), 287-94.

23. (a) McKnight, T. R.; Noworolski, S. M.; Vigneron, D. B.; Nelson, S. J., An automated technique for the quantitative assessment of 3D-MRSI data from patients with glioma. J Magn Reson Imaging 2001, 13 (2), 167-77; (b) Nelson, S. J.; Graves, E.; Pirzkall, A.; Li, X.; Antiniw Chan, A.; Vigneron, D. B.; McKnight, T. R., In vivo molecular imaging for planning radiation therapy of gliomas: an application of 1H MRSI. J Magn Reson Imaging 2002, 16 (4), 464-76; (c) Chan, A. A.; Lau, A.; Pirzkall, A.; Chang, S. M.; Verhey, L. J.; Larson, D.; McDermott, M. W.; Dillon, W. P.; Nelson, S. J., Proton magnetic resonance spectroscopy imaging in the evaluation of patients undergoing gamma knife surgery for Grade IV glioma. J Neurosurg 2004, 101 (3), 467-75.

24. (a) Howe, F. A.; Opstad, K. S., 1H MR spectroscopy of brain tumours and masses. NMR Biomed 2003, 16 (3), 123-31; (b) Lichy, M. P.; Bachert, P.; Hamprecht, F.; Weber, M. A.; Debus, J.; Schulz-Ertner, D.; Schlemmer, H. P.; Kauczor, H. U., [Application of (1)H MR spectroscopic imaging in radiation oncology: choline as a marker for determining the relative probability of tumor progression after radiation of glial brain tumors]. Rofo 2006, 178 (6), 627-33.

25. (a) Tedeschi, G.; Lundbom, N.; Raman, R.; Bonavita, S.; Duyn, J. H.; Alger, J. R.; Di Chiro, G., Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton magnetic resonance spectroscopy imaging study. J Neurosurg 1997, 87 (4), 516-24; (b) Wald, L. L.; Nelson, S. J.; Day, M. R.; Noworolski, S. E.; Henry, R. G.; Huhn, S. L.; Chang, S.; Prados, M. D.; Sneed, P. K.; Larson, D. A.; Wara, W. M.; McDermott, M.; Dillon, W. P.; Gutin, P. H.; Vigneron, D. B., Serial proton magnetic resonance spectroscopy imaging of glioblastoma multiforme after brachytherapy. J Neurosurg 1997, 87 (4), 525-34; (c) Graves, E. E.; Nelson, S. J.; Vigneron, D. B.; Verhey, L.; McDermott, M.; Larson, D.; Chang, S.; Prados, M. D.; Dillon, W. P., Serial proton MR spectroscopic imaging of recurrent malignant gliomas after gamma knife radiosurgery. AJNR Am J Neuroradiol 2001, 22 (4), 613-24; (d) Chernov, M.; Hayashi, M.; Izawa, M.; Ochiai, T.; Usukura, M.; Abe, K.; Ono, Y.; Muragaki, Y.; Kubo, O.; Hori, T.; Takakura, K., Differentiation of the radiation-induced necrosis and tumor recurrence after gamma knife radiosurgery for brain metastases: importance of multi-voxel proton MRS. Minim Invasive Neurosurg 2005, 48 (4), 228-34; (e) Weybright, P.; Sundgren, P. C.; Maly, P.; Hassan, D. G.; Nan, B.; Rohrer, S.; Junck, L., Differentiation between brain tumor recurrence and radiation injury using MR spectroscopy. AJR Am J Roentgenol 2005, 185 (6), 1471-6; (f) Rock, J. P.; Hearshen, D.; Scarpace, L.; Croteau, D.; Gutierrez, J.; Fisher, J. L.; Rosenblum, M. L.; Mikkelsen, T., Correlations between magnetic resonance spectroscopy and image-guided histopathology, with special attention to radiation necrosis. Neurosurgery 2002, 51 (4), 912-9; discussion 919-20; (g) Rock, J. P.; Scarpace, L.; Hearshen, D.; Gutierrez, J.; Fisher, J. L.; Rosenblum, M.; Mikkelsen, T., Associations among magnetic resonance spectroscopy, apparent diffusion coefficients, and image-guided histopathology with special attention to radiation necrosis. Neurosurgery 2004, 54 (5), 1111-7; discussion 1117-9; (h) Schlemmer, H. P.; Bachert, P.; Henze, M.; Buslei, R.; Herfarth, K. K.; Debus, J.; van Kaick, G., Differentiation of radiation necrosis from tumor progression using proton magnetic resonance spectroscopy. Neuroradiology 2002, 44 (3), 216-22. 26. (a) Auer, D. P.; Gossl, C.; Schirmer, T.; Czisch, M., Improved analysis of 1H-MR spectra in the presence of mobile lipids. Magn Reson Med 2001, 46 (3), 615-8; (b) Ebel, A.; Maudsley, A. A., Comparison of methods for reduction of lipid contamination for in vivo proton MR spectroscopic imaging of the brain. Magn Reson Med 2001, 46 (4), 706-12.

