diff --git a/.gitignore b/.gitignore index a1a500b..e104617 100644 --- a/.gitignore +++ b/.gitignore @@ -23,4 +23,6 @@ md5sums.txt .cache nosetests.xml coverage.xml -*.pyc \ No newline at end of file +*.pyc +phantoms/MR_XCAT_qMRI/*.json +phantoms/MR_XCAT_qMRI/*.txt \ No newline at end of file diff --git a/phantoms/MR_XCAT_qMRI/sim_ivim_sig.py b/phantoms/MR_XCAT_qMRI/sim_ivim_sig.py index e3c5635..df334c3 100644 --- a/phantoms/MR_XCAT_qMRI/sim_ivim_sig.py +++ b/phantoms/MR_XCAT_qMRI/sim_ivim_sig.py @@ -11,7 +11,7 @@ # code adapted from MAtlab code by Eric Schrauben: https://github.com/schrau24/XCAT-ERIC # This code generates a 4D IVIM phantom as nifti file -def phantom(bvalue, noise, TR=3000, TE=40, motion=False, rician=False, interleaved=False): +def phantom(bvalue, noise, TR=3000, TE=40, motion=False, rician=False, interleaved=False,T1T2=True): np.random.seed(42) if motion: states = range(1,21) @@ -23,10 +23,10 @@ def phantom(bvalue, noise, TR=3000, TE=40, motion=False, rician=False, interleav # Access the variables in the loaded .mat file XCAT = mat_data['IMG'] - XCAT = XCAT[0:-1:2,0:-1:2,10:160:4] + XCAT = XCAT[-1:0:-2,-1:0:-2,10:160:4] D, f, Ds = contrast_curve_calc() - S, Dim, fim, Dpim, legend = XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds) + S, Dim, fim, Dpim, legend = XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds,T1T2=T1T2) if state == 1: Dim_out = Dim fim_out = fim @@ -95,15 +95,19 @@ def contrast_curve_calc(): D[8] = 3e-3 # 8 Blood ra D[10] = 1.37e-3 # 8 Muscle: 10.3389/fresc.2022.910068 D[13] = 1.5e-3 # 13 liver: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901 + D[14] = 3.0e-3 # 13 Galbladder D[17] = 1.67e-3 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421 D[18] = 1.67e-3 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421 D[20] = 1.5e-3 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586 + D[21] = 3.0e-3 # 21 stomach content D[22] = 1.3e-3 # 22 Pancreas (from literature) D[23] = 2.12e-3 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519 D[24] = 2.09e-3 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519 D[25] = 2.12e-3 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519 D[26] = 2.09e-3 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519 D[30] = 1.3e-3 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495 + D[34] = 4.1e-4 # 34 spinal cord :doi: 10.3389/fonc.2022.961473 + D[35] = 0.43e-3 # 35 Bone marrow : https://pubmed.ncbi.nlm.nih.gov/30194746/ D[36] = 3e-3 # 36 artery D[37] = 3e-3 # 37 vein D[40] = 1.31e-3 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926 @@ -124,15 +128,19 @@ def contrast_curve_calc(): f[8] = 1.00 # 8 Blood ra f[10] = 0.10 # 8 Muscle: 10.3389/fresc.2022.910068 f[13] = 0.11 # 13 liver : Delattre et al. doi: 10.1097/RLI.0b013e31826ef901 + f[14] = 0 # 13 Gal f[17] = 0.32 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421 f[18] = 0.32 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421 f[20] = 0.3 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586 + f[21] = 0.0 # 20 stomach content f[22] = 0.15 # 22 Pancreas (from literature) f[23] = 0.097 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519 f[24] = 0.158 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519 f[25] = 0.097 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519 f[26] = 0.158 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519 f[30] = 0.2 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495 + f[34] = 0.178 # 34 spinal cord :doi: 10.3389/fonc.2022.961473 + f[35] = 0.145 # 35 Bone marrow : https://pubmed.ncbi.nlm.nih.gov/30194746/ f[36] = 1.0 # 36 artery f[37] = 1.0 # 37 vein f[40] = 0.69 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926 @@ -153,15 +161,19 @@ def contrast_curve_calc(): Ds[8] = 0.1 # 8 Blood ra Ds[10] = 0.0263 # 8 Muscle: 10.3389/fresc.2022.910068 Ds[13] = 0.1 # 13 liver: Delattre et al. doi: 10.1097/RLI.0b013e31826ef901 + Ds[14] = 0.1 # 14 Gal Ds[17] = 0.03 # 17 esophagus : Huang et al. doi: 10.1259/bjr.20170421 Ds[18] = 0.03 # 18 esophagus cont : Huang et al. doi: 10.1259/bjr.20170421 Ds[20] = 0.012 # 20 stomach wall: Li et al. doi: 10.3389/fonc.2022.821586 + Ds[21] = 0.0 # 20 stomach content Ds[22] = 0.01 # 22 Pancreas (from literature) Ds[23] = 0.02 # 23 right kydney cortex : van Baalen et al. Doi: jmri.25519 Ds[24] = 0.019 # 23 right kydney medulla : van Baalen et al. Doi: jmri.25519 Ds[25] = 0.02 # 23 left kydney cortex : van Baalen et al. Doi: jmri.25519 Ds[26] = 0.019 # 23 left kydney medulla : van Baalen et al. Doi: jmri.25519 Ds[30] = 0.03 # 30 spleen : Taimouri et al. Doi: 10.1118/1.4915495 + Ds[34] = 0.0289 # 34 spinal cord :doi: 10.3389/fonc.2022.961473 + Ds[35] = 0.05 # 35 Bone marrow : Ds[36] = 0.1 # 36 artery Ds[37] = 0.1 # 37 vein Ds[40] = 0.029 # 40 asc lower intestine : Hai-Jing et al. doi: 10.1097/RCT.0000000000000926 @@ -175,7 +187,7 @@ def contrast_curve_calc(): return D, f, Ds -def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds, b0=3, ivim_cont = True): +def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds, b0=3, ivim_cont = True, T1T2=True): ########################################################################################### # This script converts XCAT tissue values to MR contrast based on the SSFP signal equation. # Christopher W. Roy 2018-12-04 # fetal.xcmr@gmail.com @@ -285,7 +297,7 @@ def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds, b0=3, ivim_cont = True): Tissue[19] = [1045.5, 37.3, 1201, 44] Tissue[20] = [981.5, 36, 1232.9, 37.20] #Tissue[20] = [981.5, 36, 1232.9, 37.20] - Tissue[21] = [0, 0, 0, 0] + Tissue[21] = [2500, 1250, 4000, 2000] Tissue[22] = [584, 46, 725, 43] Tissue[23] = [828, 71, 1168, 66] Tissue[24] = [1412, 85, 1545, 81] @@ -351,7 +363,6 @@ def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds, b0=3, ivim_cont = True): else: T1 = Tissue[iTissue, 2] T2 = Tissue[iTissue, 3] - if ivim_cont and not np.isnan([D[iTissue], f[iTissue], Ds[iTissue]]).any(): # note we are assuming blood fraction has the same T1 as tissue fraction here for simplicity. Can be changed in future. Dtemp=D[iTissue] @@ -362,8 +373,11 @@ def XCAT_to_MR_DCE(XCAT, TR, TE, bvalue, D, f, Ds, b0=3, ivim_cont = True): ftemp=np.random.rand(1)*0.5 Dstemp=5e-3+np.random.rand(1)*1e-1 S0 = ivim(bvalue,Dtemp,ftemp,Dstemp) - if T1 > 0 or T2 > 0: - MR = MR + np.tile(np.expand_dims(XCAT == iTissue,3),len(S0)) * S0 * (1 - 2 * np.exp(-(TR - TE / 2) / T1) + np.exp(-TR / T1)) * np.exp(-TE / T2) + if T1T2: + if T1 > 0 or T2 > 0: + MR = MR + np.tile(np.expand_dims(XCAT == iTissue,3),len(S0)) * S0 * (1 - 2 * np.exp(-(TR - TE / 2) / T1) + np.exp(-TR / T1)) * np.exp(-TE / T2) + else: + MR = MR + np.tile(np.expand_dims(XCAT == iTissue,3),len(S0)) * S0 Dim = Dim + (XCAT == iTissue) * Dtemp fim = fim + (XCAT == iTissue) * ftemp Dpim = Dpim + (XCAT == iTissue) * Dstemp @@ -403,6 +417,7 @@ def parse_bvalues_file(file_path): parser.add_argument("-n", "--noise", type=float, default=0.0005, help="Noise") parser.add_argument("-m", "--motion", action="store_true", help="Motion