27. (a) Poptani, H.; Kaartinen, J.; Gupta, R. K.; Niemitz, M.; Hiltunen, Y.; Kauppinen, R. A., Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. J Cancer Res Clin Oncol 1999, 125 (6), 343-9; (b) Preul, M. C.; Caramanos, Z.; Leblanc, R.; Villemure, J. G.; Arnold, D. L., Using pattern analysis of in vivo proton MRSI data to improve the diagnosis and surgical management of patients with brain tumors. NMR Biomed 1998, 11 (4-5), 192-200; (c) Herminghaus, S.; Dierks, T.; Pilatus, U.; Moller-Hartmann, W.; Wittsack, J.; Marquardt, G.; Labisch, C.; Lanfermann, H.; Schlote, W.; Zanella, F. E., Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. J Neurosurg 2003, 98 (1), 74-81; (d) Tate, A. R.; Underwood, J.; Acosta, D. M.; Julia-Sape, M.; Majos, C.; Moreno-Torres, A.; Howe, F. A.; van der Graaf, M.; Lefournier, V.; Murphy, M. M.; Loosemore, A.; Ladroue, C.; Wesseling, P.; Luc Bosson, J.; Cabanas, M. E.; Simonetti, A. W.; Gajewicz, W.; Calvar, J.; Capdevila, A.; Wilkins, P. R.; Bell, B. A.; Remy, C.; Heerschap, A.; Watson, D.; Griffiths, J. R.; Arus, C., Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed 2006, 19 (4), 411-34; (e) Maudsley, A. A.; Darkazanli, A.; Alger, J. R.; Hall, L. O.; Schuff, N.; Studholme, C.; Yu, Y.; Ebel, A.; Frew, A.; Goldgof, D.; Gu, Y.; Pagare, R.; Rousseau, F.; Sivasankaran, K.; Soher, B. J.; Weber, P.; Young, K.; Zhu, X., Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging. NMR Biomed 2006, 19 (4), 492-503.

28. (a) De Edelenyi, F. S.; Rubin, C.; Esteve, F.; Grand, S.; Decorps, M.; Lefournier, V.; Le Bas, J. F.; Remy, C., A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images. Nat Med 2000, 6 (11), 1287-9; (b) Simonetti, A. W.; Melssen, W. J.; van der Graaf, M.; Postma, G. J.; Heerschap, A.; Buydens, L. M., A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy. Anal Chem 2003, 75 (20), 5352-61; (c) Galanaud, D.; Nicoli, F.; Chinot, O.; Confort-Gouny, S.; Figarella-Branger, D.; Roche, P.; Fuentes, S.; Le Fur, Y.; Ranjeva, J. P.; Cozzone, P. J., Noninvasive diagnostic assessment of brain tumors using combined in vivo MR imaging and spectroscopy. Magn Reson Med 2006, 55 (6), 1236-45; (d) De Vos, M.; Laudadio, T.; Simonetti, A. W.; Heerschap, A.; Van Huffel, S., Fast nosologic imaging of the brain. J Magn Reson 2006; (e) Devos, A.; Simonetti, A. W.; van der Graaf, M.; Lukas, L.; Suykens, J. A.; Vanhamme, L.; Buydens, L. M.; Heerschap, A.; Van Huffel, S., The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson 2005, 173 (2), 218-28.