flag") parser.add_argument("-i", "--interleaved", action="store_true", help="Interleaved flag") + parser.add_argument("-u", "--T1T2", action="store_true", help="weight signal with T1T2") # note, set this to zero when generating test data for unit testing! args = parser.parse_args() if args.bvalues_file and args.bvalue: @@ -420,14 +435,15 @@ def parse_bvalues_file(file_path): noise = args.noise motion = args.motion interleaved = args.interleaved + T1T2 = args.T1T2 download_data() for key, bvalue in bvalues.items(): bvalue = np.array(bvalue) - sig, XCAT, Dim, fim, Dpim, legend = phantom(bvalue, noise, motion=motion, interleaved=interleaved) + sig, XCAT, Dim, fim, Dpim, legend = phantom(bvalue, noise, motion=motion, interleaved=interleaved,T1T2=T1T2) # sig = np.flip(sig,axis=0) # sig = np.flip(sig,axis=1) res=np.eye(4) - res[2]=2 + res[2,2]=2 voxel_selector_fraction = 0.5 D, f, Ds = contrast_curve_calc() diff --git a/tests/IVIMmodels/unit_tests/algorithms.json b/tests/IVIMmodels/unit_tests/algorithms.json index a39df9f..09e2062 100644 --- a/tests/IVIMmodels/unit_tests/algorithms.json +++ b/tests/IVIMmodels/unit_tests/algorithms.json @@ -100,7 +100,7 @@ }, "ETP_SRI_LinearFitting": { "xfail_names": { - "Vein": true + "Blood RV": true }, "options": { "thresholds": [500] diff --git a/tests/IVIMmodels/unit_tests/generic.json b/tests/IVIMmodels/unit_tests/generic.json index 47fcb3e..223c417 100644 --- a/tests/IVIMmodels/unit_tests/generic.json +++ b/tests/IVIMmodels/unit_tests/generic.json @@ -1,28 +1,28 @@ { "Myocardium LV": { "noise": 0.0005, - "D": 0.0024, + "D": 0.0023999999999999994, "f": 0.14999999999999997, "Dp": 0.08, "data": [ - 0.37020853964957995, - 0.36495023994839576, - 0.36040161395777776, - 0.3482649413062642, - 0.3311252519245551, - 0.31128471484956144, - 0.29753771205592827, - 0.27895404495875237, - 0.2627838930390298, - 0.24600672258762385, - 0.21960592872343127, - 0.17325350570406992, - 0.13578433027277387, - 0.12026630133722724, - 0.08269998770527108, - 0.0583466003590057, - 0.04025758982493964, - 0.028838742483508213 + 1.0001231751746, + 0.9863831981799663, + 0.9736795410767166, + 0.9399687907072308, + 0.8967250611562163, + 0.8404681740089247, + 0.8050090669541026, + 0.7567153344368321, + 0.710120431006179, + 0.6680098450848827, + 0.5928443202089699, + 0.46635060352675767, + 0.3670703137207122, + 0.3255000885344592, + 0.2268720656823917, + 0.1591125315010815, + 0.1114196849818942, + 0.07638861567746785 ] }, "myocardium RV": { @@ -31,50 +31,50 @@ "f": 0.14999999999999997, "Dp": 0.07999999999999999, "data": [ - 0.36977273246921033, - 0.36537365912605413, - 0.3595159042305869, - 0.34752698949768374, - 0.33173079647378234, - 0.3110608405629893, - 0.29805095418985367, - 0.27952927870925937, - 0.2623759963395676, - 0.2465802608039568, - 0.2205672287951045, - 0.17374944003807846, - 0.13502827555889882, - 0.12066297243669499, - 0.0836121527856757, - 0.05884198664879518, - 0.041489689098644505, - 0.028319859045401394 + 1.000180079067396, + 0.986990380915803, + 0.972900789084442, + 0.939736386666685, + 0.8971605905354147, + 0.8399637991928016, + 0.8044883933349772, + 0.7570561134394189, + 0.7105776950887231, + 0.6690513540994412, + 0.5928512978405378, + 0.46634107416135884, + 0.36603261857375496, + 0.32608853910956437, + 0.22768988835218287, + 0.1574577830722027, + 0.1105906460374374, + 0.07645407094838856 ] }, "myocardium ra": { "noise": 0.0005, - "D": 0.0015, - "f": 0.07000000000000002, - "Dp": 0.07000000000000002, + "D": 0.0014999999999999998, + "f": 0.07, + "Dp": 0.07, "data": [ - 0.3702523127001939, - 0.36755530947936665, - 0.3655092171111401, - 0.3587682397967246, - 0.35132224032603726, - 0.34087328451399485, - 0.3322050929514831, - 0.31948125995637927, - 0.30728799801895257, - 0.29586365531065567, - 0.2747381860006265, - 0.23736891610574076, - 0.20330679507798272, - 0.18899390991911522, - 0.15183897083022813, - 0.11947654863593715, - 0.0961082153184848, - 0.07618502040438892 + 1.0005083974943823, + 0.9933276431820893, + 0.9888917449810792, + 0.9723245049412418, + 0.9515956772980587, + 0.9193243894352221, + 0.8979671161354167, + 0.8650603283883946, + 0.8311011950802928, + 0.8001863046312927, + 0.7419099058924017, + 0.6400539643681297, + 0.5486982712430136, + 0.5100608251175099, + 0.4081030648058328, + 0.3242680675714864, + 0.25992409130127514, + 0.2068495366721375 ] }, "Blood RV": { @@ -83,24 +83,24 @@ "f": 1.0, "Dp": 0.1, "data": [ - 0.677301342624779, - 0.6130133898159349, - 0.5549567775209086, - 0.41065608429951045, - 0.2485679217237561, - 0.09152471508006849, - 0.03422606076706993, - 0.004391344655692806, - -0.0004185323666306052, - 0.00020745211260014227, - 9.950041472725093e-05, - 0.00033731346702161615, - -0.0004581887966317206, - 3.932025610280517e-05, - 0.0007977517497581966, - -9.674856879304352e-05, - -0.0007204219029777662, - -0.00010866334273215243 + 1.000603693073812, + 0.904606934487691, + 0.818063650190734, + 0.6061350763287074, + 0.36819110032265356, + 0.1355553150583588, + 0.05010509203622437, + 0.0072463289357294755, + 0.00018700717279331042, + -0.0003714271020912267, + 0.0005724345627937243, + 0.00027501426716422045, + 6.60209245021009e-05, + 0.0005070911042906482, + -0.0010201585810508762, + -0.00033545531560461706, + -4.138621780211343e-05, + -3.057038566732644e-05 ] }, "Blood RA": { @@ -109,24 +109,50 @@ "f": 1.0, "Dp": 0.09999999999999998, "data": [ - 0.6784642687872807, - 0.6135371749705592, - 0.5551735492299803, - 0.41181139461379773, - 0.24923005810598367, - 0.0905737104418847, - 0.03324974683655229, - 0.005422829610804135, - 0.0013661550851885077, - -0.00010124567605655351, - -0.0006196825747740377, - -0.000911449851230475, - 4.552359004633976e-06, - 0.00045058131568997706, - -0.0004429879455544712, - 0.00023402108507587148, - 0.0008873455071852579, - -0.0003493718684806242 + 0.9996412591901256, + 0.9045050114102029, + 0.8192855804119286, + 0.6061658664563122, + 0.36827988782030663, + 0.1351204360331529, + 0.04933291369547591, + 0.0071928296039783035, + 0.0002920234672598372, + -0.0005199682835767715, + 0.00016067058613793012, + -0.00035291450109271123, + 0.0008226789593082577, + -0.0004336298974722816, + -0.000362292924476757, + -0.0005147600841361719, + 0.00019344887052810314, + 5.962153441482305e-05 + ] + }, + "muscle": { + "noise": 0.0005, + "D": 0.00137, + "f": 0.10000000000000002, + "Dp": 0.026299999999999994, + "data": [ + 1.0000325379134485, + 0.9958416765116361, + 0.992251953029248, + 0.9806948706181458, + 0.9652148066834317, + 0.9350173634169726, + 0.9093031036764246, + 0.8665802456277173, + 0.826627506056065, + 0.7926653882373672, + 0.7345335304585094, + 0.6388759729401986, + 0.5565983335419028, + 0.5198757764936984, + 0.4246674084569358, + 0.34442874554680125, + 0.2806458618590227, + 0.22818434184198186 ] }, "Liver": { @@ -135,206 +161,258 @@ "f": 0.11000000000000007, "Dp": 0.1, "data": [ - 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"Dp": 0.028999999999999998, + "Dp": 0.028999999999999995, "data": [ - 