29. Julia-Sape, M.; Griffiths, J. R.; Tate, R. A.; Howe, F. A.; Acosta, D.; Postma, G.; Underwood, J.; Majos, C.; Arus, C., Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR Biomed 2015, 28 (12), 1772-87.

30. (a) van der Graaf, M.; Julia-Sape, M.; Howe, F. A.; Ziegler, A.; Majos, C.; Moreno-Torres, A.; Rijpkema, M.; Acosta, D.; Opstad, K. S.; van der Meulen, Y. M.; Arus, C.; Heerschap, A., MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR Biomed 2008, 21 (2), 148-58; (b) Wright, A. J.; Arus, C.; Wijnen, J. P.; Moreno-Torres, A.; Griffiths, J. R.; Celda, B.; Howe, F. A., Automated quality control protocol for MR spectra of brain tumors. Magn Reson Med 2008, 59 (6), 1274-81.

31. (a) Choi, C.; Ganji, S. K.; DeBerardinis, R. J.; Hatanpaa, K. J.; Rakheja, D.; Kovacs, Z.; Yang, X. L.; Mashimo, T.; Raisanen, J. M.; Marin-Valencia, I.; Pascual, J. M.; Madden, C. J.; Mickey, B. E.; Malloy, C. R.; Bachoo, R. M.; Maher, E. A., 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 2012, 18 (4), 624-9; (b) Ganji, S. K.; An, Z.; Tiwari, V.; McNeil, S.; Pinho, M. C.; Pan, E.; Mickey, B. E.; Maher, E. A.; Choi, C., In vivo detection of 2-hydroxyglutarate in brain tumors by optimized point-resolved spectroscopy (PRESS) at 7T. Magn Reson Med 2016; (c) Pope, W. B.; Prins, R. M.; Albert Thomas, M.; Nagarajan, R.; Yen, K. E.; Bittinger, M. A.; Salamon, N.; Chou, A. P.; Yong, W. H.; Soto, H.; Wilson, N.; Driggers, E.; Jang, H. G.; Su, S. M.; Schenkein, D. P.; Lai, A.; Cloughesy, T. F.; Kornblum, H. I.; Wu, H.; Fantin, V. R.; Liau, L. M., Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neurooncol 2012, 107 (1), 197-205; (d) Andronesi, O. C.; Rapalino, O.; Gerstner, E.; Chi, A.; Batchelor, T. T.; Cahill, D. P.; Sorensen, A. G.; Rosen, B. R., Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. J Clin Invest 2013, 123 (9), 3659-63; (e) Andronesi, O. C.; Loebel, F.; Bogner, W.; Marjanska, M.; Vander Heiden, M. G.; Iafrate, A. J.; Dietrich, J.; Batchelor, T. T.; Gerstner, E. R.; Kaelin, W. G.; Chi, A. S.; Rosen, B. R.; Cahill, D. P., Treatment Response Assessment in IDH-Mutant Glioma Patients by Noninvasive 3D Functional Spectroscopic Mapping of 2-Hydroxyglutarate. Clin Cancer Res 2016, 22 (7), 1632-41.

32. (a) Hoeks, C. M.; Barentsz, J. O.; Hambrock, T.; Yakar, D.; Somford, D. M.; Heijmink, S. W.; Scheenen, T. W.; Vos, P. C.; Huisman, H.; van Oort, I. M.; Witjes, J. A.; Heerschap, A.; Futterer, J. J., Prostate cancer: multiparametric MR imaging for detection, localization, and staging. Radiology 2011, 261 (1), 46-66; (b) De Visschere, P. J.; Briganti, A.; Futterer, J. J.; Ghadjar, P.; Isbarn, H.; Massard, C.; Ost, P.; Sooriakumaran, P.; Surcel, C. I.; Valerio, M.; van den Bergh, R. C.; Ploussard, G.; Giannarini, G.; Villeirs, G. M., Role of multiparametric magnetic resonance imaging in early detection of prostate cancer. Insights Imaging 2016, 7 (2), 205-14.

33. (a) Bomers, J. G.; Barentsz, J. O., Standardization of multiparametric prostate MR imaging using PI-RADS. Biomed Res Int 2014, 2014, 431680; (b) Turkbey, B.; Choyke, P. L., PIRADS 2.0: what is new? Diagn Interv Radiol 2015, 21 (5), 382-4. 34. Kobus, T.; Wright, A. J.; Scheenen, T. W.; Heerschap, A., Mapping of prostate cancer by 1H MRSI. NMR Biomed 2014, 27 (1), 39-52.

35. (a) Heerschap, A.; Jager, G. J.; van der Graaf, M.; Barentsz, J. O.; Ruijs, S. H., Proton MR spectroscopy of the normal human prostate with an endorectal coil and a double spin-echo pulse sequence. Magn Reson Med 1997, 37 (2), 204-13; (b) Kurhanewicz, J.; Vigneron, D. B.; Nelson, S. J.; Hricak, H.; MacDonald, J. M.; Konety, B.; Narayan, P., Citrate as an in vivo marker to discriminate prostate cancer from benign prostatic hyperplasia and normal prostate peripheral zone: detection via localized proton spectroscopy. Urology 1995, 45 (3), 459-66; (c) Heerschap, A.; Jager, G. J.; van der Graaf, M.; Barentsz, J. O.; de la Rosette, J. J.; Oosterhof, G. O.; Ruijter, E. T.; Ruijs, S. H., In vivo proton MR spectroscopy reveals altered metabolite content in malignant prostate tissue. Anticancer Res 1997, 17 (3A), 1455-60.

36. (a) Kurhanewicz, J.; Vigneron, D. B.; Hricak, H.; Narayan, P.; Carroll, P.; Nelson, S. J., Three-dimensional H-1 MR spectroscopic imaging of the in situ human prostate with high (0.24-0.7-cm3) spatial resolution. Radiology 1996, 198 (3), 795-805; (b) van der Graaf, M.; van den Boogert, H. J.; Jager, G. J.; Barentsz, J. O.; Heerschap, A., Human prostate: multisection proton MR spectroscopic imaging with a single spin-echo sequence--preliminary experience. Radiology 1999, 213 (3), 919-25.

37. (a) Jung, J. A.; Coakley, F. V.; Vigneron, D. B.; Swanson, M. G.; Qayyum, A.; Weinberg, V.; Jones, K. D.; Carroll, P. R.; Kurhanewicz, J., Prostate depiction at endorectal MR spectroscopic imaging: investigation of a standardized evaluation system. Radiology 2004, 233 (3), 701-8; (b) Futterer, J. J.; Scheenen, T. W.; Heijmink, S. W.; Huisman, H. J.; Hulsbergen-Van de Kaa, C. A.; Witjes, J. A.; Heerschap, A.; Barentsz, J. O., Standardized threshold approach using three-dimensional proton magnetic resonance spectroscopic imaging in prostate cancer localization of the entire prostate. Invest Radiol 2007, 42 (2), 116-22.

38. Futterer, J. e. a., Dynamic Contrast-Enhanced MR and Proton MR Spectroscopic Imaging in Localizing Prostate Cancer. submitted 2006.

39. Kobus, T.; Vos, P. C.; Hambrock, T.; De Rooij, M.; Hulsbergen-Van de Kaa, C. A.; Barentsz, J. O.; Heerschap, A.; Scheenen, T. W., Prostate cancer aggressiveness: in vivo assessment of MR spectroscopy and diffusion-weighted imaging at 3 T. Radiology 2012, 265 (2), 457-67. 40. Scheenen, T. W.; Futterer, J.; Weiland, E.; van Hecke, P.; Lemort, M.; Zechmann, C.; Schlemmer, H. P.; Broome, D.; Villeirs, G.; Lu, J.; Barentsz, J.; Roell, S.; Heerschap, A., Discriminating cancer from noncancer tissue in the prostate by 3-dimensional proton magnetic resonance spectroscopic imaging: a prospective multicenter validation study. Invest Radiol 2011, 46 (1), 25-33.