0.3758930792047429, - 0.36871753415550856, - 0.3596646182674821, - 0.34064921555832417, - 0.30870691010518925, - 0.2582272686179002, - 0.221043089173151, - 0.1697427289837398, - 0.13576211924902365, - 0.11657239096913335, - 0.09974041437941726, - 0.084471000128348, - 0.07361374280718957, - 0.06839430869527796, - 0.060340500144174566, - 0.0461358155720791, - 0.04043988203898548, - 0.032151323740629956, - 0.027567944335165455, - 0.024265661849860197 + 1.0003100507264253, + 0.9808596670792784, + 0.9597545818740579, + 0.9055849726199571, + 0.8233362795667671, + 0.6887317310706255, + 0.5870581928986083, + 0.45236274282341904, + 0.3594990082273397, + 0.310030888592008, + 0.26236401163996526, + 0.22400543543401877, + 0.1958158891551436, + 0.18431387247922337, + 0.16132776146031788, + 0.1244084849029068, + 0.10891000051533029, + 0.08464107969156172, + 0.07411767571710161, + 0.06393035384587284 ] }, "trans lower intestine": { @@ -509,110 +649,110 @@ "f": 0.6899999999999997, "Dp": 0.029000000000000012, "data": [ - 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"Dp": 0.009999999999999998, + "Dp": 0.01, "data": [ - 0.3103093353499032, - 0.3086555979462937, - 0.30868551309587694, - 0.3054401417241601, - 0.3000029954428556, - 0.28945134888384705, - 0.2802004082880951, - 0.26178803339645007, - 0.2407823261780593, - 0.2215963893368975, - 0.1882027440790469, - 0.1374763807694227, - 0.10141005432707072, - 0.08739455730843261, - 0.06381550663211193, - 0.035407574842628166, - 0.026744554367110734, - 0.014898384338215711, - 0.010535994594789228, - 0.008640506769047538 + 1.0003177369531795, + 0.9967700492818756, + 0.9932450399240392, + 0.9821050370779619, + 0.9659430243319476, + 0.9328798251185195, + 0.9017573461125011, + 0.8426436705806011, + 0.775228838690929, + 0.7148834135872844, + 0.6087557713962932, + 0.44550149716711357, + 0.32731583448632096, + 0.2813752896424043, + 0.20856329358816902, + 0.11439276497405955, + 0.08403520759953775, + 0.04561583404905009, + 0.03481048741490723, + 0.02561040144000529 ] }, "config": { diff --git a/tests/IVIMmodels/unit_tests/test_ivim_fit.py b/tests/IVIMmodels/unit_tests/test_ivim_fit.py index 41a08af..f4ecc2b 100644 --- a/tests/IVIMmodels/unit_tests/test_ivim_fit.py +++ b/tests/IVIMmodels/unit_tests/test_ivim_fit.py @@ -169,6 +169,8 @@ def test_ivim_fit_saved(name, bvals, data, algorithm, xfail, kwargs, tolerances, tolerances = tolerances_helper(tolerances, ratio, data["noise"]) [f_fit, Dp_fit, D_fit] = fit.osipi_fit(signal, bvals) npt.assert_allclose(data['f'], f_fit, rtol=tolerances["rtol"]["f"], atol=tolerances["atol"]["f"]) - npt.assert_allclose(data['D'], D_fit, rtol=tolerances["rtol"]["D"], atol=tolerances["atol"]["D"]) - npt.assert_allclose(data['Dp'], Dp_fit, rtol=tolerances["rtol"]["Dp"], atol=tolerances["atol"]["Dp"]) + if data['f']<0.80: # we need some signal for D to be detected + npt.assert_allclose(data['D'], D_fit, rtol=tolerances["rtol"]["D"], atol=tolerances["atol"]["D"]) + if data['f']>0.03: #we need some f for D* to be interpretable + npt.assert_allclose(data['Dp'], Dp_fit, rtol=tolerances["rtol"]["Dp"], atol=tolerances["atol"]["Dp"]) diff --git a/utilities/data_simulation/Download_data.py b/utilities/data_simulation/Download_data.py index d03d8eb..c28886b 100644 --- a/utilities/data_simulation/Download_data.py +++ b/utilities/data_simulation/Download_data.py @@ -2,7 +2,7 @@ import os import subprocess import time - +import zenodo_get def unzip_file(zip_file_path, extracted_folder_path): # Open the zip file