41. (a) Shukla-Dave, A.; Hricak, H.; Eberhardt, S. C.; Olgac, S.; Muruganandham, M.; Scardino, P. T.; Reuter, V. E.; Koutcher, J. A.; Zakian, K. L., Chronic prostatitis: MR imaging and 1H MR spectroscopic imaging findings--initial observations. Radiology 2004, 231 (3), 717-24; (b) van Dorsten F; Engelbrecht M; vsan der Graaf M; de la Rosette J; Barentsz J; A, H., Differentiation of prostatitus from prostate carcinoma using 1H MR spectroscopic imaging and dynamic contrast-enhanced MRI. Proc. Intl. Soc. Mag. Reson. Med. 2001, 9.

42. Hom, J. J.; Coakley, F. V.; Simko, J. P.; Lu, Y.; Qayyum, A.; Westphalen, A. C.; Schmitt, L. D.; Carroll, P. R.; Kurhanewicz, J., High-grade prostatic intraepithelial neoplasia in patients with prostate cancer: MR and MR spectroscopic imaging features--initial experience. Radiology 2007, 242 (2), 483-9.

43. (a) Kurhanewicz, J.; Vigneron, D. B.; Hricak, H.; Parivar, F.; Nelson, S. J.; Shinohara, K.; Carroll, P. R., Prostate cancer: metabolic response to cryosurgery as detected with 3D H-1 MR spectroscopic imaging. Radiology 1996, 200 (2), 489-96; (b) Parivar, F.; Hricak, H.; Shinohara, K.; Kurhanewicz, J.; Vigneron, D. B.; Nelson, S. J.; Carroll, P. R., Detection of locally recurrent prostate cancer after cryosurgery: evaluation by transrectal ultrasound, magnetic resonance imaging, and three-dimensional proton magnetic resonance spectroscopy. Urology 1996, 48 (4), 594-9; (c) Coakley, F. V.; Teh, H. S.; Qayyum, A.; Swanson, M. G.; Lu, Y.; Roach, M., 3rd; Pickett, B.; Shinohara, K.; Vigneron, D. B.; Kurhanewicz, J., Endorectal MR imaging and MR spectroscopic imaging for locally recurrent prostate cancer after external beam radiation therapy: preliminary experience. Radiology 2004, 233 (2), 441-8; (d) Pickett, B.; Kurhanewicz, J.; Coakley, F.; Shinohara, K.; Fein, B.; Roach, M., 3rd, Use of MRI and spectroscopy in evaluation of external beam radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys 2004, 60 (4), 1047-55; (e) Pucar, D.; Shukla-Dave, A.; Hricak, H.; Moskowitz, C. S.; Kuroiwa, K.; Olgac, S.; Ebora, L. E.; Scardino, P. T.; Koutcher, J. A.; Zakian, K. L., Prostate cancer: correlation of MR imaging and MR spectroscopy with pathologic findings after radiation therapy-initial experience. Radiology 2005, 236 (2), 545-53; (f) Zaider, M.; Zelefsky, M. J.; Lee, E. K.; Zakian, K. L.; Amols, H. I.; Dyke, J.; Cohen, G.; Hu, Y.; Endi, A. K.; Chui, C.; Koutcher, J. A., Treatment planning for prostate implants using magnetic-resonance spectroscopy imaging. Int J Radiat Oncol Biol Phys 2000, 47 (4), 1085-96.

44. Shukla-Dave, A.; Hricak, H.; Scardino, P. T., Imaging low-risk prostate cancer. Curr Opin Urol 2008, 18 (1), 78-86.

45. (a) Zakian, K. L.; Sircar, K.; Hricak, H.; Chen, H. N.; Shukla-Dave, A.; Eberhardt, S.; Muruganandham, M.; Ebora, L.; Kattan, M. W.; Reuter, V. E.; Scardino, P. T.; Koutcher, J. A., Correlation of proton MR spectroscopic imaging with gleason score based on step-section pathologic analysis after radical prostatectomy. Radiology 2005, 234 (3), 804-14; (b) Kurhanewicz, J.; Swanson, M. G.; Nelson, S. J.; Vigneron, D. B., Combined magnetic resonance imaging and spectroscopic imaging approach to molecular imaging of prostate cancer. J Magn Reson Imaging 2002, 16 (4), 451-63; (c) Selnaes, K. M.; Gribbestad, I. S.; Bertilsson, H.; Wright, A.; Angelsen, A.; Heerschap, A.; Tessem, M. B., Spatially matched in vivo and ex vivo MR metabolic profiles of prostate cancer -- investigation of a correlation with Gleason score. NMR Biomed 2013, 26 (5), 600-6.

46. (a) Zakian, K. L.; Shukla-Dave, A.; Ackerstaff, E.; Hricak, H.; Koutcher, J. A., 1H magnetic resonance spectroscopy of prostate cancer: biomarkers for tumor characterization. Cancer Biomark 2008, 4 (4-5), 263-76; (b) Chang, S. T.; Westphalen, A. C.; Jha, P.; Jung, A. J.; Carroll, P. R.; Kurhanewicz, J.; Coakley, F. V., Endorectal MRI and MR spectroscopic imaging of prostate cancer: developing selection criteria for MR-guided focal therapy. J Magn Reson Imaging 2014, 39 (3), 519-25.

47. Cabrera, A. R.; Coakley, F. V.; Westphalen, A. C.; Lu, Y.; Zhao, S.; Shinohara, K.; Carroll, P. R.; Kurhanewicz, J., Prostate cancer: is inapparent tumor at endorectal MR and MR spectroscopic imaging a favorable prognostic finding in patients who select active surveillance? Radiology 2008, 247 (2), 444-50.

48. Zakian, K. L.; Hatfield, W.; Aras, O.; Cao, K.; Yakar, D.; Goldman, D. A.; Moskowitz, C. S.; Shukla-Dave, A.; Tehrani, Y. M.; Fine, S.; Eastham, J.; Hricak, H., Prostate MRSI Predicts Outcome in Radical Prostatectomy Patients. Magn Reson Imaging 2016.

49. (a) Chen, A. P.; Cunningham, C. H.; Kurhanewicz, J.; Xu, D.; Hurd, R. E.; Pauly, J. M.; Carvajal, L.; Karpodinis, K.; Vigneron, D. B., High-resolution 3D MR spectroscopic imaging of the prostate at 3 T with the MLEV-PRESS sequence. Magn Reson Imaging 2006, 24 (7), 825-32; (b) Futterer, J. J.; Scheenen, T. W.; Huisman, H. J.; Klomp, D. W.; van Dorsten, F. A.; Hulsbergen-van de Kaa, C. A.; Witjes, J. A.; Heerschap, A.; Barentsz, J. O., Initial experience of 3 tesla endorectal coil magnetic resonance imaging and 1H-spectroscopic imaging of the prostate. Invest Radiol 2004, 39 (11), 671-80; (c) Scheenen, T. W.; Gambarota, G.; Weiland, E.; Klomp, D. W.; Futterer, J. J.; Barentsz, J. O.; Heerschap, A., Optimal timing for in vivo 1H-MR spectroscopic imaging of the human prostate at 3T. Magn Reson Med 2005, 53 (6), 1268-74; (d) Scheenen, T. W.; Heijmink, S. W.; Roell, S. A.; Hulsbergen-Van de Kaa, C. A.; Knipscheer, B. C.; Witjes, J. A.; Barentsz, J. O.; Heerschap, A., Three-dimensional proton MR spectroscopy of human prostate at 3 T without endorectal coil: feasibility. Radiology 2007, 245 (2), 507-16; (e) Lichy, M. P.; Pintaske, J.; Kottke, R.; Machann, J.; Anastasiadis, A.; Roell, S.; Hennenlotter, J.; Diergarten, T.; Schick, F.; Stenzl, A.; Claussen, C. D.; Schlemmer, H. P., 3D proton MR spectroscopic imaging of prostate cancer using a standard spine coil at 1.5 T in clinical routine: a feasibility study. Eur Radiol 2005, 15 (4), 653-60.

50. (a) Wright, A. J.; Heerschap, A., Simple baseline correction for 1H MRSI data of the prostate. Magn Reson Med 2012, 68 (6), 1724-30; (b) Wright, A. J.; Kobus, T.; Selnaes, K. M.; Gribbestad, I. S.; Weiland, E.; Scheenen, T. W.; Heerschap, A., Quality control of prostate 1 H MRSI data. NMR Biomed 2013, 26 (2), 193-203; (c) Kelm, B. M.; Menze, B. H.; Zechmann, C. M.; Baudendistel, K. T.; Hamprecht, F. A., Automated estimation of tumor probability in prostate magnetic resonance spectroscopic imaging: pattern recognition vs quantification. Magn Reson Med 2007, 57 (1), 150-9.

51. Steinseifer, I. K.; van Asten, J. J.; Weiland, E.; Scheenen, T. W.; Maas, M. C.; Heerschap, A., Improved volume selective (1) H MR spectroscopic imaging of the prostate with gradient offset independent adiabaticity pulses at 3 tesla. Magn Reson Med 2015, 74 (4), 915-24.

52. Steinseifer, I. K.; Philips, B. W.; Gagoski, B.; Weiland, E.; Scheenen, T. W.; Heerschap, A., Flexible proton 3D MR spectroscopic imaging of the prostate with low-power adiabatic pulses for volume selection and spiral readout. Magn Reson Med 2016.

53. Tayari N, Steinseifer I, Fu C, Weiland E, van Asten J, Scheenen T, Maas M1 and Heerschap A In Robust 3D 1H MRSI of the prostate without endorectal coil at 3T, Proceedings ISMRM Toronto 2015.

54. Lagemaat, M. W.; Breukels, V.; Vos, E. K.; Kerr, A. B.; van Uden, M. J.; Orzada, S.; Bitz, A. K.; Maas, M. C.; Scheenen, T. W., (1) H MR spectroscopic imaging of the prostate at 7T using spectral-spatial pulses. Magn Reson Med 2016, 75 (3), 933-45.

55. Magnetic Resonance spectroscopy for evaluation of suspected brain tumor. www.bcbs.com.tec/vol18/18_01.html : TEC Bull 2003, 20 (1), 23 - 6.

56. (a) Ross, B. D., Evidence-based Medicine: what's wrong with spectroscopy papers. Proc. Intl. Soc. Mag. Reson. Med. 13 2005, 126; (b) Lin, A.; Ross, B. D.; Harris, K.; Wong, W., Efficacy of proton magnetic resonance spectroscopy in neurological diagnosis and neurotherapeutic decision making. NeuroRx 2005, 2 (2), 197-214.

57. Hollingworth, W.; Medina, L. S.; Lenkinski, R. E.; Shibata, D. K.; Bernal, B.; Zurakowski, D.; Comstock, B.; Jarvik, J. G., A systematic literature review of magnetic resonance spectroscopy for the characterization of brain tumors. AJNR Am J Neuroradiol 2006, 27 (7), 1404-11.

58. Golman, K.; Zandt, R. I.; Lerche, M.; Pehrson, R.; Ardenkjaer-Larsen, J. H., Metabolic imaging by hyperpolarized 13C magnetic resonance imaging for in vivo tumor diagnosis. Cancer Res 2006, 66 (22), 10855-60.

59. Nelson, S. J.; Kurhanewicz, J.; Vigneron, D. B.; Larson, P. E.; Harzstark, A. L.; Ferrone, M.; van Criekinge, M.; Chang, J. W.; Bok, R.; Park, I.; Reed, G.; Carvajal, L.; Small, E. J.; Munster, P.; Weinberg, V. K.; Ardenkjaer-Larsen, J. H.; Chen, A. P.; Hurd, R. E.; Odegardstuen, L. I.; Robb, F. J.; Tropp, J.; Murray, J. A., Metabolic imaging of patients with prostate cancer using hyperpolarized [1-(1)(3)C]pyruvate. Sci Transl Med 2013, 5 (198), 198ra108.

Figures

Choline over citrate map showing a high ratio (yellow) at the location of the tumor (red on histopathology). A spectrum of a voxel in the tumor lesion is also shown.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)